Sunila Godara ¹, Rishipal Singh ¹, Sanjeev Kumar ¹

Affiliations
1 Department of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology, Hisar - 125001, Haryana, IN

Abstract

Background/Objectives: Medical Decision Support System (MDSS) is a diagnostic interface which provides computer assisted information retrieval as well as may support excellence decision making, to stay away from human error. Even if human decision-making is frequently most advantageous, but it is poor when there are vast amounts of data to be classified. Also capability and accuracy of decisions will decrease when humans are set into pressure and massive work. Forever there is a need and scope for a better MDSS. Methods/Statistical Analysis: Cluster analysis is a method of grouping of objects keen on different groups, has proved to be a valuable tool for identifying co-expressed genes, biologically related groupings of genes and patterns. K-means, Hierarchical and Fuzzy c-means are various clustering techniques have been employed to work as core part of MDSS. Findings: Proposed Weighted Class Based Clustering (WCBC) method is dependent on classifying properties of medical data itself. Weights are calculated on the basis of class value consequently increases separability by placing more number of instances of same class in same cluster. In this paper, the clustering algorithms K-means, Hierarchical, Fuzzy and Weighted Class based K-Means are examined for medical domains. Our finding is that on medical domains the Proposed Weighted Class Based Clustering outperforms others. Application/Improvements: The application of Proposed Weighted Class Based Clustering on medical datasets gave an insight into predictive ability of Machine Learning in medical diagnosis and there is a wide liberty that proposed approach can be used in RBF Neural Network for center calculation and data base Kernel Learning which is open area of research these days.

Keywords

Clustering, Fuzzy, Hierarchical, K-Means, MDSS, Weighted Class Based Clustering.

Full Text

   

Nitin Mishra ¹, Murari Kumar ², Sanjeev Kumar ¹

Affiliations
1 Department of Civil Engineering, GEU, 566/6, Bell Road, Society Area, Clement Town, Dehradun - 248002, Uttarakhand, IN
2 School of Civil Engineering, LPU, Jalandhar-Delhi, G.T. Road, National Highway 1, Jalandhar, Phagwara - 144411,Punjab, IN

Abstract

Objective: To identify trend in annual precipitation time series using the M-K and Sen’s T-tests. Methods/Analysis: Climate change has disrupted the major climatic parameters at a global level. However, there is no equal change for all regions and have localized intensity especially in India. These changes should be identified nearby to manage the natural water resources. One of the most important climatic parameters is precipitation. As a starting point towards the apprehension of global climate change precipitation has been widely measured. The main objective of this study is to analyze the temporal variability of precipitation for the period 110 years, to enhance the hydrological status of the Uttarakhand districts of Kumaon region. The aim is to identify trend in annual precipitation time series using the M-K and Sen’s T tests. Sen’s estimator method has been used to estimate the extent of trend in precipitation. Before applying the M-K test for the trend in precipitation auto correlation effect is reduced. Finding: The analysis of M-K test shows non-significance increasing (positive) trend on annual basis. These areas experience a heavier rainfall for duration of shorter splash, which leads to very less scope for groundwater recharge and more runoff in these areas. Thus, these findings give a broad overview of the regional rainfall behavior in the study area. Applications/Improvements: The similar study can be carried out for other places as well with more locations for more diversity in the results attributing to the surroundings etc. to get a more clear and precise view about the trend in annual precipitation.

Keywords

Mann-Kendall Tests, Non-Parametric Tests and Auto Correlation, Precipitation, Sen’s Estimator Tests.

Full Text

   

Sanjeev Kumar ¹, Preeti Chaurasia ²

Affiliations
1 School of Agriculture, Lovely Professional University, Jalandhar - 144411, Punjab, IN
2 Faculty of Agriculture and Forestry, Mewar University, Chittorgarh - 312901, Rajasthan, IN

Abstract

Background/Objectives: Arbuscular Mycorrhizae (AMF) form a symbiotic association with more than 80% terrestrial plants. It is not only important for natural ecosystems, but also for sustainable agricultural production under managed ecosystem. Disturbances produced in managed ecosystem influence mycorrhizal abundance and species composition. Methods/Statistical Analysis: The present study addresses to the constraints of recording abundance and identities, as determined from morphological characters of AM spores, biomarker (fatty acids methyl ester) and largely by ribosomal sequence marker. In order to overcome of these restrictions, the present method is the combined approach based on molecular, biochemical and morphological analysis. Findings: Integrated methodology shift towards holistic methods for define AM biodiversity in agriculture system. Our finding suggested suitable alternative molecular markers for identification of AM fungi up to lower taxon level. Current investigation also identifies the selection pressures that effect AMF diversity in agricultural land and look into induction of AMF diversity by different management practices under sustainable development. Applications/Improvements: Results forecast biodiversity study of AM fungi in complex habitat based on integration of molecular, morphological and biochemical approach. These integrated systematic and ecological researches look forward to the development of powerful and cost effective techniques for AM ecotype selection from complex environmental soil.

Keywords

Diversity, Mycorrhiza, Molecular Markers, Ribosomal DNA, Rhizophagus.

Full Text