Miracle-dz/UniFAR-FTUs huggingface dataset:
Generative Adversarial Networks(GANs): Applications and Future Scope

Generative Adversarial Networks, GANs are the most recent and exciting development in the field of generative models. Inspired by game theory, the system consists of two models, one is a generator that generates the information and whose goal is to fool the discriminator who has been trained to differentiate between real and fake samples. In this paper, the architecture of the models is explored along with its ideology. Research on application of GANs have shown improvement in various possible areas over the traditional models. Especially areas pertaining to images such as image filling and text to image have shown promising results. However, because of GANs being relatively new, there remains a lot to be done. In this paper, we explore the existing models and the drawbacks or possible areas of improvement of these models. We finally discuss the open-ended research areas such as repetition, system equilibrium, finding appropriate metrics for evaluation and various others that would help make GANs more adaptable and would help achieve the potential they have for effectiveness in real-world scenarios such as realistic speech generation.
VLANs Investigation in IEEE 802.11s Based Wireless Mesh Networks

— The virtual local area network (VLAN) technology is a convenient concept to improve the wireless mesh networks performance by eliminating the unnecessary rebroadcasts flooded from stations located outside the mesh BSS. Hundreds or even thousands of stations may be located at a IEEE 802 LAN segment which leads to think about broadcasting cost if such segment is bridged to the wireless mesh BSS. The latter is seen as a single broadcast domain from external networks. Consequently, flooding may hinder the transmission of data frames due the broadcasting storm problems. VLANs is a logical concept that aims to segment a network into different broadcast domains by compartmentalizing users and devices. A bridging solution that carry VLANs traffic along the mesh BSS may reduce the flooding impact on data frames transmission. In this paper, we investigate the VLAN support for IEEE 802.11s. We were motivated by the fact that no specification of VLAN integration has been defined in the draft of IEEE 802.11s.
Stem Cell Researches Using Animal Mouse

Stem Cell is the original of life. All cells come from stem cells. Serving as a repair system for the living body, the stem cells can divide without limit to replenish other cells as long as the living body is still alive. When a stem cell divides, each new cell has the potential to either remain a stem cell situation or become another type of cell with a more specialized function, such as a muscle cell, a red blood cell, a bone cell, a nerve cell, or a brain cell. Stem cell research is a typical and important topic of life science. This material collects some literatures on mouse stem cell. [Smith M H. Stem Cell Researches Using Animal Mouse. Stem Cell 2014;5(1):52-76] (ISSN 1545-4570). http://www.sciencepub. net/stem. 6
EVALUATION OF ANTIBIOTIC PRODUCTION IN MICROMONOSPORA BY HPTLC

A total of 5 different types of actinobacteria were isolated from the soil of Ganges river bed, Haridwar. All the isolated actinobacteria were characterized and identified based on the morphological and biochemical characteristics. The isolates were belonged to the genera Micromonospora, Streptomyces and Nocardia. Only Micromonospora sp. was selected for further studies. Antibacterial potential was determined by both cross streak method and disc diffusion method. The result of the screening revealed that the crude extract was effective against E.coli and S. aureus. It was further analyzed by high performance thin layer chromatography. Rf value in the TLC chromatogram characterized that, the metabolite comes under aminoglycoside group very much related to gentamicin.
Investigating the charging potential of Plug-in Hybrid Electric Vehicles integrated with Sustainable buildings

One of the major challenges of the twenty-first century is developing technologies that reduce greenhouse gas emissions. One technology with the potential to address these challenges is plug-in (hybrid) electric vehicles (PHEVs). PHEVs derive much of their energy from the electric power grid rather than gasoline. If the projections of large PHEV penetration are true, they will put considerable additional stress onto existing power grids. It has been proposed that the appropriate scheduling of PHEV charging can reduce this stress through demand response. The PHEV charging scheduling has multiple facts. The focus of this paper is to develop algorithmic approaches to deal with the uncertainties associated with PHEV charging. The scheduling problem is modeled as a multi-stage online decision problem where input parameters of future charging requests and power grid status are not revealed when current charging decisions are made. We present two algorithms: consensus and expectation that use predictions about the future to make scheduling decisions.
Time-Resolved On-Axis Spectroscopic Stagnation Temperature Measurements in Shock Tunnel Flows

This paper demonstrates time-resolved stagnation temperature measurements in a shock tunnel at a frequency of 25 kHz using emission spectroscopy in air and nitrogen test conditions. The two most important parameters for determining the flow conditions generated in a shock tunnel experiment are the stagnation pressure and temperature of the flow just upstream of the supersonic nozzle. While the pressure can be measured using a wall-mounted transducer with relative ease, the measurement of the temperature requires a optical technique such as time resolved emission spectroscopy. Knowledge of the transient stagnation temperature behavior is critical to all subsequent expansion tube flow processes. The driver gas emission spectrum data at the post-shock condition shows continuum and atomic line radiation. The continuum radiation can be described by a black body radiator with the individual spectra showing sufficient continuum information for accurately fitting Planck functions. Atomic line radiation was excluded by skipping those data from the measured spectra. The fitting routine shows clear differences in determined temperatures including and neglecting atomic line radiation. These measurements allow for the exact determination of the shock tunnel flow conditions in combination with pressure transducer data. The flow condition used in the experiment corresponds to a nominal Mach-10 condition at an altitude of 65 km, however, the technique is not limited to this condition and can be used for a large range of flow conditions.
CHALLENGES & DESIGN ISSUES IN WSN

: Wireless Sensor Networks have become an important research topic in last years. WSN is a collection of tiny, large number of densely deployed sensor node; these sensor nodes are smart, effective which is very powerful and versatile networking where traditional wired and wireless networking is unable to deploy. These sensor nodes have limited transmission range, processing and storage capabilities as well as their energy resources. In sensor networks, minimization of energy consumption is considered a major performance criterion to provide maximum network lifetime. When considering energy conservation, routing protocols should also be designed to achieve fault tolerance in communications. In this paper, we present a review of some major challenges & issues related to wireless sensor networks.
DNA Barcode Genes (rbcL, 18s rRNA and ITS Phylogeny) in Skeletonema costatum Grevelli (Cleve, 1873)

Diatoms live in all types of water bodies from fresh to marine habitats and also in soil and aero-terrestrial ecosystems. Because they are sensitive to pollution, acidification, and salinization, they are important bioindicators and are often used for routine water quality assessments (Stevenson and Pan., 1999). Since morphological identification of diatoms is time-consuming and demands specialized in-depth knowledge, DNA barcoding method provide easy, rapid and exact identification. So far, very few works have been undertaken on the topic DNA barcoding in diatoms (Kaczmarska et al., 2007). Many ecological and monitoring studies are misleading, because identifications have not been verified by experienced taxonomists and also decrease in number of taxonomist in the field has been witnessed (Morales et al., 2001).
Evaluating the Efficiency of CPUs, GPUs and FPGAs on a Near-Duplicate Document Detection Via OpenCL

Discovering identical or near-identical items is urgently important in many applications such as Web crawling since it drastically reduces the text processing costs. Simhash is a widely used technique, able to attribute a bit-string identity to a text, such that similar texts have similar identities. In this study, a real-time solution for a simhash calculation in OpenCL is presented. We also show how it can be utilized by multi-CPUs, GPUs and FPGAs. As a result we indicate that the bottom line computation realized on the FPGA through OpenCL provides significant power advantages.
MISALIGNED IMAGE INTEGRATION BASED ON CONVEX OPTIMIZATION

- Images captured by the camera in poor lighting conditions are not suitable for obtaining a clear picture. It will cause blurred or noisy elements in the captured image. It is a serious problem. A new method for obtaining a natural color image under reduced illumination has been proposed here. It is a new method of image integration used to combine two images in order to obtain a natural image under reduced lighting conditions. The long-exposure and the flash images are used as two images for integration. Combining the color details of the long-exposure image with features of the flash image. Most of the existing systems required perfect alignment between pairs of images for integration. However proposed method does not require perfect alignment between flash and long-exposure images. Here express the integration of images as a convex optimization algorithm with primal dual splitting algorithm and produce output image in natural color from high contrast and blurry images.
APPLICATION OF TAGUCHI METHOD FOR OPTIMIZATION OF PROCESS PARAMETERS FOR MINIMUM SURFACE ROUGHNESS IN TURNING OF 45C8

Industries across the world are struggling to overcome the problem of rejection rate. In order to meet the customer’s satisfaction, it is important to maintain the better quality of the product which can only be afforded by reducing the rejection rate. Surface roughness is one the important parameter in reducing the rejection rate. In this study, the Taguchi Method is used in order to find out the optimal cutting parameters to get the better surface roughness of 45C8 steel. Various cutting parameters viz: cutting speed, fed and depth of cut were taken and Taguchi method is used to find out the optimal combination so as to get the better surface roughness. Orthogonal arrays of Taguchi, the signal-to noise ratio (S/N) ratio, the analysis of variance (ANOVA) are employed to find the optimal levels and to analyze the cutting parameters on surface roughness. Finally confirmation tests with the optimal levels of cutting parameters are carried out in order to demonstrate the effectiveness of Taguchi optimization method. Key words— ANOVA, 45C8 (EN8), Surface roughness, S/N Ratio, Taguchi Method.
Optimization of Two-Stroke Petrol Engine

This paper discusses the optimization of two stroke engine with the use of fuel injection system and expansion chamber. This proposed optimization is in the form of lowered emissions following better in-cylinder combustion and enhanced engine performance characteristics. The use of fuel injection system in this engine facilitates proper mixture of fuel and air, giving increased control of combustion and emissions, as a result of which power output and efficiency increases. Fuel injection system is also economical as it provides a correct estimation of the quantity of fuel required at proper time. However, there still exists the problem of fresh charge leaking during scavenging, which decreases the volumetric efficiency. This can be compensated up to a certain extent by the use of an expansion chamber. The introduction of an expansion chamber reduces the emissions while increasing power output due to increased volumetric efficiency. Keywords— expansion chamber, fuel injection, two stroke engine, engine performance, emissions
Design, Development and In-Vitro Evaluation of Orodispersible Tablets of Nateglinide Using Direct Compression Method

Nateglinide is the meglitinide derivative class of blood glucose lowering agent. It's used for the treatment of type 2 diabetes mellitus condition most in pediatric and geriatric populations. the Novel drug developments In fast disintegration/dissolving tablets this have a convenient dose in pediatric, elderly patients and who have trouble in swallowing tablets.so the present study and aim of formulation convenient of administration and patient acceptability friendly dosage from i.e. fast dissolving tablet nateglinide was carried out using formulation techniques direct compression with the addition of superdisintegrants. Superdisintegrants such as croscarmellose sodium, crospovidone, and sodium starch glycolate. Different binders using and optimized superdisintegrants concentration. The tablets evaluated for friability, hardness, weight variation, wetting time, disintegration time, and content uniformity. The formulation optimized by evaluation of in-vitro dissolution study, drug-excipient compatibility and accelerated stability study. The formulation was concluded fast disintegration tablets of nateglinide were formulated successfully with desired characteristics and rapid onset of action. And enhanced the patient convenience and compliance. Dhanapal Y. *, Sathiesh M. Balachandar K Department of Pharmaceutical Analysis, PSG College of Pharmacy, Peelamedu Coimbatore 641004, India. Submission: 29 October 2018 Accepted: 4 November 2018 Published: 30 November 2018 www.ijppr. humanjournals.com Citation: Dhanapal Y.et al. Ijppr.Human, 2018; Vol. 13 (4): 220-243. 22
A Study on Autism Spectrum Disorders using Classification Techniques

-In recent years, the academic establishments has undertaken variety of initiatives to support school boards, college authorities, special schools and colleges in teaching students with Autism Spectrum disorders(ASD). Autism Spectrum Disorders (ASD) are complex neurological disorders that have a lifelong effect on the event of assorted talents and skills. The foremost vital goal of the paper is to review the autism problem, to detect the levels of autism with the help of data mining classification algorithms. The data mining has been typically accepted as a decision making process to facilitate higher resource utilization in terms of autism students’ performance.
Study on Liner wear in Single Toggle Jaw Crushers: A Review

This article presents a review and discussion of the causes of liner wear in single toggle jaw crushers. Literatures from different authors were sorted, reviewed and discussed. The various articles considered showed that the main cause of the rapid wear of jaw liners is the sliding interaction between the crushed material and the jaw liners during the crushing operation. Reducing the sliding interaction will therefore relieve the rapid wear of the jaw liners. Though some researchers have attempted to resolve the rapid wear problem by altering the material constituents of the jaw liners, it has not been able to yield much results. This is because the key aspect in resolving wear failures is to recognize wear as a system property or characteristic and not only a material property[1]. The sliding of the crushed material on the surfaces of the jaw liners can be reduced by changing the surface profile of the jaw liners.
Digital Image Watermarking using Machine Learning Techniques: A Technical Review

: Recently, digital information can be easily manipulated, copied, distributed and stored, which has resulted in the demand for safe ownership of the information. The watermarking provides a very good solution to the problem of copyright protection and content authentication. This paper discusses basic concepts of digital watermarking and its characteristics, important attacks on watermark system, general watermark embedding and extraction process, important transform and machine learning techniques and some reported transform and spatial domain based watermarking method using machine learning. Finally, the current state-of-the-art in the field is also discussed. This paper will be more important for researchers to implement effective watermarking method using machine learning technique.
Issues and Challenges in Big Data Mining

Big Data is fast becoming a big problem since last year. Big data refers to datasets which has large size and complexity. We can‘t capture, store, manage and analyze with typical database software tools. Data mining is highlighted buzzword that is used to describe the range of Big data analytics, with collection, extraction, analysis and statics. Big Data mining involves to extracting useful information from these huge sets of data and streams of data, due to its volume, velocity and variety. This paper describes an overview of Big Data mining, problems related to mining and the new opportunities. During discussion we include platform and framework for managing and processing large data sets. We also discuss the knowledge discovery process, data mining, and various open source tools with current condition, issues and forecast to the future.
A COMPREHENSIVE STUDY OF INDEPENDENT COMPONENT ANALYSIS (ICA) IN THE CHARACTERIZATION OF HUMAN FACES.

: Biometrics is the computer based validation of a person’s identity and is becoming more and more essential due to the increasing demand for high security systems. The objective of this work is to implement and assess the performance of independent component analysis technique in the characterization of human faces. The ICA is implemented using MATLAB 7.0 package which defines the input layer, the hidden layer and the output layer of the network. The Independent component analysis is an array of higher order statistical neurons which learns by examples through a training process before it can function in its area of application. The face recognition code was also trained and tested using cropped face images of 50×50, 60×60, 70×70, 80×80 and they all have the same recognition rate which is 100%. In conclusion, face detection and recognition using independent component analysis is a robust and reliable method
Sorption of metal oxoanions from aqueous solution by surfactant modified zeolite Mordenite, Linde Type A and Alpha

The hazardous ill effects of heavy metals on the environment and public health is an issue of serious concern. The use of zeolites for environmental applications is gaining new research interests mainly due to their properties and significant worldwide occurrence. The present work describes the synthesis, surface modification of zeolite and their use as sorbent. The removal efficacy of synthesized zeolite materials for the selective removal of metal as oxoanions from waste water was investigated. Chromium, arsenic and vanadium were the target metals, which had a strong oxidation ability and migration capability. The modified zeolite were characterised by X-ray diffraction, scanning electron microscopy, particle size analysis and evaluated for chromate Cr(VI), arsenate As(V) and vanadate V(V) removal. An uptake of chromate, arsenate and vanadate from aqueous solutions on modified zeolite has been studied. The efficiency of removal is different for Cr(VI) and As(V) and for V(V) oxoanions on to different zeolite. Sorption of metals revealed dependence on sorbent dose, time and sorbate concentrations with high sorption efficiencies at optimum operating parameters. The batch method has been employed, using metal concentrations in solution ranging from 5μg/ml to50μg/ml. The studies showed the selectivity of the different modified zeolites for the different metal oxoanions. At the same time modified zeolites hold great potential to remove oxoanions heavy metal species from waste water.
A Comparative Survey of Meta Cognition and Academic Motivation in Urban and Rural Girl and Boy Students

The aim of this research was to do a comparative survey of met cognition and academic motivation in urban and rural students, in interaction with gender. So, 400 students were selected from high schools in Sabzevar townships by a multi-stage cluster sampling. The subjects were 196 urban and 204 rural students, comprising 210 girls and 190 boys. The instrument for studying met cognition in this study was Met cognition Awareness Inventory (MAI). This questionnaire has 2 main dimensions: Knowledge Met cognition and Regulative Met cognition. Another instrument which we study academic motivation by was Inventory of School Motivation (ISM). This questionnaire has 3 cardinal dimensions: Intrinsic Motivation, Extrinsic Motivation and Self-concept. According to the achieved results the urban students were higher than rural students in the whole met cognition scale and in the dimension of knowledge met cognition; and also from the viewpoint of academic motivation, the urban students were the higher group in the dimension of external motivation. Furthermore, for the gender difference in met cognition girls did better than boys in the modality of regulative met cognition. And also in the questionnaire of academic motivation, girls were of higher motivation in all dimensions except for the dimension of self-concept. The interaction effect between gender and the place of residence was examined for each of dimensions of met cognition and academic motivation by using the two-way ANOVA test that was not significant.
Credit Card Fraud Detection Techniques: A Review

: The prediction analysis is the approach which can predict future possibilities on the current data. When the physical-card based purchasing technique is applied, the card is given by the cardholder to the merchant so that a successful payment method can be performed. The fraudulent transactions are conducted by the attacker by stealing the credit card. When the loss of the card is not noticed by the cardholder, a huge loss can be faced by the credit card company. A very little amount of information is required by the attacker for conducting any fraudulent transaction in online transactions. In this research work, various credit card fraud detection techniques are reviewed in terms of certain parameters.
A Survey on Leach Based Protocols in Wireless Sensor Network

— Wireless sensor networks (WSNs) consist of various homogeneous or heterogeneous sensor nodes deployed over an area. The most challenging issue in wireless sensor network research is its routing protocols as the sensors have limited power so protocols should be like it consumes less energy during active mode. To increase lifetime of a sensor network is to design protocols in such a way that it makes less use of battery, hence increment the lifetime of network. LEACH is the most commonly used energy efficient cluster based protocol. In this survey paper we represent LEACH based routing protocols with phases, operation and detailed algorithm used by LEACH. In this paper we also represent how to manage issues and on WSN. This paper also surveys advancements done in LEACH to improve its performance.
In Silico Modeling and Validation of L-Arginase, an Anti-Cancer Enzyme using Computational Tools

- The microbial L-Arginase is a potent drug which can be used for the treatment of various forms of cancer and other severe complications. In the present work L-Arginase sequence of a bacterium was obtained from UniProt server and it is analyzed in SWISS MODEL to develop its 3-D model. The L-Arginase enzyme model was verified and validated in ProSA web server. Further the efficiency of L-Arginase can be enhanced by using sophisticated computational tools and such L-Arginase with enhanced activity can be produced by microbes in the laboratory.