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

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

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

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

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

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

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

We describe improved mechanisms to accurately clas-sify days when news for topics receive unexpectedly high amount of coverage. We further investigate the factors which influence this classification using ‘Pres-idential Elections’ as the topic of interest. This help sin bringing out useful trends and relations between days with hot topics by varying variables like history window size,van-ratio etc. We also propose a statisti-cal scheme to approximate major events related to the topic. We then try to approximate the chain of event srelated to the major events. This can support a news alert service and also serve the purpose of automati-cally tracking news which follow up major events.

Abstract

Here we apply the graph-theoretic concept of Betweenness centrality to a class of protein repeats, e.g., Armadillo (ARM) and HEAT. The Betweenness of a node represents how often a node is traversed on the shortest path between all pairs of nodes i, j in the network and thus gives the contribution of each node in the network. These repeats are not easily detectable at the sequence level because of low conservation between independent repeated units, e.g., HEAT repeats are known to have less than 13% identity. Their identification at the structure level typically involves self structure-structure comparison, which can be computationally very intensive. Our analysis of a set of proteins from ARM and HEAT repeat family shows that the repeat regions exhibit similar connectivity patterns for the repeating units. Since it is generally accepted that in many networks, the larger the degree of a node, the larger the chance that many of the shortest paths will pass through this node, computing vertex Betweenness provides a simple and elegant approach for identifying tandem structural repeats in proteins.

Abstract

Here we present a simple method based on graph spectral properties to automatically partition multi-domain proteins into individual domains. The identification of structural domains in proteins is based on the assumption that the interactions between the amino acids are higher within a domain than across the domains. These interactions and the topological details of protein structures can be effectively captured by the protein contact graph, constructed by considering each amino acid as a node with an edge drawn between two nodes if the Cα atoms of the amino acids are within 7Å. Here we show that Newman’s community detection approach in social networks can be used to identify domains in protein structures. We have implemented this approach on protein contact networks and analyze the eigenvectors of the largest eigenvalue of modularity matrix, which is a modified form of the Adjacency matrix, using a quality function called “modularity” to identify optimal divisions of the network into domains. The proposed approach works even when the domains are formed with amino acids not occurring sequentially along the polypeptide chain and no a priori information regarding the number of nodes is required.