Placement Analysis using Data Mining

Education data mining is one of the growing fields of the present time as it grows many issues to improve system comes in the notice one of them is improvement of the placement. Placement is a very important issue for any educational organization. Every organization wants to improve its placement. Success of any educational institute is measured by the placed student of the organization. This paper actually deals with the application of neural network to the educational data to improve placement. In today’s world all organization faces one of the big problems is recruit right candidate for the suitable position, Organization ready to invest a huge amount to recruitment process but till now they failed to recruit. In this paper, we apply the data mining techniques for placement prediction. To predict the performance of a student’s is the great concern for the organization, as they seek knowledgeable, talented and qualified professionals to need to fill up their positions. According to the survey, the corporate companies spend a sum of 1800crores for choosing candidates to fill up their vacancies. The Majority of the companies recruiting the candidates via on-campus recruitment and to fill up them positions. Our method is very useful for corporate companies, consultancies. This method is the best way to get the right candidate at the right time in the corporate world. The industry gets the best talent candidates from different institutes/universities, and the students also get a chance to kick start their career with some of the best organization. But the students facing some difficulties in getting placements. To overcome the problem we apply the Improved Decision Tree classification algorithms on these data, we have predicted which students placed in Recruitment Drives. Corporate companies need only knowledgeable and skilled persons for the vacant position. To find that particularly skilled person there's a question that how can the companies identify them. In order to overcome this problem in this paper, we provide a complete solution to the recruitment process. Actual challenges appear when they will develop real-world software. Training develops confidence in whatever School, Colleges, Universities will train. But all corporate expect skilled, confidence with active persons. We are giving to find the right candidate for the right job in this development. After recruiting employees corporate feel much confident about their development. So as much as possible, clear them confusions and get new ideas about their project training and become confident about their work

Beyond factor analysis: Multidimensionality and the Parkinson’s Disease Sleep Scale-Revised

Many studies have sought to describe the relationship between sleep disturbance and cognition in Parkinson’s disease (PD). The Parkinson’s Disease Sleep Scale (PDSS) and its variants (the Parkinson’s disease Sleep Scale-Revised; PDSS-R, and the Parkinson’s Disease Sleep Scale-2; PDSS-2) quantify a range of symptoms impacting sleep in only 15 items. However, data from these scales may be problematic as included items have considerable conceptual breadth, and there may be overlap in the constructs assessed. Multidimensional measurement models, accounting for the tendency for items to measure multiple constructs, may be useful more accurately to model variance than traditional confirmatory factor analysis. In the present study, we tested the hypothesis that a multidimensional model (a bifactor model) is more appropriate than traditional factor analysis for data generated by these types of scales, using data collected using the PDSS-R as an exemplar. 166 participants diagnosed with idiopathic PD participated in this study. Using PDSS-R data, we compared three models: a unidimensional model; a 3-factor model consisting of sub-factors measuring insomnia, motor symptoms and obstructive sleep apnoea (OSA) and REM sleep behaviour disorder (RBD) symptoms; and, a confirmatory bifactor model with both a general factor and the same three sub-factors. Only the confirmatory bifactor model achieved satisfactory model fit, suggesting that PDSS-R data are multidimensional. There were differential associations between factor scores and patient characteristics, suggesting that some PDSS-R items, but not others, are influenced by mood and personality in addition to sleep symptoms. Multidimensional measurement models may also be a helpful tool in the PDSS and the PDSS-2 scales and may improve the sensitivity of these instruments

Cognitive dysfunction in insomnia phenotypes: Further evidence for different disorders

Study Objectives: To determine cognitive profiles in individuals with short sleep duration insomnia (SSDI) and normal sleep duration insomnia (NSDI; also, paradoxical insomnia), compared to healthy sleepers. Method: Polysomnographic (PSG) and neuropsychological data were analysed from 902 community-based Raine Study participants aged 22 ± 0.6 years of whom 124 met criteria for insomnia (53 with NSDI and 71 with or SSDI) and 246 were classified as healthy with normal sleep (i.e., without insomnia or other sleep disorders). Measurements of self- report (attention and memory) and laboratory-assessed (attention, episodic memory, working memory, learning, and psychomotor function) cognition and mood, and PSG-based sleep stages (% total sleep time; %TST) were compared between these 3 groups. Results: In comparison to the healthy sleeper group, both insomnia groups had poorer self-reported attention, memory, mood, and sleep, and poorer laboratory-assessed attention (inconsistency). The NSDI group had less consistent working memory reaction time than healthy-sleepers or those with SSDI. The SSDI group had more inconsistency in executive function (shifting), and showed greater %TST in stage N1 and N3, and less REM sleep than either healthy-sleepers or those with NSDI. Conclusions: Individuals with NSDI demonstrated greater working memory inconsistency, despite no laboratory assessed sleep problems, implicating early signs of pathophysiology other than disturbed sleep. Those with SSDI demonstrated different sleep architecture, poorer attention (inconsistency), and greater executive function (inconsistency) compared to healthy-sleepers and those with NSDI, implicating sleep disturbance in the disease process of this phenotype

Longitudinal association of intraindividual variability with cognitive decline and dementia: A meta-analysis

Objective: Intraindividual variability (IIV) –variance in an individuals’ cognitive performance - may be associated with subsequent cognitive decline and/or conversion to dementia in older adults. This novel measure of cognition encompasses two main operationalisations: inconsistency (IIV-I) and dispersion (IIV-D), referring to variance within or across tasks respectively. Each operationalisation can also be measured with or without covariates. This meta-analytic study explores the association between IIV and subsequent cognitive outcomes regardless of operational definitions and measurement approaches. Method: Longitudinal studies (N = 13) that have examined IIV in association with later cognitive decline and/or conversation to MCI/dementia were analysed. The effect of IIV operationalisation was explored. Additional sub group analysis of measurement approaches could not be examined due to the limited number of appropriate studies available for inclusion. Results: Meta-analytic estimates suggest IIV is associated with subsequent cognitive decline and/or conversion to MCI/dementia (r = .20 , 95% CI [.09, .31]) with no significant difference between the two operationalisations observed (Q = 3.41, p = .065). Conclusion: Cognitive IIV, including both IIV-I and IIV-D operationalisations, appears to be associated with subsequent cognitive decline and/or dementia and may offer a novel indicator of incipient dementia in both clinical and research settings

Microstructural Characterization and Mechanical Behavior of Copper Matrix Composites Reinforced by B4C and Sea Shell Powder

This paper investigates the microstructural and mechanical properties of copper metal matrix composites reinforced with B4C and crushed sea shell particles (fabricated using powder metallurgy). In powder form, copper is widely used in structural applications. Copper also possesses very good electrical and thermal conductivity, ductility, and corrosion resistance. B4C is the third-hardest-known material that also possesses excellent toughness and wear resistance. Sea shells are readily available along coastal areas. Therefore, an attempt has been made in this work to investigate the feasibility of its utilization in powder metallurgy. Two batches of samples were prepared. In the first batch, the percentage of boron carbide and copper powder were varied, and seashell powder was not included. In the second batch, the percentages of B4C, copper, and sea shell powder were varied in order to assess the change effected by the sea shell material. The sintered samples of both batches were subjected to microstructural characterization to ascertain the homogeneous distribution of the reinforcements. The microhardness and wear resistance of all of the fabricated samples were assessed. The results confirmed that the inclusion of 2% sea shell powder (by weight) with 10% boron carbide improved the wear resistance and hardness of the composite

Gestational diabetes mellitus – The modern Indian perspective

Gestational diabetes mellitus (GDM) is a serious and most frequent health complication during pregnancy which is associated with a significant increase in the risk of maternal and neonatal outcomes. GDM is usually the result of β-cell dysfunction along with chronic insulin resistance during pregnancy. Seshiah et al. pioneer work led to the adoption of Diabetes in Pregnancy Study Group in India criteria as the norm to diagnose GDM, especially in the community setting. In 2014, the Maternal Health Division of the Ministry of Health and Family Welfare, Government of India, updated guidelines and stressed upon the proper use of guidelines such as using a glucometer for self-monitoring and the use of oral hypoglycaemic agents. The 2018 Government of India guidelines stress the importance of counselling about lifestyle modifications, weight control, exercise, and family planning

Investigation of Static, Modal and Harmonic vibration analyses of Single Row SKF6205 Deep Groove Ball Bearing for thermal applications

It is the necessary to predict the endurance capability of the mechanical element with its increased application and complexity. The present research work estimates the stress variation and displacement characteristics using finite element analyses of Single Row SKF6205 Deep Groove Ball Bearing under radial and axial loadings. The vibration analyses are evaluated in three aspects; static, modal, and harmonic analysis. The simulations show the variation of stress levels of the bearing in different loads. These results are used to predict the fatigue life, wear rate, and productivity of the ball bearing at various stochastic conditions