Abstract

This paper reports the development of an IR learning remote which when attached to occupancy sensors can switch off air conditioners during unoccupied durations and thereby save energy. This IR learning remote acts as an interface between occupancy sensor and AC by transmitting the control signal of occupancy sensor to the AC, by sending appropriate IR command. Generally, window and split air conditioners cannot be connected to BMS and regular occupancy sensors with relays designed for smaller loads. Such ACs’ can be controlled by occupancy sensors with the help of the IR remote developed in this work. As this system does not need any wiring and uses IR signals to control AC, the system can also be used for retrofit applications. This paper describes the development of the IR learning remote and the energy savings observed during the experiment. Further, energy simulations performed using EnergyPlus show that for a typical office with two hours of unoccupied duration during 8.5 office hours in a day, savings of 20% in air conditioning and 23.5% in lighting can be achieved in composite climate by switching OFF the AC and lights when the room is unoccupied. The percentage savings in air conditioning energy consumption is less than that of lighting even when both the AC and the lights are switched off for the same duration is due to the fact that AC has to remove the heat that comes into the space during the unoccupied duration. Simple pay back of this device is about two months.

Abstract

The method of calibrated simulation has been used to estimate the energy savings by application of cool roof for the building of Satyam Learning Centre (SLC) at Hyderabad

Abstract

To improve the performance of agricultural education, research efforts are being made to exploit to developan education methodology to impart practical skills to agricultural graduates regarding aforesaid farming situationproblems in an effective and efficient manner. In this paper, we explain the background and the overview of theproposed ICT-based framework to impart applied education to agriculture professionals.

Abstract

Recently, an approach has been proposed in the literatureto extract frequent patterns which occur periodically. Inthis paper, we have proposed an approach to extract rareperiodic-frequent patterns. Normally, the singleminsupbased frequent pattern mining approaches like Apriori andFP-growth suffer from “rare item problem”. That is, at highminsu p, frequent patterns consisting of rare items will bemissed, and at lowminsu p, number of frequent patternsexplode. In the literature, efforts have been made to extractrare frequent patterns under “multiple minimum supportframework”. It was observed that the periodic-frequentpattern mining approach also suffers from the “rare itemproblem”. In this paper, we have extended “multiple mini-mum support framework” to extract rare periodic-frequentpatterns and developed a new algorithm to extract rareperiodic-frequent patterns. Experiment results show thatthe proposed approach is efficient.

Abstract

Information systems receive data through theuser interface forms. Improperly designed user interface formincreases navigational burden which in turn increases data-entry cost and data-entry errors. Research efforts are goingon to investigate the improved methods for user interfacedesign. Normally, context-based approach is followed to designuser interfaces. In this approach, the input attributes aredivided into several contexts based on the functionality anduser interface form is designed for each context. It can beobserved that, in some information systems, some attributesreceive values only during certain time duration. We termthis property as temporality of the attribute. In this paper,we have proposed an approach to exploit the temporality ofattributes for identifying the active-contexts to design improveduser interface. The implementation results on a real systemshow that the proposed method reduces the navigationalburden significantly as compared to the existing context-basedapproach

Abstract

The eSaguTMsystem is an IT-based personalized agro advisory system. In this system, the agricultural experts generate the advice by using the latest information about the crop situation received in the form of both photographs and text. The agricultural expert advice is delivered to each farm on a regular basis (typically once in a week/two weeks depending on the type of crop) from the sowing stage to the harvesting stage to the farmer without farmer asking a question. Since 2004, the eSagu system is being developed by operating on several crops and farms in Andhra Pradesh state of India. It has been found that the agriculture expert can prepare the expert advice in an efficient manner based on the crop photographs and the related information. The impact results show that the expert advices helped the farmers to achieve significant savings in the capital investment and improvement in the yield. Encouraged by the results of eSagu experiment, a country-wide integrated agri-service program is planned by Media Lab Asia to provide expert advice and other agri-related services to Indian farming community. In this paper we briefly explain the development of eSagu system, advantages and future plan.

Abstract

In the E-commerce environment, a customer faces several difficul-ties for selecting the product. The technology like recommendationsystem is being developed to improve the performance of productselection. In this paper, we have investigated the problem of ‘se-lecting a product from group of similar products’ faced by the cus-tomer. For example, when a customer wants to buy a Sony camerathrough E-commerce Web site, the customer has to go through theinformation of several Sony camera models to select the appropri-ate one. In this paper, we have proposed an improved approachto help the customers to select the appropriate product. We haveexploited the fact that every product possesses some specialness,which is exhibited through few special features. The proposed ap-proach identifies the special features and organizes the features ofthe product in an effective manner. We have conducted the experi-ments on three real world data-sets related to Nokia-mobile phones,Sony-cameras and HP-laptops. The results indicate that the pro-posed approach has a potential to improve the performance of theproduct selection.

Abstract

Despite the growing importance of other sectors, agriculture continues to hold the key to the growth of GDP, food security, employment and income in India. Over a period of time, agriculture is undergoing a change towards high value crops, livestock, fisheries etc. Along with it substantial changes have occurred in seed varieties, use of chemical inputs, cultivation practices. Such a process has also culminated in distress conditions in the farm sector. Studies1 undertaken in the states of Andhra Pradesh, Karnataka, Maharastra and Punjab have revealed the serious crisis among the farming community. It is pertinent to note that net income is negative in some states( Narayan Moorthy: 2006) and nearly two thirds of the farmers are frustrated with their profession ( Desh

Abstract

For clustering, cosine similarity method is apopular approach to compute the similarity between twodocument vectors where each document vector consists ofweighted terms. It can be noted that, the value of theweight assigned to the terms influences the similarity value.Generally, the weight for each term is calculated by using thepopular TF-IDF method. In the TF-IDF method, the weightof a term is a function of its frequency in the document andin overall document collection. However, it can be observedthat, TF-IDF weighting method ignores the semantic asso-ciation between the terms. In this paper, we have proposedan improved term weighting method calledBoostWeightbyconsidering the semantic association between the terms. Forthis, we exploit the generalization ability of hierarchicalknowledge repositories such as “Open Web Directory” tosemantically associate the terms. The proposedBoostWeightmethod increases the weight of terms if they have commongeneralized term. The clustering experiments on the realdata-set show thatBoostWeightimproves both entropy andpurity values over traditional term weighting approaches.

Abstract

In this paper we have proposed an improved ap-proach to extract rare association rules. Rare association rulesare the association rules containing rare items. Rare items are lessfrequent items. For extracting rare itemsets, the single minimumsupport (minsup) based approaches like Apriori approach sufferfrom “rare item problem” dilemma. At high minsup value, rareitemsets are missed, and at low minsup value, the number offrequent itemsets explodes. To extract rare itemsets, an effort hasbeen made in the literature in which minsup of each item is fixedequal to the percentage of its support. Even though this approachimproves the performance over single minsup based approaches,it still suffers from “rare item problem” dilemma. If minsup forthe item is fixed by setting the percentage value high, the rareitemsets are missed as the minsup for the rare items becomes closeto their support, and if minsup for the item is fixed by setting thepercentage value low, the number of frequent itemsets explodes.In this paper, we propose an improved approach in which minsupis fixed for each item based on the notion of “support difference”.The proposed approach assigns appropriate minsup values forfrequent as well as rare items based on their item supportsand reduces both “rule missing” and “rule explosion” problems.Experimental results on both synthetic and real world datasetsshow that the proposed approach improves performance overexisting approaches by minimizing the explosion of number offrequent itemsets involving frequent items and without missingthe frequent itemsets involving rare items.