Integrating Scientific Publication into an Applied Gaming Ecosystem

Salman, M. et al. (2016). Integrating Scientific Publication into an Applied Gaming Ecosystem. GSTF Journal on Computing (JoC), Volume 5 (Issue 1), pp. 45-51.

Fingerprinting of Mangrove Ecosystems along Northern Kerala Coast using Biomarker Approach

Mangrove forests are the most productive and bio-diverse wetlands on earth. It generate a large amount of litter in the form of leaves, branches, twigs, inflorescence and other debris and provides habitat for diverse flora and fauna of marine and terrestrial origin such as bacteria, fungi, algae, lichens, zooplankton, benthos, birds, reptiles and mammals. These systems act as nursery for many fishes and shellfishes. The other sources may also provide important organic carbon inputs; including allochthonous riverine or marine material, autochthonous production by benthic or epiphytic micro- or macroalgae, and local water column production by phytoplankton. Since mangrove sediments are very complex which receives autochthonous and allochthonous organic matter inputs, the information extracted from the analysis of mangrove sediments is the fingerprint of both natural and human-induced changes.

Recomendr-entity recommendation based on ad-hoc dimensions

The growing availability and popularity of opinion rich resources on the online web resources, such as review sites and personal blogs, has made it convenient to find out about the opinions and experiences of layman people. But, simultaneously, this huge eruption of data has made it difficult to reach to a conclusion. In this thesis, I develop a novel recommendation system, Recomendr that can help users digest all the reviews about an entity and compare candidate entities based on ad-hoc dimensions specified by keywords. It expects keyword specified ad-hoc dimensions/features as input from the user and based on those features; it compares the selected range of entities using reviews provided on the related User Generated Contents (UGC) e.g. online reviews. It then rates the textual stream of data using a scoring function and returns the decision based on an aggregate opinion to the user. Evaluation of Recomendr using a data set in the laptop domain shows that it can effectively recommend the best laptop as per user-specified dimensions such as price. Recomendr is a general system that can potentially work for any entities on which online reviews or opinionated text is available.