Sliding Window Control Based High Utility Pattern Mining For Industrial Use
Value of the item set is considered as an utility of that item set and find out the high utility of the item set is the aim of utility mining .Some time database parameters are considered to find out high utility pattern eg. Profit, cost etc. In day to day life high utility pattern mining play important role in applications. Its current hot topic in today’s research area. Different existing algorithms are present in this area .It first of all identify the candidate itemsets by using their utilities, and simultaneously identify the exact utility of that candidate pattern. Problem of using this algorithm is large number of candidate itemsets are generated. But after computing exact utility it’s clear that most of the candidate having no high utility. For generating profitable product manufacturing plan its very important to understanding the customer preferences in industrial area. One of the approach in pattern mining, by considering the quantity, quality and cost of each product for generating high profitable product set, which employed to find out high utility pattern. For establishing highly profitable manufacturing plan which allow corporation to maximize its revenue, high utility pattern mining is important aspect. Large amount of stream data related to customer purchase behavior used for establishing manufacturing plan. Recent preference of the customers also helps in generating manufacturing plans. This survey work contains a list structure and a novel algorithm for generating high utility pattern over large data, on the basis of Sliding Window Control Mode. This approach avoid the generation of candidate pattern. Due to that algorithm not required large amount of memory space as well as computational resources for verifying candidate patterns. Due to this it’s very efficient approach.
Published by: Swapnali Londhe, Rupesh Mahajan
Author: Swapnali Londhe
Paper ID: V4I1-1170
Paper Status: published
Published: January 8, 2018
Consumer’s Acceptance to Green Building Concept for Sustainable Construction in India
The changing consumer demands for eco-friendly products and services in all industrial sectors have paved way for constant growth and development of the real estate sector, especially in Delhi-NCR. The growing interest towards green products in real estate can be attributed to the fact that quality of air is decreasing day by day. According to few reports, buildings consume more than 30% of energy utilizing 40% of resources while simultaneously generating 40% of wastes and 35% of harmful greenhouse gases. So this study is focused on consumer’s acceptance and willingness to pay more for green building products in Delhi-NCR. For the purpose of the study 150 commercial property owners were contacted to identify the factors stimulating them to pay premium prices for sustainable buildings over conventional buildings. From the results, it was found that environmental attitude, green awareness, architectural factors and social influence significantly influence the customer willingness to pay higher prices for green buildings. Social Influence is the strongest factor that has positively influenced the customer inner will to pay premium prices for green buildings. The research also provides an in-depth understanding of factors stimulating customers to go green in the context of construction industry specifically in Delhi-NCR region.
Published by: Ankit Kumar Jat, Prithviraj Dilip Mane
Author: Ankit Kumar Jat
Paper ID: V4I1-1163
Paper Status: published
Published: January 6, 2018
Analysis and Design of Multistorey Building G+4
A multi-storey is a building that has multiple floors above the ground. It can be a residential or commercial building. in this project the analysis and design of multi-storey building G+4. In general the analysis of multi-storey is elaborate and rigorous because those are statically indeterminate structures. Shears and moments due to different loading conditions are determined by many methods such as portal method, moment distribution method and matrix method. The present project deals with the analysis of a G+4 building. The dead load & live loads are applied and the design for beams, columns, footing is obtained Manually. The Analysis part of the structure is done using Kani’s Method and the values are taken for design.
Published by: Mohd Zohair, K. Mounika Reddy
Author: Mohd Zohair
Paper ID: V4I1-1153
Paper Status: published
Published: January 6, 2018
Energy Aware Node Mobility Prediction in Mobile Adhoc Networks
The analysis of human location histories is currently getting an increasing attention, due to the widespread usage of geopositioning technologies such as the GPS, and as well as online location-based services that allow users to share this information. Tasks such as the prediction of person’s movement can be addressed through the usage of this data, in order for offering support for more privileged applications, such as adaptive mobile services through proactive context-based functions. Here we aim to develop a simple and effective scheme to predict when the user will leave the current location and where he will move to future position. This paper presents a hybrid method for predicting human mobility on the basis of Mobility Markov Chain Models (MMCs). The proposed approach clusters location histories according to their characteristics, and later trains the MMC model based on mobility history to obtain transition matrix. The usage of MMC allows us to take information of location characteristics as parameters, and also to account for the effects of each individual’s previous actions. The proposed system is a mobility prediction with adaptive duty cycling approach in reducing energy consumption, with a mobility model called Mobility Markov Chain (MMC) for predicting the future location. We report a series of experiments with a real-world location history dataset and from the LifeMap dataset, showing that the prediction accuracy is in the range of 65 to 85 percent.
Published by: Ram S. Kale
Author: Ram S. Kale
Paper ID: V4I1-1165
Paper Status: published
Published: January 6, 2018
A Literature Survey on Smart Home Automation Security
This paper presents a detailed description of different technologies and home automation systems from a security point of view. This paper highlights various security flaws in existing home automation systems and how the concept of security and the meaning of the word “intruder” have evolved over time. We studied the challenges in home automation security from point of view of home owner and security provider. This work considers home automation systems like central controller based home automation systems, context-aware home automation systems, Bluetooth-based home automation systems, Short Messaging Service-based home automation systems, Global System for Mobile communication or mobile-based home automation systems, and Internet-based home automation systems. The work is concluded by giving future directions home automation Security Research.
Published by: Rohit Ragmahale
Author: Rohit Ragmahale
Paper ID: V4I1-1160
Paper Status: published
Published: January 5, 2018
Automatic Feature Selection from EHR & DNN Modeling
Recently there are a lot of advancements in healthcare technology. Amongst, Electronic Health Record (EHR) is an upcoming trend which stores patients’ demographics, lab tests & results, medical history, habits etc. collaborated in electronic form. EHR is huge data, which is difficult to maintain and retrieve. So the idea of health risk prediction is formulated in this work. To get the relevant data from EHR, feature selection technique is used. Feature selection is responsible to collect only important and needed data from the dataset. For feature selection regression method is used in which loss function is proposed due to which accuracy and performance of the model are increased. Further risk prediction is done using neural network model. Deep Neural Network (DNN) is best suited for pattern learning and prediction purpose. It consists of various layers which have their specific function. DNN uses transfer learning to avoid repeated training for the whole system. Dataset considered here is of hypertension. EHR data is also synthetically created for analysis.
Published by: Shreyal Gajare, Shilpa Sonawani
Author: Shreyal Gajare
Paper ID: V4I1-1159
Paper Status: published
Published: January 5, 2018
