Production, Marketing and Value Chain Analysis of Guava in Allahabad District of Uttar Pradesh, India

Allahabad region is best known for the guava. The study aims to observe the total production of guava under different size holding for that the farmers were divided into three size groups small, medium and large. The average total yield in different size groups was calculated. It was found that there were two main channels prevailing in the District. There were lots of problems involved in the VCA of guava viz. absence of producer\u27s association, guava wilt, long chain of middlemen and absence of VCA intelligence and financ

Performance comparison of blind and non-blind channel equalizers using artificial neural networks

In digital communication systems, multipath propagation induces Inter Symbol Interference (ISI). To reduce the effect of ISI different channel equalization algorithms are used. Complex equalization algorithms allow for achieving the best performance but they do not meet the requirements for implementation of real-time detection at low complexity, thus limiting their application. In this paper, we present different blind and non-blind equalization structures based on Artificial Neural Networks (ANNs) and, also, we analyze their complexity versus performance. Since the activation function at the output layer depends on the cost function with respect to the input, in the present work we use mean squared error as loss function for the output layer. The simulated network is based on multilayer feedforward perceptron ANN, which is trained by utilizing the error back-propagation algorithm. The weights of the network are updated in accordance with training of the network to improve the convergence speed. Simulation results demonstrate that the implementation of equalizers using ANN provides an upper hand over the performance and computational complexity with respect to conventional methods

CHALLENGES IN QUALITY IMPROVEMENT OF ENGINEERING EDUCATION IN INDIA

In India, Engineering Education is being implemented under Ministry of Human Resource Development. Factors contributing for the quality of Engineering education are mainly - financial constraints, trained technical teachers, lack of standardization, lack of practical exposure, lack of priority and other factors. The research discusses the initiatives undertaken for quality assurance in engineering education. It identified the need for development and training of technical teachers in imparting knowledge based teaching and learning, redesigning suitable curriculum, use of multiple teaching resources, introducing ICT added teaching and competency based assessment along with others. The rising need for Industry Institute collaboration emerges out for identification of manpower requirement, identification courses, sector-wise skill profile and development of competency based curricula and learning material, competency based training, assessment & joint certification

Identifying and Analyzing Reduplication Multiword Expressions in Hindi Text Using Machine Learning

The task of identifying and analyzing Reduplication Multiword Expressions (RMWEs) in Natural Language Processing (NLP) involves extracting repeated words from various text forms and classifying them into Onomatopoeic, non-Onomatopoeic, partial, or semantic types. With the increasing use of low-resource languages in news, opinions, comments, hashtags, reviews, posts, and journals, this study proposes a machine learning-based RMWE identification method for Hindi text. The method employs linguistic patterns and statistical data, along with a proposed threshold boundary detection in statistical filtering. The Jaccard distance of dissimilarity and Sorensen Dice Coefficient of Similarity are used for semantic relation analysis. The proposed approach was evaluated using the publicly available Hindi corpus from IITB, measuring performance between two consecutive thresholds with the lowest error and highest recall. This study proposes an effective method for Indian computational linguistics, with experimental results highlighting its viability and utility, and providing a blueprint for current procedures.publishedVersio

Renewable Energy Production from Lantana Camara Biomass

With consumption of about 3 of the world s total energy per annum India is the world s sixth largest energy consumer Exploring renewable energy sources has a strong worldwide interest in the meeting the energy needs of fast growing population both for environmental reasons release of pollutants and fossil reserves depletion and economical ones Most of the population in India does not have access to reliable energy India has millions of tonnes of unwanted available biomass often burnt inefficiently in open fields causing air pollution However this waste can be turned into a completely environment-friendly source of energy World economy is dominated by technologies that rely on fossil energy petroleum coal natural gas to produce fuels power chemicals and materials While the use of conventional energy like oil coal and electricity has increased enormously in the last 25 years in ASEAN economies India still imports crude oil petroleum over 111 92 million tonnes peryear This heavy dependence on imported oil leads to economic and social uncertainties Currently there is a strong worldwide interest in the development of technologies that allow the exploitation of renewable energy sources both for environmental release of pollutants and fossil reserves depletion and economical reason

A Scalable, Lexicon Based Technique for Sentiment Analysis

Rapid increase in the volume of sentiment rich social media on the web has resulted in an increased interest among researchers regarding Sentimental Analysis and opinion mining. However, with so much social media available on the web, sentiment analysis is now considered as a big data task. Hence the conventional sentiment analysis approaches fails to efficiently handle the vast amount of sentiment data available now a days. The main focus of the research was to find such a technique that can efficiently perform sentiment analysis on big data sets. A technique that can categorize the text as positive, negative and neutral in a fast and accurate manner. In the research, sentiment analysis was performed on a large data set of tweets using Hadoop and the performance of the technique was measured in form of speed and accuracy. The experimental results shows that the technique exhibits very good efficiency in handling big sentiment data sets.Comment: 9 pages 1 figure 2 table

The impact of affirmative action policy on the employment of women in the private sector: the case of Saudi Arabia

The principal goal of Affirmative Action policy around the world is to equalise opportunities and to increase the share of employment for minority groups and females. The Saudi Arabian Affirmative Action is no exception; since 1985, it has been a crucial means of ensuring ‘fair participation’ in employment for Saudi citizens. This policy, known as ‘Saudisation’, aims to tackle labour market issues by replacing the high volume of foreign workers with Saudi nationals. However, one of the major criticisms of the policy has been its failure to increase the number of females in private sector employment. Women still comprise a very low proportion of this sector, compared to men. Nevertheless, some argue that the active role of ‘Saudisation’ has brought some positive changes, as some trends show that women’s share of employment in certain sectors, i.e. banking, has been rising significantly. Nevertheless, the lack of comprehensive data required to analyse the impact of such a policy on women’s employment has made it difficult to determine the extent of its effect. The role of Affirmative Action (Saudisation) in female employment in the private sector in past decades is at the core of the investigation in this study. Unlike former studies on this subject, this research attempts to investigate the long term effect of the policy by applying an event study uniquely suited to a historical exploration of this issue. This method examines variations during the period 1990 to 2014, in order to identify and evaluate the effectiveness of the policy in promoting fair employment for women across the private sector. This will be the first study to document broadly and comprehensively the long-term effect of Saudisation policy on the employment of females in the private sector