Covariate conscious approach for Gait recognition based upon Zernike moment invariants

Gait recognition i.e. identification of an individual from his/her walking pattern is an emerging field. While existing gait recognition techniques perform satisfactorily in normal walking conditions, there performance tend to suffer drastically with variations in clothing and carrying conditions. In this work, we propose a novel covariate cognizant framework to deal with the presence of such covariates. We describe gait motion by forming a single 2D spatio-temporal template from video sequence, called Average Energy Silhouette image (AESI). Zernike moment invariants (ZMIs) are then computed to screen the parts of AESI infected with covariates. Following this, features are extracted from Spatial Distribution of Oriented Gradients (SDOGs) and novel Mean of Directional Pixels (MDPs) methods. The obtained features are fused together to form the final well-endowed feature set. Experimental evaluation of the proposed framework on three publicly available datasets i.e. CASIA dataset B, OU-ISIR Treadmill dataset B and USF Human-ID challenge dataset with recently published gait recognition approaches, prove its superior performance.Comment: 11 page

Methodologies of Legacy Clinical Decision Support System -A Review

Information technology playing a prominent role in the field of medical by incorporating the Clinical Decision Support System(CDSS) in their routine practices. CDSS is a computer based interactive program to assist the physician to make the right decision at the right time. Now a day's Clinical decision support system is a dynamic research area in the field of computer, but the lack of the knowledge of the understanding as well as the functioning of the system ,make the adoption slow by the physician and patient. The literature review of this paper will focus on the overview of legacy CDSS, the kind of methodologies and classifier employed to prepare such decision support system using a non-technical approach to the physician and the strategy- makers . This study will provide the scope of understanding the clinical decision support along with the gateway to physician ,policy-makers to develop and deploy the decision support system as a healthcare service to make the quick, agile and right decision. Future direction to handle the uncertainties along with the challenges of clinical decision support system are also enlightened in this study

Proposed Web Application for Guidance and Support of Students: SmartLAD

SmartLAD is a Support Platform that connects students, mentors, and professionals of the field and enables them to share their knowledge and experience. We equip the students with a powerful network to rely on, skill-oriented courses that impart real-life skills, and experienced mentors to guide them. Our platform helps students to stay focused on their goals and keep on working hard to achieve their dreams. SmartLAD works as a Support Platform that helps students in their journey of being successful and excelling in their careers by providing them with skills, resources, guidance, and network. We help the students by providing them with a Network that the students can use without any charges and limits and can connect to people and share knowledge

A Novel Approach for Clustering Big Data based on MapReduce

Clustering is one of the most important applications of data mining. It has attracted attention of researchers in statistics and machine learning. It is used in many applications like information retrieval, image processing and social network analytics etc. It helps the user to understand the similarity and dissimilarity between objects. Cluster analysis makes the users understand complex and large data sets more clearly. There are different types of clustering algorithms analyzed by various researchers. Kmeans is the most popular partitioning based algorithm as it provides good results because of accurate calculation on numerical data. But Kmeans give good results for numerical data only. Big data is combination of numerical and categorical data. Kprototype algorithm is used to deal with numerical as well as categorical data. Kprototype combines the distance calculated from numeric and categorical data. With the growth of data due to social networking websites, business transactions, scientific calculation etc., there is vast collection of structured, semi-structured and unstructured data. So, there is need of optimization of Kprototype so that these varieties of data can be analyzed efficiently.In this work, Kprototype algorithm is implemented on MapReduce in this paper. Experiments have proved that Kprototype implemented on Mapreduce gives better performance gain on multiple nodes as compared to single node. CPU execution time and speedup are used as evaluation metrics for comparison.Intellegent splitter is proposed in this paper which splits mixed big data into numerical and categorical data. Comparison with traditional algorithms proves that proposed algorithm works better for large scale of data

Estudio del efecto moderador en una localidad encuestada sobre la intención de adopción de M-Commerce

Introduction: The present research was conducted at the University of Delhi in 2018. Problem: With the increase in usage of internet technology through wireless devices, the relevance of m-commerce has amplified. In a developing country like India, the rural and urban population is not equally divided on the use of m-commerce and this demands a detailed study regarding this problem.  Objective: The study aims to determine the factors that influence the m-commerce adoption intention of customers and how the effect varies over rural and urban populations. Methodology: This study combines the TAM and UTAUT model to consider the determinants as perceived ease of use, perceived usefulness, perceived risk, perceived cost, social interaction, and facilitating conditions, taking the endogenous variable as intention to adopt m-commerce.     Results: The results of PLS-SEM accepted the hypotheses underlying the model and also validated the moderating role played by a respondent’s locality over the intention to adopt m-commerce. Conclusion: The proposed model was validated by using PLS-SEM approach on a sample size of 200 collected from the urban and rural areas of Delhi NCR. Moreover, the moderating effect of a respondent’s locality was observed over adoption intention. Originality: With the advancement in technological infrastructure and improvement in mobile data facilities, customers have shown enthusiasm towards making online transactions using their phones. The advantage of mobile commerce over computer based electronic commerce is its mobility. Extant research has shown interest in studying the adoption intention of mobile commerce, based on determinants from the TAM or UTAUT model or their combinations. This study combines both models to choose the determinants of mobile adoption intention.  Limitation: Further studies can be conducted by considering other combinations of determinants and extending the model to incorporate the loyalty measures.Introducción: la presente investigación se realizó en la Universidad de Delhi en 2018. Problema: con el aumento en el uso de la tecnología de Internet a través de dispositivos inalámbricos, larelevancia del comercio móvil se ha ampliado. En un país en desarrollo como India, la población rural y urbana no está dividida por igual en el uso del comercio móvil y esto exige un estudio detallado sobre este problema. Objetivo: el estudio tiene como objetivo determinar los factores que influyen en la intención de adopción deM-Commerce de los clientes y cómo varía el efecto sobre las poblaciones rurales y urbanas.Metodología: este estudio combina el modelo TAM y UTAUT para considerar los determinantes como facilidad de uso percibida, utilidad percibida, riesgo percibido, costo percibido, interacción social y condiciones facilitadoras, tomando la variable endógena como intención de adoptar el comercio móvil. Resultados: los resultados de PLS-SEM aceptaron las hipótesis subyacentes al modelo y también validaronel papel moderador desempeñado por la localidad del encuestado sobre la intención de adoptar el comerciomóvil. Conclusión: el modelo propuesto fue validado utilizando el enfoque PLS-SEM en un tamaño de 200 muestras recolectadas de las áreas urbanas y rurales de Delhi NCR. Además, el efecto moderador de la localidad del encuestado se observó sobre la intención de adopción. Originalidad: con el avance en la infraestructura tecnológica y la mejora en las instalaciones de datos móviles, los clientes han mostrado entusiasmo por realizar transacciones en línea usando sus teléfonos. La ventaja del comercio móvil sobre el comercio electrónico basado en computadora es su movilidad. La investigación existente ha mostrado interés en estudiar la intención de adopción del comercio móvil, basada en determinantes de la intención de adopción móvil

Efficiency of Indian Commodity Market: A Survey of Brokers’ Perception

The present study documents the finding of a survey of brokers’ perception pertaining to the recently introduced commodity derivatives market in India. The survey results show the brokers’ assessment about trading/marketing activities and their perception of the benefits and concerns about commodity derivatives. It also throw some light on the perception of brokers about the efficiency of Indian commodity derivatives in performing the functions of price discovery, hedging effectiveness and volatility dynamics. The survey results show that high net worth individual are contributing significantly in the trade volume of commodity derivatives. Interestingly, retail investors are also emerged as the significant contributor in total turnover of brokers. Survey results exhibit that price discovery and hedging effectiveness functions are well performed by all the commodity futures except the energy commodities futures. Energy commodities, being the most volatile commodities, are perceived as having less hedging effectiveness as compared to others. Brokers are assenting on the high to moderate impact of open interest, volume and time to maturity on the volatility of the commodity futures derivatives

Offline Handwriting Recognition Using Genetic Algorithm

In this paper, a new method for offline handwriting recognition is presented. A robust algorithm for handwriting segmentation has been described here with the help of which individual characters can be segmented from a word selected from a paragraph of handwritten text image which is given as input to the module. Then each of the segmented characters are converted into column vectors of 625 values that are later fed into the advanced neural network setup that has been designed in the form of text files. The networks has been designed with quadruple layered neural network with 625 input and 26 output neurons each corresponding to a character from a-z, the outputs of all the four networks is fed into the genetic algorithm which has been developed using the concepts of correlation, with the help of this the overall network is optimized with the help of genetic algorithm thus providing us with recognized outputs with great efficiency of 71%