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An isolation improvement for a closely spaced quad port multiple-input multiple-output (MIMO) antenna for WLAN applications is presented. The proposed antenna consists of four L-shaped monopole microstrip patch antennas with a rectangular slot at the center and truncated edges at the corner of each patch element. Interelement isolation is improved by more than 12 dB by considering the patch size, ground size, and distance between the patch elements. The designed antenna uses λ/4 distance between the patch elements. The slot in each patch improves the bandwidth, and truncated edges at the corner of the patch improve the impedance matching of the antenna. The proposed MIMO antenna is designed with the overall dimensions of 45.5 mm × 45.5 mm × 1.96 mm. The MIMO parameters for the proposed technique, which include envelope correlation coefficient (E.C.C.) are less than 0.5, and the diversity gain (D.G.) is 10 dB, suggesting this antenna’s suitability for MIMO operations. The average peak realized gain (A.P.R.G.) and radiation efficiency (RE) is 2.8 dBi and greater than 80% for the prescribed band of interest. The antenna element is fabricated and tested using microwave analyzer N9917A. The measured result seems to be in good contact with the simulation. The proposed MIMO antenna is well-suitable for WLAN applications.

Web citation analysis is primarily used to examine the impact of an author, an article or a publication by counting the amount of intervals that has been cited by other authors. The significant goal of web citation analysis is helping the researchers to find their related papers for their further analysis. The references of the paper must be cited by the author, which is used to recognize the link among the previous relevant research. The citation provides the valuable information that directs the researchers to gain knowledge about the current trends and future developments and obtain new ideas in their respective fields. The citation’s information are incorporate in the database called web citation database such as Google Scholar, Web of Science, Scopus and so on. From the web citation database, extracting the user required information is very complex task. Most of the open source tools are available online but manual process is needed to select the user-required information from the web page. For instance if the user need author name and publisher from the web citation database, they required to choose the exact information tags manually in existing tools which consumes more time. To overcome this difficulty we proposed an algorithm Firefly Optimization Algorithm based Web Scraping for web content extraction from web citation database. The primary purpose of this research is to extract author information extraction process extracts citation information published by an author, journal name, publisher, year and citation using web citation analysis. The user’s query input will be the keyword for example big data, artificial intelligence, etc. The web citation information from multiple web pages using Web crawling and web scraping techniques are applied for web citation information based on the user query and Particle Swarm Optimization, Hidden Markov Model are applied for finding the best solution from all the feasible solutions. Experiments illustrate the proposed FOAWS algorithm outperforms well comparing to other two algorithms.

The main objective of Internet of Things (IoT) is connecting with different objects via Internet without human intervention. Wireless Sensor Networks (WSNs) which involves ubiquitous computing through which small sensors are connected to the Internet and are used for collecting data. Significant amount of information flowing in the internet is made up of sensory data. To resolve the storage issues of the huge data generated by IoT, the Hadoop Distributed File System are used that streams data to user applications as required. It is difficult to accomplish analysis of vast amount of data (big data) with existing data processing methods. To avoid redundant and irrelevant data, the data needs to be classified. This work presents the use of Support Vector Machine, and Adaboost classifiers, and modifying Adaboost classifier with Genetic Algorithm (GA), Stochastic Diffusion Search (SDS), and Particle Swarm Optimization (PSO). To avoid redundant classifiers, an ensemble algorithm is proposed in this work, PSO with Adaboost classifier and SDS-GA with Adaboost classifier, that can reinitialize attributes, thus avoiding reaching local optimum, and optimizing the coefficients of Adaboost weak classifiers. The proposed algorithms effectively classify the data gathered from WSN and IoT applications. The outcomes of the experiment showed that the proposed SDS-GA algorithm is efficient over other algorithms with respect to accuracy, precision, recall, f measure and false discovery rate.

Introduction: Adequate amount and quality of sleep is the basic need for any human. Children and adolescents requires at least eight to nine hours of sleep per night. Insomnia results in both mental and physical health consequences, diminished productivity, accident proneness, increased medical utilisation and elevated risk of psychiatric disorders. Sleep hygiene intervention is a behavioral intervention to improve sleep quality. Aim: To determine the effectiveness of sleep hygiene intervention programme in reduction of insomnia and obesity among undergraduate medical students. Materials and Methods: This interventional study was conducted at Tagore Medical College and Hospital, Chennai, India, January 2019 to May 2019, among 602 undergraduate medical students. Study was conducted in two phases, in phase 1, participants were surveyed for Insomnia Severity Index (ISI) scoring and BMI. Those with ISI scores between 8-21 and BMI ≥30 kg/m2 were eligible for phase 2. In phase 2 using, stratified random sampling total 100 subjects, 50 each for control and intervention group were selected. Periodical sleep hygiene intervention was given for intervention group, insisting to follow 10 simple non pharmacological behavioral measures. Follow-up assessment of weight (kg) and ISI scoring was done. Paired t-test, repeated measures Analysis of Variance (ANOVA) and Bonferine Posthoc test was done to identify the statistical significance using Statistical Package for Social Sciences (SPSS) version 20.0. Results: Among the 100 participants in phase II, 29 were female in both control and intervention group. The prevalence of Subthreshold insomnia 289 (48%) was higher. Over 4 month period comparison, intervention group had better reduction in weight {(62.48±10.62 kg to 51.48±6.88 kg) vs (61.58±10.35 kg to 62.48±10.66 kg)} and insomnia severity (13.70±3.62 to 10.34±2.43) vs (12.56±3.78 to 13.50±3.63) than control group. Conclusion: Simple non pharmacological intervention seems to have greater benefits in improving sleep hygiene and reducing obesity

Face swapping has been a thriving genre of work which primarily is associated with the replacement or substitution of one referral face on the face of the other person, be it by means of still images or in real time. This paper exhibits the research on Real-Time Face Swapping algorithm, using the reference image of the user and henceforth considering it as our input image instead of using a data set thereby working on a real time model by the use of the python open-source model of Computer Vision i.e., OpenCV. The input image facial features and attributes are extracted and are replaced to make the final resulting output through our model. The training of the model starts with learning the facial alignments and features to be able to recognize a face and extract its features before swapping it. Now, heading over to the process of image warping by the dissociation and partitioning of the user's face and its background. To increase the precision of the model, in the second part of parsing the face is performed to cancel out other features of the live image, hence the output is obtained. In this research, it was tried to bring accuracy and precision to the model by improving the condition of the image, editing out borders, and training it to a bunch of data for face recognition and correction. On analyzing it delivers the most subtle outputs alongside performing the tasks with the process including the analysis of our image and its output too. The model delivered is to improve the conditions of security, privacy, image capture, and entertainment purposes.

The Internet of Things (IoT) depicts the network that contains the objects or the “things” that have been embedded along with the network connectivity, the sensors, electronics or the software that enables the objects to collect and exchange data. Wireless sensor networks (WSNs) connect different sensors/things to the Internet by means of a gateway which interfaces the concept of the WSN to the Internet. They have a certain trait that collects all sensed data and duly forwards it to a gateway using a one-way protocol. Huge amount of either unstructured or semi-structured data collected by the WSN is transmitted to IoT for processing. To improve the efficacy of the storing and processing of data, it is required to classify the data. Genetic algorithm is used to find optimal solutions in IoT. Stochastic diffusion search is a heuristic algorithm which has a robust mathematical model and is distributed. This work proposed a Stochastic AdaBoost algorithm for efficient classification of data obtained from WSN and IoT network.

Big Data Technologies are well-accepted in the recent years in bio-medical and genome informatics. They are capable to process gigantic and heterogeneous genome information with good precision and recall. With the quick advancements in computation and storage technologies, the cost of acquiring and processing the genomic data has decreased significantly. The upcoming sequencing platforms will produce vast amount of data, which will imperatively require high-performance systems for on-demand analysis with time-bound efficiency. Recent bio-informatics tools are capable of utilizing the novel features of Hadoop in a more flexible way. In particular, big data technologies such as MapReduce and Hive are able to provide high-speed computational environment for the analysis of petabyte scale datasets. This has attracted the focus of bio-scientists to use the big data applications to automate the entire genome analysis. The proposed framework is designed over MapReduce and Java on extended Hadoop platform to achieve the parallelism of Big Data Analysis. It will assist the bioinformatics community by providing a comprehensive solution for Descriptive, Comparative, Exploratory, Inferential, Predictive and Causal Analysis on Genome data. The proposed framework is user-friendly, fully-customizable, scalable and fit for comprehensive real-time genome analysis from data acquisition till predictive sequence analysis.

The growth of cancer in India is growing hastily in recent years. Efficient monitoring and medication procedures are needed in high demand. Recent research states diagnose of cancer during its early break through will prevent mortality. The evolution of smart mobile devices paves its mutual focus in healthcare sectors. In this paper, an Intellectual model of disease diagnosis using the advantage of smart mobile devices has been proposed. This mobile based cancer diagnosis model uses a cloud environment for disease prediction and analysis. The Principal Component Analysis (PCA) technique is utilized to confiscate the superfluous features and choose the most appropriate features. Using the optimized features, cancer disease classification is accomplished using Support Vector Machines with sigmoid kernel function. SVM classifies the patients as normal and abnormal and the evaluated results are conveyed to the patients as well as the respective medical practitioners. The accuracy achieved through proposed model is satisfiable in comparison with other existing methods. Proposed Model incorporates with big data technologies to address the current issues of cancer system.

Recently machine learning have been a field of interest for many researchers. Machine learning holds applications in tremendous sectors like healthcare, e-commerce and so on. It plays a vital role in the field of healthcare by their significant contribution through its ability to read from the historic/ past data especially in the diagnosis/predicting of diseases. Many classification algorithms have been proposed for the prediction process. The problem with classifiers is their vast variety and difficulty in choosing an appropriate method for the particular problem. To overcome the issue of selecting the appropriate method a comprehensive study on various classifiers should be done. This paper focuses on the working of various classifiers for prediction of prostate Cancer in calculating the level of efficiency in prediction and this helps in selecting the best method.

Honey bee is one of the charming insect that utilizes a collective behavioral nature to achieve the powerful action. Protecting honey bees is one of the important jobs of every human in the world to preserve the ecological balance. Tracking and determining the several species of the bees over their life span electronically is a tedious work. Automated classification of species is important to preserve the various species of honey bees from danger. The diseases that affect the honey bees during their life span have to be detected autonomously and the spread of the diseases to other healthy honey bees has to be preserved. The proposed technique aims in classifying the several species of honey bees and identifying the diseases that are prone to honey bees. Convolution neural network with two dimensional layers are used as a classifier in the proposed model. Data augmentation using Synthetic Minority Over-sampling Technique (SMOTE) is utilized. More than 5000 images of honey bees with lot of features are used for learning purpose. The proposed methodology attained an accuracy of 86% for subspecies classification and 84% for bee health identification.

Web citation analysis is emerging as an important subject of research in web mining, information retrieval, library science, etc. Scientific publications form a significant part of the research. The quality of the publication is determined by the citation which is to a published or an unpublished source. Citation analysis is used to evaluate the corresponding significance or an impression of an author or publication which is assessed by several times that an author or publication has been cited by other related works. It is useful in ascertaining the impression of a research article. It is useful in learning more about an area of knowledge. The main objective of this chapter is to provide knowledge about web citation analysis. A brief overview of web citation index, citation styles, citation-based metrics, and research challenges are discussed.

In this research work, we propose a low-profile PIFA to operate under the dual resonant frequencies of f0,1/2 and f2,1/2 to show their improvement in gain and bandwidth. Initially, our study focuses on removing all the even-order modes by placing the shorting wall at one end of the patch. Then, a couple of shorting (connecting) pins are fixed at the nodal lines of the radiating element to examine the variant of their resonant frequencies in odd-order modes. This results in increasing the lower resonant frequency of f0,1/2 while maintaining the higher resonant mode of f2,1/2. In addition to that, the patch width is tuned effectively to reallocate the resonant modes more closely to one another. Finally, a small rectangular slot is created on the radiating element to minimize the corresponding inductance formed by the feeding probe and a couple of shorting pins. The results show that f0,1/2 and f2,1/2 are moved close to each other by placing a couple of perfect electric (shorting) pins under the patch element and adjusting the radiating patch width, respectively. It alters the lower and higher resonant frequency from 2.48 to 3.49 GHz and 4.81 to 4.49 GHz, and also it reduces the frequency ratio from 1.93% to 1.28%. The proposed patch antenna is modeled and simulated using high-frequency structure simulator (HFSS), and the results show that PIFA has a bandwidth of 76.8 MHz with a peak gain of 6.46 dB.

Diclofenac (DCF), a persistent pharmaceutical micropollutant which occurs in the ecosystems causing adverse effects on aquatic as well as terrestrial organisms. In this study, magnetic sawdust (MSD) was prepared using co-precipitation method for biosorptive removal of DCF from water. The MSD was characterized using various analytical techniques like microscopic and spectroscopic analysis. Magnetometer study confirms the ferromagnetic behavior of the biosorbent which is a key advantage in the separation of MSD after biosorption. The effect of experimental parameters was optimized in batch mode with evaluated maximum efficiency of 86.12 % at pH 6, biosorbent dosage 25 mg for 50 mg/L of DCF. Ecotoxicological assessment has been performed for the treated and untreated sample using plant seeds, microbes and zebra fish to check the adverse effects of DCF on these organisms. Evaluation of toxicity studies revealed that inhibition concentration of DCF for various seeds (60.91 mg/L to 43.11 mg/L), E. coli (48.82 μg/mL) and B. subtilis (31.55 μg/mL). The lethal concentration of DCF on the Danio rerio was found to be 156.99 mg/L. In contrast, significant increase in both the concentration measures of DCF after biosorption was observed making this biosorbent a potent alternative to other available treatment measures.

A compact single element planar antenna covering ultra-wideband (UWB) spectrum for Microwave C and X band applications is presented. The proposed UWB antenna is introduced to expand the bandwidth and to shrink the profile of an antenna. An additional slot with inverted F shaped pattern is etched on surface of the patch in order to completely eliminate the unwanted dip between the two bands. The whole structure of the antenna remains simple and compact. Good radiation characteristics and gain are obtained in the microwave bands at 5.4GHz and 8.75GHz with specific input return loss around −18dB and −15dB. The exact details of the planar antenna are presented and simulated. The obtained results are carried out by using Ansoft HFSS.

This chapter rolls on about the infrastructure capabilities of Internet of Things in managing the huge amount of big data and the future research context in developing a dynamic environment where IoT devices can connect and manage their resources on their own. New services are needed to progress the performance and service quality provided by the old services. Self-adaptation is essential for the IoT devices in a dynamic environment. These devices could publish/subscribe/notify/search/retrieve the data autonomously in a dynamic environment. In opportunistic networks, mobile collaboration could be utilized in case of direct communication failure and in cases of handling large data. These networks are mainly used during short-range communication when nodes are closer to each other. During communication, the routes are constructed dynamically according to the availability of the nodes learned based on machine learning algorithms. Data distribution is made on the basis of publishing/subscribing method. For continuous connectivity of devices in wired and wireless links, standard communication protocols are essential to manage the high traffic in the network as well. Added to it, new solutions are required for efficient storage, fetching, and searching of data in these complex environments.

Phishing is a critical issue that faces the digital security. The straightforwardness of the web and Internet uncovered open doors for offenders to transfer malevolent substance at the same time with the upgrade of online business trades, for example, phishing – the demonstration of taking individual data which ascends in number. Internet clients’ costs have been increased to billions of dollars for each year due to phishing. Phishers use parodied email, Uniform Resource Locator (URL) locations of phony sites, and phishing programming to take individual data and monetary record subtleties, for example, usernames and passwords. The boycott system is definitely not a sufficient method to remain safe from the cybercriminals. Hence, phishing site pointers must be considered for this reason, with the presence and utilization of machine learning calculations. The current techniques make utilization of all separated attributes in the phishing URL location, prompting high false positive rate.

Nature has made a huge impact on human’s diurnal lives in finding solutions for complex problems. Majority of the people are receiving benefits by the advanced technologies. Without the aid of computers, it is hard to imitate the problem solving techniques being implemented by nature. Bio-mimicry is one such study which enables the researchers in solving human problems by emulating and mimicking nature. This emerging technology fetches its sources from bio-inspired engineering at various levels. BIC, short for bio-inspired computing or biologically inspired computation, found to be a broad term, which comprises different fields like computer science, mathematics and biology. Mix and match of various biological and computational processes is the reliable means for researchers in this regard and this leads to the convergence of two fields: computer science and biology. In recent past, the bio-inspired optimization algorithms are highly convincing to bring out best expositions for intricate snags by joining hands with machine learning. Along with BIC, swarm intelligence (SI) is also equally gaining importance. Bio-inspiring algorithms like ant colony algorithm, bat algorithm, cuckoo search, fire-fly algorithms, and particle swarm optimization possess extensive applications in almost all fields of science and engineering. Swarm intelligence is the collective behavior of decentralized, self-organized systems, though they are natural or artificial and it is based on meta-heuristic approach which generally pulls out an approximate optimal solution independent of the problems in less time. Nowadays, the information technology advancements induce felons to commit copious cybercrimes using cyberspace. More sophisticated cyber-defense systems are to be devised essentially, since (1) cyber-infrastructures are extremely open and prone to infringements and other intimidations and (2) human intervention and physical devices are not adequate to monitor and fortify the cyber-infrastructures. The ability to detect a wide variety of threats and adaptability to make quick real-time decisions, flexibility and robustness are the major expected qualities for these systems. Abundant BIC approaches incorporated with artificial intelligence (AI) using IOT plays a vital role in cybercrime detection and prevention. Let us have a cattle graze of few techniques of AI associated with bio-inspired computing and its futuristic scope over human community to combat cybercrimes and this review would pave the alleyway for forthcoming studies to cherry-pick algorithms based on fitment.

COVID-19 cases have been rising rapidly in countries where the SARS-CoV-2 variant of concern (VOC), Omicron (B.1.1.529) has been reported. We conducted a study to describe the epidemiological and clinical characteristics and outcomes of COVID-19 patients with 'S' gene target failure (SGTF, suspected Omicron). Furthermore, their clinical outcomes with COVID-19 patients with non-SGTF (non-Omicron) were also compared.This study was conducted in Tamil Nadu, India, between December 14, 2021 and January 7, 2022 among patients who underwent reverse transcription-PCR testing for SARS-CoV-2 in four laboratories with facilities for S gene screening. Consecutively selected COVID-19 patients with SGTF were telephonically contacted, seven and 14 days respectively after their date of positive result to collect information on the socio-demographic characteristics, previous history of COVID-19, vaccination status and clinical course of illness along with treatment details. To compare their outcomes with non-SGTF patients, one randomly suspected non-Omicron case for every two suspected Omicron cases from the line-list were selected, matching for the date of sample collection and the testing laboratory.A total of 1175 SGTF COVID-19 patients were enrolled for this study. Almost 6 per cent (n=72) reported a history of previous infection. 141 (13.5%) suspected Omicron cases were non-vaccinated, while 148 (14.2%) and 703 (67.4%) had received valid one and two doses of COVID-19 vaccines, respectively. Predominant symptoms reported included fever (n=508, 43.2%), body pain (n=275, 23.4%), running nose (n=261, 22.2%) and cough (n=249, 21.2%). Five (0.4%) of the 1175 suspected Omicron cases required oxygen supplementation as compared to ten (1.6%) of the 634 suspected non-Omicron cases. No deaths were reported among omicron suspects, whereas there were four deaths among suspected non-Omicron cases.Majority of the suspected Omicron cases had a mild course of illness. The overall severity of these cases was less compared to the suspected non-Omicron cases.

Chitosan was fabricated via gelation method using CaBr2.xH2O/methanol solution and was studied as a potential adsorbent (MCh) in adsorbing anionic synthetic dyes like Bromophenol blue (BB), Direct blue 6 (DB) and Congo red (CR) from single (one dye species at a time) and multi (having two dyes; binary and all three dyes; tertiary) adsorptive systems. Physico-chemical modifications of MCh surface prior and post modification and dye adsorption were evaluated using scanning electron microscopy, Energy-dispersive X-ray spectroscopy, powder X-ray diffraction analysis, surface area analysis and Fourier-transformed infrared spectroscopy. Influential parameters influencing the adsorption process viz. initial pH of dye solution, MCh dosage, adsorption temperature and initial concentration of dye species were optimised. Adsorptive studies involving single adsorptive setups verified formation of sorbate's (dye species) monolayer over the sorbent's (MCh) surface via chemisorption; as established by Langmuir isotherm and pseudo-second order kinetics model analysis. Theoretical maximum adsorption capacities of MCh for BB, DB and CR was found to be 81.301 mg/g, 163.934 mg/g and 75.758 mg/g, respectively. Meanwhile, for all multi-adsorptive systems, competitive Langmuir isotherm model verified antagonistic behaviour of an individual dye over other dye adsorption over MCh surface in their respective adsorptive systems. Thermodynamics of the sorbate-sorbent interaction was exothermic, spontaneous, with elevated degree of disorderedness; concluding the interaction as thermodynamically favourable. Co-existing metal cations and anionic salts had minimal effect on MCh's adsorption efficiency. Phytotoxicity assay via germination of Vigna mungo seeds verified the efficacy of the adsorbent in eliminating the dye species from single and multi-adsorptive systems.

Sentiment analysis is the computational task of automatically determining what feelings a writer is expressed in text. Sentiment analysis is gaining much attention in recent years. It is often framed as a binary distinction, i.e. positive vs. negative, but it can also be a more fine-grained, like identifying the specific emotion an author is expressing like fear, joy or anger. Globally, business enterprises can leverage opinion polarity and sentiment, topic recognition to gain deeper understanding of the drivers and the overall scope. Subsequently, these insights can advance competitive intelligence and improve customer service, thereby creating a better brand image and providing a competitive edge. To extract the content from e-commerce website using web scraping technique. It will be looping through then number of pages or so of comments for each of the products. In this work, online product reviews are collected using web scraping technique. The collected online product reviews are analyzed using opinion or sentiment analysis using classification models such as KNN, SVM, Random Forest, CNN (Convolutional Neural Network) and proposed hybrid SVM-CNN. Experiments for the classification models are performed with promising outcomes.

A low profile rectangular micro-strip patch antenna (RMPA) with different contact feeding techniques has been proposed for 5 GHz Wireless Fidelity (Wi-Fi) band. The proposed work consists of a MPA printed on a Duroid wafer of 1.575mm thickness and a dielectric loss tangent of 0.0009. A comparative study of RMPA for different contact feeding techniques such as microstrip line, coaxial line and inset feed is analyzed and simulated using Ansoft HFSS tool. The comparison results show that inset feed and probe feed exhibits a better gain of 6.2 dB compared with edge feed with a gain of 5.7 dB and the directivity achieves maximum for inset feed with a value of 6.38.

Smart farming based on recent Internet of Things (IoT) technologies is the most advanced method to grow food cleanly while making it sustainable. This method applies modern information and communication technologies to agriculture, targeted not only to reduce waste but also to increase agricultural productivity to an optimum level. Agriculture is considered the backbone of the world’s economy. The end-product’s quantity and quality both play a vital role. Using the proposed IoT technology, the identification of diseases in plants with focus on the affected area is more accurate. It also reduces the number of incorrect conclusions that may be drawn, which can lead to incorrect actions taken for sustaining the plants in farms. The proposed technique will also be able to predict the damage level by pests on plants to take appropriate actions to improve productivity. Digital images taken from the plants will be processed and examined using pattern recognition and digital image processing techniques. Proposed image analysis techniques will segment these images to identify diseases and the affected level. This will help to prevent plant disease and increase the nutrient level of the plant, with the help of IoT. The automatic detection of plant disease will be beneficial in monitoring a large field of crops.

A circularly polarised (CP) patch antenna with dual feed is presented. The antenna consists of a radiating patch, substrate and ground plane. A pair of feed is placed at TM10 and TM01 mode with sam...

A novel biosorbent Caryota urens seeds (CUS) was investigated for the technical feasibility for the biosorption of hexavalent chromium from aqueous solution. The biosorbent was characterized by SEM, FT-IR spectral, and EDS analysis. Effects of biosorbent size, biosorbent dosage, Cr(VI) concentration, pH, and contact time on the biosorption of hexavalent chromium were investigated. Both the Langmuir and Freundlich isotherms were suitable for describing the biosorption of Cr(VI) onto CUS. Application of the Langmuir isotherm model to the biosorption system yielded a maximum biosorption capacity of 52.63 mg/g at an equilibrium pH value of 2 and 303 K. Kinetic data were best fitted with the pseudo-second-order kinetic model. The results indicate that the CUS can be effective biosorbent for the removal of Cr(VI) from aqueous solution.

Efficacy of Eucalyptus camaldulensis seeds in raw (ECS) and phosphoric acid activated form (PECS) in the remediation of Cr(VI) from simulated and electroplating waste has been checked in this study. Structural and physico-chemical characterization of the biosorbents have been elucidated using CHNS, Mercury intrusion porosimetry, FTIR and SEM-EDAX analyses. Batch parameters like dose of biosorbent, pH, contact time, speed of contact, temperature were optimized for a maximum removal of 91.44% for ECS and 98.27% for PECS. Monolayer adsorption capacity for ECS and PECs were found to be 35.3 ± 4.84 mg/g and 51.93 ± 8.11mg/g respectively, elucidated from the nonlinear isotherm analysis. Freundlich Model fitted well for both the biosorbents in removing Cr(VI) at optimum conditions. Interaction of Cr(VI) with ECS and PCES was explained more suitably by kinetics. Pseudo second order model fitting for ECS and PECS revealed that the process followed multilayered chemisorption. Thermodynamic studies confirmed the adsorption of Cr(VI) by ECS and PECS are energetically favorable, stable and exothermic. Continuous column operations for maximum removal of Cr(VI) were optimized for Column bed-height, Initial Cr(VI) concentration and Initial flow rate. Column performances were checked with Adams–Bohart, Thomas, Yoon–Nelson and Bed Depth Service Time (BDST) models. Efficacy of PECS in removing Cr(VI) from real effluent was checked using electroplating effluent containing 190 mg/L of Cr(VI) and resulted with 45.35% removal. Thorough experimental findings showed that PECS is a feasible biosorbent that can remediate Cr(VI) from aqueous solutions effectively.

Hexavalent chromium tolerant non-pathogenic fungi isolated from lab waste were used for the reduction of toxic Cr(VI) (hexavalent chromium) from simulated and tannery effluent. The fungal cells were identified as Trichoderma asperellum (T. asperellum) through genomic DNA sequencing. Surface morphology of the cells and the functional groups embedded on their cells were studied using Scanning Electron Microscopy (SEM) and Fourier Transform infrared spectroscopy (FT-IR). The minimum inhibitory Cr(VI) concentration that the cells can tolerate was determined to be 11.53 mM. The effect of batch parameters influencing Cr(VI) reduction was numerically optimized by three parameter Box–Behnken response surface model (RSM) to evaluate two responses, Cr(VI) removal percentage and residual Cr(VI) concentration. The optimized process parameters for a maximum Cr(VI) removal of 86.73% and residual Cr(VI) concentration of 0.2013 mM were found to be pH 3.29, temperature 301 K and an initial Cr(VI) concentration of 1.33 mM. Crude extracellular and intracellular enzymes have been isolated and their activities over Cr(VI) reduction were determined as 0.0582 mM min − 1 mL − 1 and 0.0135 mM min − 1 mL − 1 respectively. Michelis–Menten parameters (Vmax and Km) of the enzymatic Cr(VI) reduction were depicted as 0.0015 mM s − 1 and 13.11 mM respectively. Tannery effluent containing an initial Cr(VI) concentration of 1.023 mM was reduced to 0.446 mM with the cells at optimum conditions. The results showed that free T. asperellum cells can be utilized for detoxification of simulated and real Cr(VI) containing aqueous solutions.

Background: Global Adult Tobacco Survey-2 (GATS) reveals significant improvements in the decline of tobacco consumption in the past decades, however vulnerable population groups such as migrant construction workers tobacco consumption behaviors in Chennai remain understudied, thus the study was planned to estimate the socio-economic and environmental determinants for high tobacco consumption behaviors among interstate migrant construction workers in Chennai.Methods: A cross sectional survey was undertaken using a pre-tested structured questionnaire adapted from GATS survey questionnaire assessing the socio-demographic and environmental determinants for tobacco use among study population across 13 construction sites in Chennai during May-September 2019. Data was entered in MS-Excel and analysed using SPSS.Results: Among 345 migrant construction workers, mean age of the subjects was 28.42±8.7 years, 338 (98%) were current tobacco users by smoking, chewing, snuffing, of whom 198 (58.6%) used smokeless forms of tobacco like gutkha, khaini, chewing tobacco leaves, betel quid, tobacco snuff, 98 (29%) were smokers and 42 (12.4%) were dual users i.e. both cigarettes and beedis. Khaini (57%) is the dominant form of tobacco used among migrant construction workers. Mean age of initiation of tobacco use was 21.58 years and 21.17, and 22.73 and 19.5 years for smoking, SLT and dual users respectively. The migrant construction workers with no formal literacy(p=0.000), monthly income of more than 10000 (p=0.020) Indian rupees and migrants for less than one year duration (p=0.05)were more likely using the smokeless tobacco than any other socio demographic group.Conclusions: Tobacco prevalence especially smokeless tobacco use is alarmingly high among interstate migrant construction workers to Chennai.

Cloud computing provides various types of software and hardware services collectively working together at different computational environment to the end user through internet. Cloud computing is an emerging technology that provides variety of applications to the end user and cloud services can be accessed from anywhere in the world in secured way whenever required. Nowadays free online hosting websites produce a large amount of multimedia data. It may include images, audios, 2D videos, 3D videos. Some people will use videos and audios in the distributed environment against the copyrights from the original content creators. This will create a large amount of revenue loss for the original content creator. The protection of theses multimedia content is a challenging task. Cloud computing has some security issues like authentication, privacy and data security. Our proposed system will provide highly scalable storage infrastructure in cloud environment. It also protects the multimedia content by creating a depth signature and classifying them with duplicated contents using SVM classifier. This proposed system will avoid the security issues in multimedia content and it will increase the revenue for original content creator. The proposed system can work in both public and private cloud infrastructure.

Increased usage of internet led to the migration of large amount of data to the cloud environment which uses Hadoop and map reduce framework for managing various mining applications in distributed environment. Earlier research activity in distributed mining comprises of solving complex problems using distributed computational techniques and new algorithmic designs. But as the nature of the data and user requirement becomes more complex and demanding, the existing distributed algorithms fails in multiple aspects. In our work, a new distributed frequent pattern algorithm, named Hadoop-based parallel frequent pattern mining (HPFP) has been proposed to optimally utilise the clusters efficiently and mine repeated patterns from large databases very effectively. The empirical evaluation shows that HPFP algorithm improves the performance of mining operation by increasing the level of parallelism and execution efficacy. HPFP achieves complete parallelism and delivers superior performance to become an efficient algorithm in HDFS, than existing distributed pattern mining algorithms.

The removal of Cr(VI) from aqueous solution by a novel Swietenia Mahogani fruit shell physically activated carbon (SMFS-PAC) was examined in a packed bed column study. The effects of significant parameters such as feed flow rate, bed depth, and initial Cr(VI) concentration were studied. Column data obtained at different conditions were described using the Thomas, Bed depth service time (BDST), Adams–Bohart, and Yoon–Nelson model in order to predict the breakthrough curves and to evaluate the model parameters of the packed bed column studies data. Among the models used, Thomas, BDST, and Yoon–Nelson model, respectively, fitted to the experimental data very well. The SMFS-PAC column study states the value of the good adsorption capacity for the removal of Cr(VI) from aqueous solution.

This study focuses on the use of novel Enteromorpha sp. macroalgal biomass (EMAB), for the biosorption of hexavalent chromium from aqueous solutions. The biosorbent was characterized by Fourier transformer infrared spectroscopy, energy dispersive spectroscopy, and scanning electron microscopy techniques. The effect of experimental parameters such as pH, initial concentration of Cr(VI) ions, biosorbent dosage, and temperature were evaluated. The maximum biosorption capacity for Cr(VI) was observed at pH 2.0. The modeling of the experimental data at equilibrium was performed using two parameter isotherm models. Both Langmuir and Freundlich isotherm equations better fitted the equilibrium data. A contact time of different initial Cr(VI) concentrations was about 160 min to attain biosorption equilibrium. The kinetic data were fitted by models including pseudo-first-order, pseudo-second-order, and intraparticle diffusion. The pseudo-second-order and intraparticle diffusion kinetic models adequately described the kinetic data. Moreover, the thermodynamic parameters indicated that the biosorption process was spontaneous, endothermic, and increased randomness in nature. The results showed that EMAB could be used as an effective biosorbent for the removal of Cr(VI) from aqueous solution.

This paper proposes an energy efficient scheme for cellular network using G-LEACH (Genetic Low Energy Adaptive Clustering Hierarchy). By using the genetic algorithm based upon adaptive clustering protocol the energy consumption of the every node can be reduced and then the lifetime of each node in the cellular network can be increased. The proposed algorithm is compared with LEACH (Low -Energy Adaptive Clustering Hierarchy). The G-LEACH is implemented by using three phase. They are preparation phase, set-up phase, and at last steady-state phase. The preparation phase is the initial phase of the algorithm, in this phase the cluster head extraction process is executed by within all the random cellular network nodes. All nodes send the condition and position within the cluster as a resubmit message to the base station of the cellular network. By using the resubmit message base station exploring the node as cluster head which has the most eminent energy for foster process. By using this phase the energy consumption of the node is reduced. In the set-up phase the base station disseminate the message to all the node and help the random node to form a cluster within the cellular network. At last the steady-state phase is executed merely once ahead the set-up phase in the process of cellular network. Simulation result shows the comparison between LEACH and G-LEACH and energy consumed by the each node in the cellular network

Classification is a model finding process which is used for segmenting the data into different classes based on some constraints. This work analyzes the road accidents in India data set using classification algorithms namely linear regression, logistic regression, decision tree, SVM, Naive Bayes, KNN, Random Forest and gradient boosting algorithm. Performance measures used are accuracy, error rate and execution time. This analysis is done in R data mining tool. The performance of KNN is better than other algorithms.

Background: Adolescents represent about 21.8 percent of India’s population. Various health risks with potentially life-threatening consequences become prominent in this age group. This study was undertaken with the objective to determine the morbidity profile of school going adolescents in Tamil Nadu.Methods: This descriptive, cross sectional study was planned and conducted from January 2016 to August 2017. The study population included 987 adolescent boys and girls aged between 10 to 19 years studying in high and higher secondary Government schools of Thiruvallur district of Tamil Nadu. Semi-structured questionnaire was used as data collection tool.Results: The mean age groups of this school going adolescent are 14.2 yrs. In the present study 583 (59%) of the study participants were affected by one or more morbidity condition. Among them, 395 (67.7%) were in the age group 10-14 years and 188 (32.2%) in the age group 15-19 years. 122 (21%) and 461 (79.1%) of male and female were affected respectively. In the present study, fever (21%) was the commonest reported morbidity followed by acute respiratory infection (15.7%) and acute gastrointestinal disease (13. 4%).Conclusions: This study shows that adolescents are prone to a wide range of morbidity conditions. Apart from respiratory and gastro intestinal diseases, reproductive tract infections and sexual health problems are important morbidities affecting this age group. There is strong need to sensitize health care practitioners at all levels, in both government and private sectors towards health problems in adolescent age groups.

Background: Diabetes mellitus is a metabolic syndrome due to insulin deficiency, characterized by hyperglycaemia. Indian diabetes risk score (IDRS) is the most commonly used one to determine the risk status. However there is lot of inconvenience and possible errors in measuring the waist circumference to determine the IDRS, hence the study was planned to evaluate if neck circumference could replace waist circumference in determining the diabetes risk.Methods: This cross sectional study was conducted among 300 study participants fulfilling the eligible criteria. Socio-demographic variables, parameters required for determining the IDRS was assessed, in addition, neck circumference (NC) was measured using standard protocol. Another risk score was calculated by replacing waist circumference (WC) with neck circumference and scoring was named as IDRS-NC. Pearson correlation and Wilcoxan sign rank test was done to find out the relationship between WC and NC and also to determine if IDRS-NC could replace IDRS.Results: Out of 300 study population, majority of the participants are in the age group of

Biosorption potential of Gliricidia sepium leaf powder (GSL) was investigated for elimination of hexavalent chromium (Cr(VI)) from synthetic waste-water. Behavioral effects on sorption of Cr(VI) over the biosorbent surface due to variation in pH, biosorbent-sorbate contact time, biosorbent dosage, initial chromium concentration, batch incubation temperature and agitation speed were analyzed using batch experiments. Characterization of the biosorbent includes point of zero charge evaluation, C-H-N-S analysis, Mercury intrusion porosimetry study, Scanning Electron Microscopy (SEM), Energy-dispersive X-ray (EDX) elemental analysis and Fourier Transform Infrared (FT-IR) analysis. The optimum parameters for maximal elimination (99.90%) of Cr(VI) with residual concentration of 0.48 mg/L were found at pH 2.0, contact time of 120 min, 0.3 g of biosorbent dosage, agitation speed of 100 rpm and 50 mg/L of Cr(VI) as the initial concentration. Freundlich isotherm model gave the best fit with the equilibrium data with adsorption capacity of 35.71 mg/g at pH 2 and 50 mg/L of Cr(VI)initial concentration. Pseudo-second order model best described the adsorption kinetics. Thermodynamic parameters validated the biosorption process as feasible, exothermic and stable. Desorption-regeneration experiments were performed with 0.5 N NaOH showed reasonable performances at equilibrium time. The data depicted the reusability of GSL as a promising, eco-friendly and economic biosorbent for Cr(VI) sequestration.

Wireless Sensor Network (WSN) plays an important role in the application of environmental monitoring. Mostly air pollution being major issues as cause many hazardous effects on ecological system of human being. Therefore the need of monitoring air pollution around the city and the public transport buses and cars are very important problem today. Basically, environmental monitoring methods have difficulties in wired sensor network but by using WSN it is possible to achieve the challenging issues by implementing internet/intranet. This proposed research concentrates on measuring the gas level of air contamination around the cities and reduces the man power and also increases the overall flexibility of sender and receiver. The main objective of the proposed system for the moving vehicles is monitor the NO2, Humidity, Temperature, CO levels of air contamination by using NO2 sensor, Humidity sensor, Temperature sensor, CO sensor. In our proposed work MANET(Mobile Ad Hoc Network) routing algorithm is used which has nearly 28 mobile nodes(Vehicles) provide a coverage area of 300meters around the city. The sensor data of the vehicles will be sent to the smart phones of the appropriate drivers to monitor effectively. The result of the proposed method includes the following parameters such as data type, speed of transmission, coverage of system, coverage area size, and No.of vehicles to closely monitor the proposed system.

Background: Nowadays the teens playing outdoor games are difficult to be seen and of course, they play all these games not in reality but in a screen using video games. The biggest revolution in electronic world has put our teens more addicted towards video games which not only affect their academic performance but also have impact on their behaviour and health. The objectives of the study were to assess the pattern of video game usage among the study population; to determine the influence of video game in behaviour of the study population.Methods: The study was conducted as a cross sectional study during January and February 2018, among medical students of tertiary care hospital, Chennai. The data was collected using preformed pretested questionnaire. Data were analyzed using SPSS 21.Results: Out of 211 study participants, majority 168 (80%) were males. Around, 97 (45.9%) participants, liked to play during night hours and most 114 (54.1%) of them preferred adventure and racing games. The change in behavioral patterns like, skipping sleep 20 (25.3%), using bad words 22 (27.8%), betting 40 (50.6%), risky stunts 38 (48.1%) were observed. Around half of the participants 97 (46%) were giving complaints of various health problems like finger pain, 72 (34.1%) wrist pain, 63 (29.8%) neck pain, and 54 (26%) eye pain due to usage of video games.Conclusions: The behavioral patterns explored in this study are alarming which suggest that this age group has to be sensitized on the self control to use the video games.

Background: Medical leadership, an essential skill for the emerging medical graduates has been rarely studied in India.Methods: A formative research was undertaken among 30 interns selected by purposive sampling method. Triangulation of free listing, pile sorting was done to elicit their perception on leadership attributes. This was followed by a semi-structured focus group discussion to elicit the characteristics and solutions to obtain adequate leadership skills. The data was analysed using Visual Anthropac 4.98.1/X software.Results: Interns perceived communication skills, impartiality, patience, honesty, self-discipline, receptive to others opinions, knowledge of medicine, self-confidence, and guiding teams as major characteristics of effective medical leadership. The respondents admitted they were deficient in the understanding of leadership traits as it was not required of them as medical undergraduates. Internship period provided multiple platforms to develop leadership skills such as demonstrating personal qualities, working with others, goal setting and delivering services individually thereby overcoming a training gap during undergraduate period. Interns suggested that structured training on leadership skills and personality development coupled with exposure to challenging clinical environments during the undergraduate period will help them emerge as confident leaders.Conclusions: Medical education reforms should support the inclusion of leadership training in the MBBS curriculum.

High-utility based itemset mining is the advancement of recurrent pattern mining that discovers occurrence of frequent transactions from a huge database. The issues in frequent pattern mining involve the elimination of quantities purchased by the customers and cost of purchased product. This can be resolved by high utility itemset mining which includes quantities and profit of the products in the transactions. The conventional association rule mining algorithms results in huge memory consumption due to the complexity in pruning the search space. In this paper, machine learning based high-utility itemset mining is applied to predict next order in an online grocery store depending on the transactions. The overall goal is to enhance the business profitability by stocking the high utility items in market. The Top ‘N’ variant Random Forest model is proposed to recommend the high utility itemsets, thereby predicting the reordered/next ordered items. The model is evaluated using Instacart market dataset to measure accuracy, precision and recall.

Background: In spite of the fact, that, Body mass index being a most commonly used epidemiological tool for the assessment of nutritional status, it does not measure the visceral adiposity, which is the indicator for future cardio-metabolic risk. Hence the study was planned to determine whether Waist height ratio, which is an effective tool in measuring the visceral adiposity, has any relationship with the BMI, in assessing the nutritional status.Methods: The cross sectional study was carried out among 980 school going adolescent girls, studying in class VI-XII standard, in the schools present in urban field practice area of Tagore Medical College and Hospital. Anthropometric assessment was done. BMI and WHtR was calculated, to assess the nutritional status. Relationship between BMI and WHtR was found.Results: The mean age of the participants was 14±2.9 years. Number of participants, who were normal, underweight, overweight, obese based on BMI values were 227, 5, 301, 447 respectively According to the WHtR, 560 (57%) of them were obese, while 420 (43%) were non-obese. Moderate correlation exists between BMI and WHtR, with r = 0.68.Conclusions: Waist height ratio having a positive correlation with the BMI in the assessment of nutritional status, preference of WHtR over BMI should be encouraged, as it also helps in measuring the visceral adiposity, which, is a potent risk factor for various metabolic and cardiovascular diseases.

Background: Questionnaire is the most common tool for data collection in most of the study designs. In spite of the advantage of using a standardized questionnaire for generalising the study findings, the major disadvantage is that all the population is never the same and its similarity exists only with the presence of heterogeneity, hence, any universal cut-off value cannot be used for the diverse population. This makes it clear that determination of cut-off value for any content validated questionnaire to the specified study population is essential, in order to make the study tool more effective.Methods: This study was done to determine the cut-off value of psychometric scale of selfie addiction, which was content validated. A detailed mathematical model was used to determine the cut off value. Item analysis was done. Discrimination index, weightage of each item and correction factor was calculated to determine the cut-off value.Results: The total weighted score, total raw score and correction factor are 28012.62 and 31,046, 0.9 respectively. The total adjusted scale cut-off is 30.43 (rounded as 30). The cut-off value based on crude mid-value is 25 and the cut off value determined for the standardised population is 21.Study population with cut-off value of >30 are considered to be a selfie addict, and those who have obtained a total score ≤30 are considered to be normal (non-selfie addict).Conclusions: Present study is one of its kind, in determining the cut-off value for a content validated psychometric scale without any gold standard. The above derived cut-off value of 30 for the psychometric scale of selfie addiction is valid for the specified population, as the Cronbach’s alpha, discrimination index and the correction factor is above 0.75.

Background: Providing universal family planning services is an important strategy to reduce maternal morbidity and to control population growth. Worldwide, when contraception is used properly and effectively to avoid unwanted pregnancy it can reduce 25–35% of maternal deaths. The present study aimed at measuring the proportion of married women with unmet need for family planning in an urban area of Puducherry.Methods: A cross sectional study was conducted among 235 married women in the urban field practice area of Sri Venkateshwara Medical College Hospital & Research Centre, Puducherry. Participants were selected based on predetermined eligibility criteria using systematic random sampling method. SPSS version 23.0 was used for statistical analysis. Proportion, Mean, Standard deviation and Logistic regression were applied to interpret the results.Results: Mean age of the participants was 33.3 ±6 years, Nearly 77 %( 181) of the participants reported having used any method of contraceptive at least once and 34.5 %( 81) were found to have unmet need for family planning. The most common reason for unmet need for contraception was fear of side effects (39.5%) and others were infrequent sex (12.3%),insisted by family members (12.3%) and religious reasons (12.3%).Conclusions: Unmet need for family planning was found to be fairly high in our study population. Health care personnel, preferably field staff should be trained to give an informed choice of contraception to the eligible couples to reduce the unmet need for contraception.