arXiv subjects

Fahim Faisal

Publications and source records attributed to Fahim Faisal.

22 records · Page 2Linked to original sources

arXiv2021

Design, Simulation and Feasibility Analysis of Bifacial Solar PV System in Marine Drive Road, Cox's Bazar

This paper proposes a design and simulation based investigative analysis of a vertically mounted bifacial solar photovoltaic model in Marine Drive Road, Cox's Bazar. Cox's bazar is a famous tourist destination which seems to be a flexible site for implementing such energy harvesting system without affecting the nearby eco-system and solves the existing land shortage problem. Moreover, the infrastructure will provide insulation to noise related problem faced by nearby residents, arising from traffic noises. A model road of 200 meters is reconnoitered for energy harvesting by solar power using three prominent software namely PVSOL, PVsyst, and SAM where a promising mean annual yield of 70492.9 kWh is obtained, and the bifacial gain is calculated to be 12.26%. In addition, a deviation analysis is performed among each of the software and it is found that PVSOL and PVsyst have shown less deviation. Furthermore, a comprehensive financial analysis shows total installation cost to be 84759.74$.

eess.SY↗

arXiv2021

Code to Comment Translation: A Comparative Study on Model Effectiveness & Errors

Automated source code summarization is a popular software engineering research topic wherein machine translation models are employed to "translate" code snippets into relevant natural language descriptions. Most evaluations of such models are conducted using automatic reference-based metrics. However, given the relatively large semantic gap between programming languages and natural language, we argue that this line of research would benefit from a qualitative investigation into the various error modes of current state-of-the-art models. Therefore, in this work, we perform both a quantitative and qualitative comparison of three recently proposed source code summarization models. In our quantitative evaluation, we compare the models based on the smoothed BLEU-4, METEOR, and ROUGE-L machine translation metrics, and in our qualitative evaluation, we perform a manual open-coding of the most common errors committed by the models when compared to ground truth captions. Our investigation reveals new insights into the relationship between metric-based performance and model prediction errors grounded in an empirically derived error taxonomy that can be used to drive future research efforts

cs.SE↗

arXiv2019

Mining Temporal Evolution of Knowledge Graph and Genealogical Features for Literature-based Discovery Prediction

Literature-based knowledge discovery process identifies the important but implicit relations among information embedded in published literature. Existing techniques from Information Retrieval and Natural Language Processing attempt to identify the hidden or unpublished connections between information concepts within published literature, however, these techniques undermine the concept of predicting the future and emerging relations among scientific knowledge components encapsulated within the literature. Keyword Co-occurrence Network (KCN), built upon author selected keywords (i.e., knowledge entities), is considered as a knowledge graph that focuses both on these knowledge components and knowledge structure of a scientific domain by examining the relationships between knowledge entities. Using data from two multidisciplinary research domains other than the medical domain, capitalizing on bibliometrics, the dynamicity of temporal KCNs, and a Long Short Term Memory recurrent neural network, this study proposed a framework to successfully predict the future literature-based discoveries - the emerging connections among knowledge units. Framing the problem as a dynamic supervised link prediction task, the proposed framework integrates some novel node and edge-level features. Temporal importance of keywords computed from both bipartite and unipartite networks, communities of keywords, built upon genealogical relations, and relative importance of temporal citation counts used in the feature construction process. Both node and edge-level features were input into an LSTM network to forecast the feature values for positive and negatively labeled non-connected keyword pairs and classify them accurately. High classification performance rates suggest that these features are supportive both in predicting the emerging connections between scientific knowledge units and emerging trend analysis.

cs.SI↗

arXiv2019

Disease Identification From Unstructured User Input

A method to identify probable diseases from the unstructured textual input (eg, health forum posts) by incorporating a lexicographic and semantic feature based two-phase text classification module and a symptom-disease correlation-based similarity measurement module. One notable aspect of my approach was to develop a competent algorithm to extract all inherent features from the data source to make a better decision.

cs.IR↗