Miracle-dz/UniFAR-FTUs huggingface dataset:
Text Extraction from Tamil and Hindi Document Images using Open Source Optical Character Recognition tools

- Optical Character Recognition (OCR) is a technique, which is used to extract the text from document images and converted into text format. This kind of information retrieval is called as recognition based retrieval hence that it can be edited, searched, stored more efficiently. OCR is used for many applications such as library, organization, bank cheques, number plate recognition, historical book analysis and many others applications. Various OCR tools are available for converting document images in different types of languages. The primary objective of this work is to compare the performance analysis of the three different OCR tools for extracting the text information from Tamil and Hindi document images. The OCR tools considered in this analysis are Google Docs, Free Online OCR and i2OCR. Based on the conversion accuracy it is observed that the performance of Free Online OCR is better than other OCR tools.
Market Linkage of Indian Stock Market with Select Stock Markets

This paper examines the market linkage and volatility estimation of Indian stock market with selected stock markets in the world by using various econometric models from the period of 1st April 2000 to 31st March 2018. The result suggests that Indian market positively correlated with the other selected stock markets. Indian stock market has significant influence with two out of five markets in the short run. In the long run relationship, there is no linkage between markets. However, there exists bidirectional causality in the markets. Also, there is market price volatility, and the clustering effect was found in all the major markets. From the study, it is apparent that the Indian stock market has an impact on the other stock market, in the short run. Hence, investors have the opportunity to reap the benefits of the portfolio diversification by gaining knowledge from market integration. JEL Classification: C01, C22, F32, G11, G15.
Isolation, Identification And Molecular Characterization Of Shigella Spp. From Tiruchirappalli District, Tamil Nadu India

The present study aimed at isolation and identification of Shigella spp. from human stool and characterization of the samples serovars using biochemical, molecular characterization techniques. To study the molecular characteristics of 59 Shigella spp. isolated during May 2014 to April 2015 from rectal swabs of patients with watery and/or bloody diarrhoea in a new area of Tiruchirappalli district. A total of 25 samples were collected of which 16% were positive to Salmonella serovars. Members of the genus Shigella, namely S. flexneri, S. dysenteriae, S. sonnei and S. boydii have caused and continue to be responsible for mortality and/or morbidity in high risk populations such as children under five years of age Yearly episodes of shigellosis globally have been estimated to be 164.7 million and of these, 163.2 million were in developing countries and the remaining in industrialized nations. The mortality rate was approximately 0.7%. Shigellosis is usually a self-limiting infection, however when it subsides, the intestinal ulcers heal with scar tissue formation. Uncomplicated recovery is usual and the organisms rarely cause other types of infections. Adversely, in 3 to 50% of cases, depending on the virulence of the strain, the nutritional and immune status of the host, the initial infection may be followed by neurological complications or kidney failure. Serious complications do occur at greatest frequencies in malnourished infants, toddlers, older adults and immunocompromised individuals [1, 2]. Virulence genes responsible for the pathogenesis of shigellosis may be located in the chromosome or on the inv plasmid borne by the organism. Here, we describe the application of a multiplex PCR (mPCR) design for simultaneous detection of four virulence genes (set1A, set1B, ial and ipaH) in Shigella spp. and to determine the prevalence of these virulence genes in a random selection of Shigella strains.
Image Acquisition, Noise removal, Edge Detection Methods in Image Processing Using Matlab for Prawn Species Identification

Image acquisition is a very essential and primary work for any image classification. A digital image is produced by several image sensors, which besides various types of light-sensitive cameras. Depending on the type of sensor, the resulting image (prawn) data is an ordinary 2D prawn image, or an image sequence. The prawn pixel values typically correspond to light intensity in one or several spectral bands (gray images or color images), but we can also be related to various physical measures of structure, such as depth, absorption or reflectance of sonic or electromagnetic waves, or nuclear magnetic resonance. It has been studied extensively in computer vision and photogrammetric camera lens which may be used to focus light so that objects may be viewed brighter. In this paper we review the techniques proposed in the literature include those using 3D apparatus, 2D objects and lens improvement to increase the size of the prawn so that prawns in the distance can appear larger and clear by presenting various types of cameras and their comparisons. In this paper we also presenting that the image processing techniques such as gray scale conversion, noise removal, sharpening, edge detecting, image segmentation, etc., that are applied for the captured prawn image by using Matlab source code and displaying the results.
27 IJEAB-DEC-2016-2

This paper draws attention to the growing concern of climate variability affecting coffee production in India dominated by small holders. Climate related studies across coffee producing areas around the world have encapsulated the significant impacts on coffee physiology and thereupon production and yield. Specifically, choosing Kodagu district, the largest coffee growing district in India as the study area, the climate variability is assessed by analysing recorded climatic data over the last 33 years. The results revealed a marginal decrease in total rainfall and high monthly variability, rising temperatures and declining relative humidity levels that is likely to impact coffee production given the decreasing yields in the region witnessed at the macro as well as micro level. We concluded the paper by identifying adaptation and mitigation potential measures from existing and altered management practices by the coffee growers, government initiatives and alternate incomes based on interviews with coffee growers. Keywords— Climate, Coffee, IPCC.
Modified Tumour Cut Algorithms For MRI Image Segmentation of Brain Tumours

—The brain tumour segmentation methods rely on the intensity enhancement. Among them, a clustering method have been investigated and used. In this paper, CA (Cellular Automata) based seeded tumour segmentation algorithm is proposed. Which determine the Volume of Interest (VOI) and seed selection. First, establish the connection of the CA-based segmentation to the graph-cut method to show that the iterative CA framework solves the shortest path problem. This paper describe segmentation method consist of two phases. In the first phase, the MR Image is acquired from patient database and contrast enhancing the image. In the second phase, the CA algorithm run twice for background seed (healthy cell) and foreground seed (tumour cell) for probability calculation. Furthermore, apply Graph-Cut (GC) method to differentiate necrotic and enhancing tumour tissue content, which gains importance for a detailed assessment of radiation therapy response.
PSO Based Optimal PV Placement for Maximizing Penetration

Share of photo voltaic(PV) power in power system is increasing day by day. It is due to the ease in implementation and availability in small scale. Large number of researches are going on in field of PV. Most of the study suggest the implementation of PV power stations at distribution level. In this paper detailed theoretical analyses of the impact of large scale PV is analysed. The preliminary section of this paper provides literature review with specifications of IEEE-14 bus network. Maximum loading method is implemented here. A test study is done in IEEE 14 bus to find out the optimal location of SPVG and to find the maximum safe instantaneous penetration for maximum loading method using PSO. Maximum penetration is achieved by adjusting grid parameters subjected to various power system stability and security constraints. Finally it can be concluded that PSO based optimal control of large scale PV penetration allows us to utilize SPV power efficiently.
A Novel Class F Power Amplifier with Optimized PAE and Gain Using IPSO Algorithm

In this paper Improved Particle Swarm Optimization has applied to determine optimal performance of proposed structure of class F power amplifier.Due to the high increase in demand for a wide classification of applications that require low-cost, high-efficiency, and compact systems, RF power amplifiers are considered the most critical design blocks and power consuming components in wireless communication, TV transmission, radar, and RF heating. Therefore, much research has been carried out in order to improve the performance of power amplifiers. Classes-A, B, C, D, E and F are the main techniques for realizing power amplifiers. An implementation of high efficiency class-F power amplifier in 0.18 um CMOS technology is realized in this paper. The simulation and optimization of the class-F power amplifier circuit model will be presented. KeywordsPower Amplifier; Class F; PSO Algorithm; High Efficiency.
A CENTRAL COMPOSITE DESIGN APPROACH FOR QUANTIFYING LIPID NANOPARTICLES USING UV-TRANSMITTANCE

The present work reported an economic approach for SLN development using a Bath-Sonicator. In our method Transmittance (T%) measures the effects of Dextran, Tween 80, Sonication time and Lipid Concentration over formation of Particles below 300 nm. In our brute-force methodology, the modified Solvent-evaporation method has been used for nanoparticle formation. The variables have been optimised using Central Composite Design, to find best SLN formulation with highest T%. The Particle Size analyser has been used for conforming the effect of variables over T% and for supporting our determinations. The Quadratic model from response surfaces for \T%300 nm was found significant. The best SLN preparation is made with 0.5-1% Tween 80, 12 mg Dextran, 8 mg LC and 18 min Sonication time. It has maximum T%-300 nm (83.75%) respectively, with Zeta potential as 23 mV. This proposed method can be used for formulating SLNs, along with the determination of Particle size range. The method is economic, by involving a UV Spectrophotometer based approach. Results reveal that there is no insignificant difference between Particle sizes as determined using Spectrophotometer and Malvern Zetasizer.
A SKEWED SLOT SCHEDULING APPROACH FOR REAL-TIME DATA TRANSMISSION IN INDUSTRIAL WIRELESS SENSOR NETWORKS

Improving the data transmission reliability of a time-constraint monitoring and control application in industry is a challenging task. Contemporary solutions are based on either Time Division Multiple Access (TDMA) or the hybrid model of TDMA and Carrier Sense Multiple Access (CSMA). However, most of them are burdened with a complex slot allocation scheme while others are vulnerable to the time varying nature of wireless links. Therefore, we propose a skewed slot scheduling approach that maintains a global time constraint in data delivery using a CSMA-base data transmission within a big-slot. All nodes at the same tree depth collectively allocate a big-slot and share it for data transmission reliability. The new approach is not only simple in design but also highly dependable against dynamically changing network topology. The approach also maximizes data aggregation and filtering, thus improving the balancing of energy consumption among sensor nodes.
Effect of moisture content on in vitro regeneration of embryonic axis explants of cowpea (Vigna unguiculata L.)

To see the effect of ultra desiccation on seed survival, cowpea (Vigna unguiculata L.) seeds were conditioned to various moisture contents ranging from 8-2%. When monitored after one year of storage, variability was observed in seed viability in the seeds conditioned to different moistures. Seeds conditioned to very low moisture contents exhibited greater loss in viability. Therefore, efforts were made to see the possibility of germinating the embryo under artificial condition using various media and growth regulators. The effect of various combinations of hormone in plant tissue culture media were evaluated on the growth of excised embryonic axis in vitro to develop an embryo rescue protocol. An efficient protocol was developed for in vitro shoots regeneration from cowpea embryonic axis to improve the survival of low viability seeds to avoid the loss of potentially valuable genes. Shoots were developed in most of the explants on medium containing MS salts and B5 vitamins supplemented with 0.2 mg/l BAP. All the regenerated shoots were rooted on the same medium and in vitro plantlets were acclimatized in pots containing soilrite at room temperature. All the plants produced healthy seeds after 2 months in greenhouse. This protocol can be used to rescue cowpea germplasm with low germination percentage which results in successful regeneration.
Attributes Correspondence Discovery in Ontology Instance-based Matching and RDF Data Linkage using Clustering Method

One important aspect of ontology instance matching process is elements or attributes discovery. It specifies element correspondences in order to produce potential matching elements; otherwise, all elements of a class in the source ontology have to be compared with all elements of class in the target ontology. This heavy comparison is time-consuming and resulted in the poor performance of the matching system and makes the matching incomplete. Matching two or more ontologies and RDF datasets requires complete instance matching so as to establish logically equivalent relation among semantically related entities of the data sources. This deems challenging because of the existence of semantic heterogeneity and presence of irregular data in the RDF data sources which makes elements discovery and feature value extractions difficult. Thus, we proposed a four-step elements discovery method that utilizes unsupervised K-Medoids clustering algorithm in discovering potential matching elements pairs. To ensure generalization, we take unsupervised Canopy Clustering method to be the baseline for our evaluation. In terms of scalability, our method outperforms the baseline method with approximately 99% in both Pair Completeness and Reduction Ratio as against 60% and 86% respectively in the baseline. In mapping pattern generation, our method also outperforms the baseline algorithm with the overall F-Measure of ~91% against ~85%. The result of comparism with other methods justifies the significance effect of clustering attributes in the initial stage of the instance matching which can save about 50% of the comparism.
Deep Learning Based Image Recognition for Vehicle Number Information

 Abstract: A number plate contains a sign affixed to the front and rear of a vehicle for displaying its registration number. This registration number is used for the official identification purposes by the authorities. Most regular Optical Character Recognition (OCR) techniques perform well in recognizing printed characters on documents but cannot make out the registration number on the number plates. Besides, the existing approaches to number plate detection require that the vehicle is stationary and not in motion. To address these challenges to Number plate detection we make the following contributions: i. Use Neural Networks based Number plate character recognition, ii. Create a database of captured vehicle number plate’s images, iii. Provide a client-server solution for quickly getting the details of a vehicle’s ownership.
Level of Adherence to Feeding Guidelines for Infants Born to Mothers Living with HIV in Rakai District, Uganda

Introduction: There are a number of challenges to both the mother and the healthcare workers due to the risk of Human Immunodeficiency Virus (HIV) transmission as a result of infant feeding which poses a risk of HIV transmission. Proper and sufficient nutrition is crucial for child growth and development, and throughout the lifespan of the child. The focus of the Uganda national health sector is to promote health, disease prevention, early diagnosis and treatment of disease. National minimum health care package consists of Maternal and Child Health; Prevention, management, and control of communicable diseases; Prevention, management, and control of noncommunicable diseases; Health promotion and community health initiatives. Objective: The purpose of this study was to determine the level of adherence to feeding guidelines for infants born to mothers living with HIV in Rakai District, Uganda. Methodology: A descriptive cross-sectional study design was used with a sample size of 138 respondents and employing a quantitative method of inquiry. A non-probability sampling strategy called consecutive sampling was used to sample the mothers with HIV. Data were analyzed using SPSS to generate descriptive analysis data. Results: Out of 135 HIV mothers interviewed, 69 (51.1%) adhered to breastfeeding guidelines for infants born to HIV mothers. Conclusion: 5 in every 10 HIV mothers adhere to breastfeeding guidelines for infants born to HIV mothers. Recommendations: Timely re-orientation of the mothers and family about the infant feeding, proper monitoring by the health workers.
Study Of Social Maturity Among Adolescents In Relation To Parenting Styles

The present study examines the personality of adolescents in relation to parenting styles. The sample of the study comprised of 200 eleventh class students (50 boys and 50 girls) studying in government and private schools of Chandigarh. Descriptive survey method was employed to collect the data. The major findings of the study revealed an insignificant difference in the social maturity of adolescents studying in government and private schools. But, there was no significant difference in the social maturity of adolescents in relation to low and high score on parenting style.
An Efficient Document Clustering Based on HUBNESS Proportional K-Means Algorithm

Evaluating similarity between the documents is a main operation in the text processing field. Similarity measurement is used to estimate the relationship between the records or documents.In existing system similarity between two documents can be computed with respect to feature by using Similarity Measure for Text Processing (SMTP). In proposed hybrid SMTP scheme is integrated with hubness based distance analysis scheme, where the KNN search is performed to find out the nearest neighbours based on the similarity. Hubness measure estimation calculates the hubness score. Then Document Clustering performed using Hubness - Proportional K-means (HPKM) algorithm. Semantic analysis method and optimal cluster count estimation mechanism are used to improve the clustering process. Cluster accuracy will be improved by system.
Multi-Atlas Image Segmentation of Thoracic & Abdominal Anatomy

Image segmentation is a concept that describes removal of or extraction of part of the image that contains meaningful data in comparison with the whole image. The main objective of segmentation is region partitioning that are different on visual basis or property wise and/or characteristically meaningful. Accurate segmentation of human anatomy is critically fundamental as well as most time consuming task in medical planning. This is required mainly for medical diagnosis, qualitative analysis & treatment planning. Atlas based segmentation approach guides the segmentation process relying on the concept of an atlas & the target subject image. Atlas is nothing but an already pre-segmented reference image volume validated by an expert that includes structure of interests. The atlases are mapped with the image to be
Markerpixel using Watershed Transformation

— We describe a new method for the generation of superpixels which can be implemented using watershed transformation. Our method aims at the extraction process of local and global impression of a given image. We propose a gradient-based low level segmentation process inorder to fulfill the adherence property of the segmented image. We also show this as an efficient method to achieve the regularity and adherence property of the segmented image. Since we are dealing with marker controlled Watershed transformation technique, the problem of oversegmentation can be avoided to a great extreme. Here, we try to showcase ‘markerpixels’ as an efficient tool for the creation of superpixels using Watershed transformation .
An Analysis and Design of High Performance Power Consumption Double Tail Comparator using Gated Clock and Power Gating Techniques

In the modern digital world, Analog to Digital Conversion is the major role in every hardware device. Power consumption is most important parameters for concern in CMOS technology. The main requirements of the ADC are very low power consumption, low noise, less hysteresis, less offsets and high speed. Comparators are the basic elements for designing the modern analog and mixed signal systems. The speed and area is the main factors for very high speed applications. For past several years, the technology scaling is the most important procedures for the improvement performance of circuits are in terms of the power, speed etc. In this article, analysis and design of double tail comparator transistor is done in terms of power, delay and glitches. Comparator is the important circuit in the digital design and is defined in terms of power and speed, which are the most important factors of attaining the complete performance of ADCs. Several ADCs required small delay, Low power comparator with small die size. The accuracy of comparators is mainly defined with to its power consumptions and speed. The comparators are mainly achieving the overall high performance of ADC. The high speed comparators suffered from low voltage supply. Threshold voltage of the devices is not scaled at the same time, as the supply voltage of the device. In modern CMOS a technology of the double tail comparator is designed by the using dynamic method, it mainly reduces the power and voltage. The designed modified double tail comparator is simulated with using Tanner EDA tool and analysed that, it consumes the power of about 35µw with the delay of 1.14ns.
SIMILARITY MEASURE USING LINK BASED APPROACH

—Web search engines provide an efficient interface to vast information. This web search engine provides the most semantic relativity between the given words, and it will generate the semantic measures automatically, since data on the web is noisy, huge and dynamic. we propose and analyzed and visualized similarity relationships in Web data sets to identify how to integrate content and link analysis for approximating relevance.
Modeling and Control of Multilevel Inverter with Reduced Number of Power Switches

Multilevel inverters (MLI) are mostly used medium-voltage and high-power applications due to attractive features such as low total harmonic distortion (THD) in output voltage, high efficiency, less stress on power switches and low Electromagnetic Interference (EMI). But multilevel inverter requires more number of power semiconductor switches as number of levels increase that increases cost and complexity in control circuit. In this paper, a study of new structure of MLI that uses reduced number of switches as compared to conventional MLI topology is performed. The new MLI structure reduces overall loss, cost and size as compared to conventional MLI. The three phase MLI topology with reduced number of switches is simulated using MATLAB/Simulink. The switching signals are generated using In Phase Dispositioncarrier based modulation techniquefor the new MLI. The effectiveness of the three-phase MLI topology has been demonstrated through simulation results.