Miracle-dz/UniFAR-FTUs huggingface dataset:
Impacts of ambient air quality data analysis in urban and industrial area helps in policy making

July September 2016 1088 JCPS Volume 9 Issue 3 Impacts of ambient air quality data analysis in urban and industrial area helps in policy making Christy S, Khanaa V Bharath University, Chennai, Tamil Nadu, India, *Corresponding author: E-Mail: christymelwyn @ gmail.com, Drvkannan62@yahoo.com ABSTRACT Air pollution affect our health and environment in many different ways. In past few years, the heavy environmental loading has led to the deterioration of air quality in urban and industrial areas in Chennai. The task of controlling and improving the air quality is essential for a developing country. Data mining in ambient air quality data is concerned with finding the information inside the largely available data. The information retrieved can be transformed into usable knowledge. The problem of air pollution is becoming a major concern for the health of the population. The ambient air quality data collected from Central Pollution Control Board and Tamil Nadu Pollution Control Board websites. Air quality is monitored by air quality monitoring stations deployed in huge numbers using wireless sensors around the city and industrial areas in Chennai. The four years of data from January 2012 to December 2015 are collected from various monitoring stations and processed. Data mining technique is used in prediction, forecasting and support in making effective decision for future plan. The data can be analyzed by the data mining techniques using neural network models. The pattern obtained from these models could serve as an important reference for the Government policy makers in devising future air pollution standard policies.
Clustering Algorithm for Resemblance Measure through Convex Coding

:Relational information looks frequently in many machine learning applications. Relational information consists of the dyad sapient cognations (kindred attributes or dissimilarities) between each pair of implicit objects, and are customarily stored in cognation matrices and typically no other cognizance is available. Albeit relational clustering can be formulated as graph division in approximately diligences, this formulation is not adequate for general relational information. In this paper, we aim a universal model for relative clustering predicated on symmetric convex coding. The sample is applicable to all types of relational information and cumulates the subsisting graph partitioning formulation. Under this model, we derive two option bounce optimization algorithms to figure out the symmetrical convex coding fewer than two popular distance functions, Euclidian length plus extrapolated I-divergence. Experimental evaluation and theoretical analysis show the efficacy and great potential of the proposed model and algorithms.
A Literature Survey on Mobile Cloud Computing: Open Issues and Future Directions

- Given the advances in mobile phones, users start to consider a mobile phone a personal information processing tool. So users want to execute its various operations on the top of mobile devices. Researchers have long recognized that mobile hardware is necessarily resource poor relative to static client and server hardware. Mobile cloud computing (MCC) which combines mobile computing and cloud computing is a good solution to this problem and has become one of the industry buzz words and a major discussion topic since 2009. This paper presents a review on the background and principle of MCC, characteristics, recent research works and future research trends.
BIOSYNTHESIS, CHARACTERIZATION AND ANTIBACTERIAL ACTIVITY OF SILVER NANOPARTICLES USING THE LICHEN

Currently, Green nanotechnology principle is highly applicable in synthesis of nanoparticles through simple techniques. In this study, we demonstrate the synthesis of silver nanoparticle by ethanol extract of algae fungi intermediate lichen Parmotrema perlatum. The synthesized silver nanoparticles were analyzed by UV visible spectrophotometer, XRD, FTIR, and SEM analysis. Lichen extract could be able to synthesize silver nanoparticles as proved by gradual change in the color of the reaction mixture consists of 10 ml extract in 1mM Agno3 to dark brown color. Silver nanoparticles formation was confirmed by surface Plasmon resonance (SPR) peak obtained around 400 – 420 nm in UV Visible spectrophotometer. The morphology of the biogenic silver nanoparticles was identified by SEM analysis and is polydispersed in nature. The occurrence of silver in the sample and its crystalline nature was confirmed by X-ray diffraction technique. Silver nanoparticles showed 2Θ values within the range of Bragg’s reflection (38.06 and 77.43) consequent to the existence of silver nanocrystal as obvious by X-ray diffraction spectrum. FTIR results were used to identify the biomolecules which is responsible for the silver ion reduction. The FTIR results of lichen extract synthesized silver nanoparticle displayed the prominent peaks in (3210.17, 2849.93, 2917.38, 1652.85, 1609.87, 1452.63, 1378.45, 1145.25, 823.36, 585.50, 527.27) in different ranges. These biogenic silver nanoparticles exhibited substantial European Jou nal of Biomedical AND Pharmaceutical sciences http://www.ejbps. com ISSN 2349-8870 Volume: 2 Issue: 4
A PROCEDURE TO DETERMINE THE INTRINSIC MODEL OF CNTFETs FOR RF APPLICATIONS

The aim of this paper is to present a procedure to determine the intrinsic model of Carbon NanoTube Field Effect Transistors (CNTFETs) for RF applications. In particular an active model, deduced from S parameters measurements, already proposed by us, is examined. Moreover, through a de-embedding procedure, which allows to remove random errors in measured S parameters of small-signal device, we evaluate the intrinsic model of CNTFETs to implement directly in simulation software.
Adaptive Load Detection Technique for Effective Virtual Machine Consolidation

: Cloud Computing (CC) is a hi-tech way of delivering the shared pools of software, platform and hardware resources as service through internet. The delivery and access to the resources is dynamic, convenient, on-demand and on basis of pay-as-per usage. The virtualization technology along with concept of distributed computing and other technologies made this complicated provisioning possible. Due the growing computational needs, the major challenge faced by cloud stakeholders is maintaining power-performance trade-off by satisfying high Quality of Service (QoS) and Service Level Agreements (SLA). At cloud Data Centers (DCs), the suitable optimization in power consumption through Dynamic Virtual Machine (VM) Consolidation is seen as potential approach for reducing energy consumption while maintaining QoS and SLAs up to a good level. The approach dynamically regulates the number of active machines to match resource demands while keeping others in sleep/hibernate mode to save energy. The efficient determination of overloaded and under-loaded hosts is the primary step in Dynamic VM consolidation. Based on this,the effective decisions like migration of VMs to other hosts can be taken to optimize the power. The theme of this work is to propose the ‘ Adaptive Load Detec tion Technique for Effective Dynamic VM Consolidation’ and provide the optimization for better performance and environment. The load detection technique proposed in this work is utilizinga statistical method for determining overloadedhosts. The statistical method determines adaptive thresholds to detect over load conditions. The technique is then combined with few VM selection strategies for obtaining Effective Dynamic VM Consolidation.In this work few statistical techniques for detecting overloaded hosts that are part of Energy-Efficient VM Consolidation Policies are studied. The comparison / survey of VM consolidation algorithms based on various heuristics are presented. For our simulations CloudSim toolkit version 3.0. 2 is used with real world workload traces of VMs. The experimental results demonstrates that the proposed policy is scalable and offers better consolidation quality and energy saving while effectively dealing with firm QoS requirements negotiated by SLAs.
Morphological and Histological Changes in the Camel Testes In Relation To Semen Characteristics During Breeding and Non-Breeding Seasons

A total number of 65 clinically healthy male dromedary camels were used in the present study. The age of these camels ranged from 5 to 10 years and their weights were approximately 500 kg an average. The experimental work aimed to investigate the effect of breeding and non-breeding seasons either hot-humid or hot-dry months on testicular measurements, epididymal semen characteristics and histological changes in the right and left testes of the dromedary camels. The obtained results showed that, the testis weight, testicular volume, scrotal circumference, testis tone firmer score and the percentage of sperm motility, sperm-cell concentration of the dromedary camels were significantly (P<0.05) higher during breeding than non-breeding season either hot-dry or hot-humid months, while the percentages of dead spermatozoa, sperm abnormalities and acrosomal damage of spermatozoa were significantly (P <0.05) higher during hot-humid months as compared to both hot-dry months and breeding season. Semen colour was creamish white, milky white and watery white during breeding season, hot-dry and hot-humid months, respectively. Semen consistency was viscous during breeding season and hot-dry months and semi-viscous during hot-humid months. Seminal pH value was significantly (P <0.05) higher during hot-humid months as compared to both breeding season and hot-dry months. The testis of the male dromedary camels during breeding season showed consisted of numerous semineferous tubules (ST) with different shapes and size (oval, ovaid and circular) were highly active. The ST lined by spermatogenic cells at different maturation stages (spermatogonium, spermatocytes, spermatid and spermatozoa) are present as compared to camels during non-breeding season either hot-dry or hot-humid months. [Maiada, W.A. Allam, E.B. Abdalla, A.E.B. Zeidan, M.H. Farouk, and A.M. Abd El-Salaam. Morphological and Histological Changes in the Camel Testes In Relation To Semen Characteristics During Breeding and NonBreeding Seasons. J Am Sci 2013;9(11s):74-82]. (ISSN: 1545-1003). http://www.jofamericanscience. org. 13
Evaluation of Peroxidase and Catalase Correlations with Physiological Traits in Water deficit Irrigated common Beans

To evaluate effects of water deficit on antioxidant enzyme activities and some physiological traits, two experiments based on completely randomized design were carried out under field conditions. In experiment 1, plants irrigation cycle was each 5 days (normal condition) whereas in experiment two, once per 10 days. In both experiments, 11 genotypes of common beans: white, red, pinto as well as three improved varieties Akhtar, Dehghan and D81083 were studied. Relative water content (RWC) and stomatal resistance were studied in all experimental plots. Catalase (CAT) and peroxidase (POX) activity were determined using three plants via electrophoresis in polyacrylamide gels (8%). According to data, three isozymes were detected for POX and one monomorphic band for CAT. There was significant difference for RWC and stomatal resistance between all genotypes of bean in both irrigation conditions which is an indicator for high level of genetic variation in bean. Pinto and white beans had better performance than red during water deficit condition. There was a significant and positive correlation between CAT and POX1 in both irrigation conditions. The highest positive and significant correlation was observed between POX1 and POX2 in normal irrigation, whereas POX2 and POX3 in water deficit condition. In both experiments, stomatal resistance had negative correlation with RWC. CAT and POX3 activities had negative correlation with stomatal resistance in normal condition while CAT and POX1 had negative correlation with RWC in water deficit condition.
New Improved Feature Extraction Approach of IRIS Recognition

Iris recognition is used for identify the person by recognizing iris pattern of eye of a person. Currently there is no method of IRIS recognition system that have 100% recognition rate using Gabor filter. So research issues are to improve recognition rate by improving the pre-processing of datasets, improving the feature extraction method and using the best classifier for iris recognition. Feature extraction is the key step on which recognition rate depends for iris recognition. Gabor filter extract the edge information of iris pattern using the projection on iris image but have a problem of huge dimension and high redundancy. In the proposed technique, the dimension and redundancy is reduced effectively in order to increases accuracy.
Fabrication and Characterization of Floating Microspheres of H2 Receptor Antagonist

In the current exploration a novel oral drug delivery system was developed utilizing the concepts of controlled release and mucoadhesiveness. Mucoadhesion is a topic of current interest in the design of drug delivery systems. Mucoadhesive microspheres exhibited a prolonged residence time at the site of application or absorption and facilitate an intimate contact with the underlying absorption surface and thus contribute to improved and/or better therapeutic performance of drugs. Nizatidine microspheres were equipped by emulsification-ionic gelation technique using mucoadhesive polymers such as Carbopol 934P, Hydroxypropyl methylcellulose (HPMC K15M) and Carboxy methyl cellulose with a rate controlling polymer sodium alginate. The arranged formulations were subjected to particle size and shape analysis, % drug entrapment efficiency, in vitro floatability, swelling rate, in vitro mucoadhesion test and in vitro drug release kinetics. The primed microspheres were discrete, spherical with a mean particle size in the range of 478±0.99μm to 640 ±2.67 μm. Entrapment efficiency was found to be in the range of 59.56±0.99 to 79.90± 1.78%. Formulations containing Carbopol 934P showed amplified in vitro mucoadhesion compared to formulations with Hydroxypropyl methylcellulose and CMC. In vitro drug release for all the formulations in 0.1N HCl was diffusion controlled progressively over a period of 12 h and followed First order kinetics. The in vitro drug release mechanism was nonfickian type controlled by swelling and relaxation of polymer. There was no noteworthy alteration in physico-chemical characteristics of the microspheres stored at diverse storage condition after 3 months of stability study. The fabricated system has the twofold advantages of being gastroretentive, to augment oral bioavailability and releasing drug in a controlled manner, to diminish the required frequency of administration thereby promoting patient compliance.
Sentiment Analysis of Events from Twitter Using Open Source Tool

— Now a days, Popularity of Internet has been rapidly increased. Sentiment analysis and opinion mining is the field of study that analyses people's opinions, sentiments, evaluations, attitudes, and emotions from written language. User generated contents are highly generated by users. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. It is difficult to analysis or summarize the user generated content. Most of the users writes their opinions, thoughts on blogs, social media sites, E-commerce site etc. So These Contents are very important for individuals, industry and research work to take decisions. For this Sentiment analysis and opinion mining research is hot research area which comes under the Natural Language processing. R is open source tool. In this Paper we are elaborating the different approaches of Sentiment Analysis and Opinion Mining for different dataset and find out the which approach is best for which dataset which will help to researchers to select approach and dataset. In proposed work we collected tweets using R tool of different events from twitter and did pre-processing and calculate sentiment score from that events. We plot Wordcloud of particular event which highlight the frequent term from tweets and also calculated numbers of positive, negative and neutral tweets from each
A Survey on Content based Semantic Relations in Tweets

is a popular micro blogging site, in which people share their ideas and views with others. Because of different writing conventions and character restriction there may be variation in the impact for the same event. Analyzing semantic relationship and analyze the variations have several use cases such as event detect etc. there are several technique are available for event detection and term similarity analysis using semantic information of tweets. This survey paper aims to highlight these techniques.
Discriminative Robust Local Binary Pattern Based Face Recognition From A Single Sample Per Person

Nowadays face recognition plays an important role in today’s world. The core objective of this project is to extract the facial features using the local appearance based method for the accurate face identification with single sample per class. The face biometric based person identification plays a major role in wide range of applications such as Airport security, Driver’s license, Passport, Voting System, Surveillance. This project presents face recognition based on Difference of Gaussian and feature extraction using Discriminative Robust Local Binary pattern Pattern(DRLBP)approach. The Median filter is used to extract the hybrid features and the pyramids are generated after the face granulation. Then, DoG pyramid will be formed from successive iterations of Gaussian images. By this granulation, facial features are segregated at different resolutions to provide edge information, noise, smoothness and blurriness present in a face image. In feature extraction stage, this binary face template act like a mask to extract local texture information using Discriminative Robust Local binary pattern. This method is efficient to face recognition since it is less sensitive to illumination and scaling. It reduces the computational time complexity and space complexity. This proposed approach reduces the computation time and also increases the efficiency.
Review of Retinal Blood Vessel Segmentation Techniques

—Vessel segmentation algorithms are critical components of circulatory blood vessel analysis systems. We present a survey of vessel extraction techniques, putting the various approaches and techniques in perspective by means of a classification of the existing research. We review some of the segmentation methods shows characteristics of vessels. We divide vessel segmentation algorithms and techniques into three main categories: (1) Pattern Classification and Machine Learning (2) Matched Filtering, (3) Multi-scale Techniques, Some of these categories are further divided into different sub-categories.
A CRITIQUE OF THE ADEQUACY OF POSITIVIST AND INTERPRETIVIST VIEWS OF ORGANISATIONAL STUDIES FOR UNDERSTANDING THE 21 CENTURY ORGANISATION(S)

This paper theoretically evaluated the adequacy or otherwise of the Positivist/Managerialist approach to organisational study in understanding organisations. The review of related literature revealed that positivism and managerialism through their scientific and quantitative characteristics help organisational researchers achieve an objective understanding of organisations which enables managers to make informed predictions about future expectations of business activities but the approaches do not take cognizance of the human experiences and subjective influences which more often than not exact great impact on organisational performance thereby making the decisions made following the outcome of positivist-managerialist oriented studies unrealistic and basically insufficient for understanding 21 century organisations. On the contrary, the literature shows that interpretivists/critics argue that organisational processes and activities can only be understood and interpreted in the light of the context in which they happen and by taking into account the subjective characteristics of the actors and conclusions are drawn from the real life experiences and context-based perspective of those actors. However the findings based on the interpretivist approach is ineffective for making predictions about future events and therefore often seen by managers as absolutely unnecessary and practically irrelevant for achieving predetermined goals. It was therefore concluded that none of the alternative approaches on their own can lead to a complete understanding of organisations hence the paper suggests a mixed method such that the scientific, quantitative qualities of positivism can complement the subjective, qualitative qualities of interpretivism and vice versa, for a better understanding of organisations while the search for the one-best approach continues.
Text Document Clustering Using DPM with Concept and Feature Analysis

— Clustering is one of the most important techniques in machine learning and data mining tasks. Similar documents are grouped by performing clustering techniques. Similarity measuring is used to determine transaction relationships. Hierarchical clustering model produces tree structured results. Partitioned based clustering produces the outcome in grid format. Text documents are unstructured data values with high dimensional attributes. Document clustering group ups unlabeled text documents into meaningful clusters. Traditional clustering methods require cluster count (K) for the document grouping process. Clustering accuracy degrades drastically with reference to the unsuitable cluster count. Document features are automatically partitioned into two groups’ discriminative words and nondiscriminative words. Only discriminative words are useful for grouping documents. The involvement of nondiscriminative words confuses the clustering process and leads to poor clustering solution in return. A variation inference algorithm is used to infer the document collection structure and partition of document words at the same time. Dirichlet Process Mixture (DPM) model is used to partition documents. DPM clustering model uses both the data likelihood and the clustering property of the Dirichlet Process (DP). Dirichlet Process Mixture Model for Feature Partition (DPMFP) is used to discover the latent cluster structure based on the DPM model. DPMFP clustering is performed without requiring the number of clusters as input. Discriminative word identification process is improved with the labeled document analysis mechanism. Concept relationships are analyzed with Ontology support. Semantic weight model is used for the document similarity analysis. The system improves the scalability with the support of labels and concept relations for dimensionality reduction process. The system development is planned with Java language and Oracle relational database.