A Methodology for Relating Software Structure with Energy Consumption

Bangash, Abdul Ali; Sahar, Hareem; Beg, Mirza Omer

doi:10.1109/scam.2017.18

Cited by 6 publications

(2 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To utilise the intrinsic semantic and hierarchical structure [9] of the input sentences a graph is the most suitable structure likely to be built using the extracted information. There are many methods of constructing graphs, one such method involves recognizing named entities [24].…”

Section: Srl Graphmentioning

confidence: 99%

Few Shot Learning for Information Verification

Khalid,

Beg

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Information verification is quite a challenging task, this is because many times verifying a claim can require picking pieces of information from multiple pieces of evidence which can have a hierarchy of complex semantic relations. Previously a lot of researchers have mainly focused on simply concatenating multiple evidence sentences to accept or reject claims. These approaches are limited as evidence can contain hierarchical information and dependencies. In this research, we aim to verify facts based on evidence selected from a list of articles taken from Wikipedia. Pretrained language models such as XLNET are used to generate meaningful representations and graph-based attention and convolutions are used in such a way that the system requires little additional training to learn to verify facts.

show abstract

Section: Srl Graphmentioning

confidence: 99%

Few Shot Learning for Information Verification

Khalid,

Beg

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…One day for this purpose these firms will take their technology to underdeveloped countries where these successful AI models will fail and their new training will be required according to the new environment. In the previous few years America based motor company Tesla gained a lot of customer attraction and share's value in the stock market for designing autopilot vehicles and got the promising result in autopilot [9] . These autonomous cars are designed by implementing physical traffic rules and road sense in the AI model to control the car while driving.…”

Section: Introductionmentioning

confidence: 99%

Image Classification using CNN for Traffic Signs in Pakistan

Azeem¹,

Ali²

2021

Preprint

View full text Add to dashboard Cite

The autonomous automotive industry is one of the largest and most conventional projects worldwide, with many technology companies effectively designing and orienting their products towards automobile safety and accuracy. These products are performing very well over the roads in developed countries. But can fail in the first minute in an underdeveloped country because there is much difference between a developed country environment and an underdeveloped country environment. The following study proposed to train these Artificial intelligence models in environment space in an underdeveloped country like Pakistan. The proposed approach on image classification uses convolutional neural networks for image classification for the model. For model pre-training German traffic signs data set was selected then fine tuned on Pakistan's dataset. The experimental setup showed the best results and accuracy from the previously conducted experiments. In this work to increase the accuracy, more dataset was collected to increase the size of images in every class in the data set. In the future, a low number of classes are required to be further increased where more images for traffic signs are required to be collected to get more accuracy on the training of the model over traffic signs of Pakistan's most used and popular roads motorway and national highway, whose traffic signs color, size, and shapes are different from common traffic signs.

show abstract