Sir Padampat Singhania University

About: Sir Padampat Singhania University is a education organization based out in Udaipur, India. It is known for research contribution in the topics: Encryption & Diesel fuel. The organization has 124 authors who have published 228 publications receiving 2066 citations. The organization is also known as: SPSU.

Novel Work of Diagnosis of Liver Cancer Using Tree Classifier on Liver Cancer Dataset (BUPA Liver Disorder)

Abstract: The classification plays a vital role towards diagnosis of liver cancer because still diagnosis of liver cancer is tedious job in early stages and late stage it is incurable. In this paper, BUPA liver disorder has been used and the Tree classifier used result is analyzed into WEKA Tool. LMT, J48, Random Forest, REP tree, Extra tree, Simple cart algorithms are have been utilized to investigate towards performance (accuracy, precision and recall) and error evaluation (Mean absolute error, Root mean squared error, Relative absolute error, Root relative squared error) performed.

Thermal analysis of energy efficient clock gated arithmetic logic unit on FPGA

Abstract: This paper, deals with Thermal Analysis of Energy Efficient Clock Gated Arithmetic Logic Unit on FPGA for reduction of leakage power and total power consumption and it has been analyzed that there is reduction in Leakage Power when ambient temperature decreases from 50 degree Celsius to 20 degree Celsius. Virtex-6 is 40-nm FPGA, on which Thermal Analysis of Energy Efficient Clock Gated Arithmetic Logic Unit has been analyzed with device operating frequency is 1GHz. This reduction in leakage power is analyzed on arithmetic and logic unit with latch free clock gating technique and on arithmetic and logic unit with latch based clock gating technique. There is significant Leakage power reduction by varying ambient temperature. With Latch free clock gating technique in Low Power Arithmetic and Logic Unit the Contribution of leakage power was 1.327W, when Ambient Temperature is 50 degree Celsius, which is further reduced to 1.246W at 40 degree Celsius Ambient Temperature, 1.176W at 30 degree Celsius Ambient Temperature, 1.114W at 20 degree Celsius Ambient Temperature. With Latch based clock gating technique in Low Power Arithmetic and Logic Unit the Contribution of leakage power was 1.328W When Ambient Temperature is 50 degree Celsius, which is further reduced to 1.247W at 40 degree Celsius Ambient Temperature, 1.177W at 30 degree Celsius Ambient Temperature, 1.115W at 30 degree Celsius Ambient Temperature. So, there is reduction in leakage power in energy efficient arithmetic and logic unit.

Improving the Liveability of Cities: The Role of Solar Energy in Urban and Peri-urban Areas

Abstract: Solar energy utilisation is the most important energy resource for bridging the gap between demand and supply of various energy needs in urban and peri-urban areas. The energy consumption is basically in terms of electricity for many appliances and equipment in homes, thermal energy for heating and cooling in homes and passive solar architecture for energy efficient buildings. On the other hand, the conventional energy consumption also induces the ecological imbalance such as the generation of greenhouse gases. Therefore solar energy may be considered an environmentally friendly alternative energy source for sustainable development. In this chapter, different active and passive solar energy harnessing techniques have been discussed, analysed and recommended leading to zero energy buildings (ZEBs) in urban and peri-urban areas. Here the study of solar energy applications for all types of energy needs in a residential building for advanced, ecological and smart liveability is presented. In this Chapter, we suggest some effective ways to harvest solar energy in urban and peri-urban areas using active and passive solar techniques.

Effects of Partial Dependency of Features and Feature Selection Procedure Over the Plant Leaf Image Classification

Abstract: The process of taxonomic classification of plant species has been carried out by the botanist since centuries, by observing their roots, shoots and flowers. It is the age of modernization; roads, buildings and bridges are fast replacing the vegetation, even before the botanist might personally get a chance to look at them. Therefore, the role of computer vision is justified for fast classification of plant species before they become extinct. The sole purpose of this research work is to increase the predictive classification accuracy of plant species by using their shape and texture features obtained from the digital leaf images of dorsal sides. Since the geometrical shape features of the leaves alone are not able to provide better predictive classification accuracy results, therefore, the texture features have been clubbed together to achieve higher order of accuracy results. This leads to increase in the data size. Therefore, in order to reduce the feature dataset, random feature selection procedure has been adopted, which selects features on the basis of weights of attributes. The justifiability of the features selected has been carried out by using the feature importance plots and strengthened further by the partial dependency plots having been drawn to see their final inclusion into the final feature selection dataset. The results exhibited by the shape, shape subset, texture, texture subset of feature dataset as well as the combined dataset are quite exemplary and worth showcasing. In spite of the fact that the geometrical shapes of many of the leaves may be the same or almost same, the combined shape and texture features can be a suitable alternative for improvement in predictive accuracy results.

Business Planning in the Light of Neuro-fuzzy and Predictive Forecasting

Abstract: In this paper we have pointed out gain sensing on forecast based techniques.We have cited an idea of neural based gain forecasting. Testing of sequence of gain pattern is also verifies using statsistical analysis of fuzzy value assignment. The paper also suggests realization of stable gain condition using K-Means clustering of data mining. A new concept of 3D based gain sensing has been pointed out. The paper also reveals what type of trend analysis can be observed for probabilistic gain prediction.