http://localhost:8080/xmlui/handle/123456789/752 Fri, 27 Mar 2026 08:19:01 GMT 2026-03-27T08:19:01Z http://localhost:8080/xmlui/handle/123456789/782 Title: A benchmark dataset of onlinehandwritten Gurmukhi script words and numerals Authors: Singh, H; Sharma, R K; Kumar, R et al. Abstract: This paper presents an online handwritten benchmark dataset (OHWR-Gurmukhi) for Gurmukhi script. TIET, Patiala released the unconstrained online handwriting databases, OHWR-GNumerals and OHWR-GScript, which contain isolated strokes samples produced by 190 writers. The OHWR-GNumerals covers 10 stroke classes and OHWR-GScript covers 95 stroke classes to represent the Gurmukhi character set. For data collection, two data sets of Gurmukhi words have been finalized after having a consultation with language experts in order to collect the balanced stroke samples. The preprocessing methods used to prepare these datasets include: size normalization, removing duplicate points, interpolating missing points and re-sampling. The purpose of this benchmark is to create a common platform and make the benchmark dataset publically available for research endeavors in the area of online handwriting recognition. Wed, 01 Jan 2020 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/782 2020-01-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/781 Title: Leveraging energy-efficient load balancing algorithms in fog computing Authors: Singh, S P; Kumar, R; Sharma, A et. al. Abstract: Cloud computing and smart gadgets are the need of smart world these days. This often leads to latency and irregular connectivity issues in many situations. In order to overcome this issue, an emerging technique of fog computing is used for cloud and smart devices. A decentralized computing infrastructure in which all the elements, that is, storage, compute, data and the applications in use, are passed in an efficient and logical place between cloud and the data source, is called Fog computing. The cloud computing and services are generally extended by fog computing, which brings the power and advantages of data creation and data analysis at the network edge. Real-time location based services and applications with mobility support are enabled due to the physical proximity of users and high speed internet connection to the cloud. Fog computing is promoted with leveraging load balancing techniques so as to balance the load which is done in two ways, that is, static load balancing and dynamic load balancing. In this paper, different load balancing algorithms are discussed and their comparative analysis has been carried out. Round Robin load balancing is the simplest and easiest load balancing technique to be implemented in fog computing environments. The major problem of Source IP Hash load balancing algorithm is that each change can redirect to anyone with a different server, and thus, is least preferred in fog networks. The mechanisms to make energy efficient load balancing are also considered as the part of this paper. Wed, 01 Jan 2020 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/781 2020-01-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/780 Title: A swarm intelligence-based quality of service aware resource allocation for clouds Authors: Kumar, A; Sharma, A; Kumar, R Abstract: The growing popularity of cloud computing results in very large data centres around the world with vast amount of energy requirements and CO2 emissions. These large sized data centres demand efficient management of resources to conserve energy while satisfying quality of service (QoS) requirements of the end users. In this paper, a QoS-aware resource allocation approach using ant colony optimisation is proposed. The proposed approach is implemented in CloudSim and comprehensive performance analysis shows upto 12% energy saving. Wed, 01 Jan 2020 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/780 2020-01-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/779 Title: (2020) Efficient content retrieval in fog zone using Nano- Caches Authors: Sharma, A; Singh, S P; Kumar, R et al. Abstract: It is always desired to improve the response time from cloud servers, which deliver contents without buffering. As the penetration of mobile/fog devices is increasing, the limits of cellular ranges come under question. This question arises in spite of the fact that the current Internet Service Providers and data operators are adding cellular towers frequently to reduce delay and enhance performance. This performance can be improved by increasing Nano-Cache(s) at the edges of the network for forwarding interrelated contents to remote corner of the earth. In this research work, Nano-Caches are integrated for delivering contents efficiently, using search-based optimization techniques, which are energy and response aware in nature. An algorithm, namely, Modified Teaching Learning-Based Optimization(MTLBO), is devised and implemented in fog zone to find efficient route for forwarding contents using Nano-Caches and subsequently to improve content retrieval time. Mathematical distribution model of traffic is used for simulation process. MTLBO is compared with existing algorithms, namely, Teaching Learning-Based Optimization (TLBO) Algorithm and Simulated Annealing (SA) Algorithm. The design of experiments (DOE) was carried out to observe number of iterations, learning rate, and by changing the network size. Java library was used for observing values of memory and execution time. The results show that Modified Teaching Learning-Based Optimization (MTLBO) approach is better than Teaching Learning-Based Optimization (TLBO) approach as it has less overheads in terms of memory (considering number of fog caches) and network size for delivering contents at remote areas. In comparison to the Simulated Annealing (SA) algorithm, MTLBO performs better in terms of execution time, overhead in terms of memory, and scalability as function of network size. Wed, 01 Jan 2020 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/779 2020-01-01T00:00:00Z