http://localhost:80/xmlui/handle/123456789/12765 2026-04-05T01:43:41Z 2026-04-05T01:43:41Z Shahid, Z http://localhost:80/xmlui/handle/123456789/13757 2022-10-26T10:05:42Z 2015-12-15T00:00:00Z

Title: AWARENESS FOR BETTER ADAPTATION STRATEGY DEVELOPMENT FOR CLIMATE CHANGE IMPACTS IN PAKISTAN Authors: Shahid, Z Abstract: Climate change awareness has become very important to develop adaptation strategies for climate change impacts. Climate change impacts varied in type and intensity for different countries, but the lack of awareness is making developing countries more vulnerable to the alarming impacts of climate change. The purpose of this study was to explore the level of awareness to make adaptation strategy for climate change impacts in Lahore, Pakistan. Relevant literature on climate change awareness revealed that climate change awareness in Pakistan was very low. The findings of this paper were based on a detailed survey conducted with the general public of Lahore. This research paper was attempted to find out the existing level of climate change awareness for making better adaptation strategy in Lahore.

2015-12-15T00:00:00Z Shakrullah, K Shirazi, S.A Sajjad, S.H http://localhost:80/xmlui/handle/123456789/13756 2022-10-26T10:05:19Z 2015-12-14T00:00:00Z

Title: SPATIO-TEMPORAL ANALYSIS OF LAND COVER CHANGES OF DHAKA CITY IN BANGLADESH Authors: Shakrullah, K; Shirazi, S.A; Sajjad, S.H Abstract: The objective of this study was to identify the Spatio-temporal variation in landcover of Dhaka city, the capital of Bangladesh. The study was conducted to highlight the issues of changing land-cover of mega cities of Bangladesh and its future environmental consequences. Land cover changes and temporal maps for the selected study areas were developed by using multi-temporal information data sets by Multispectral Scanner (MSS), Thematic Map (TM), Enhanced Thematic Mapper (ETM), Enhanced Thematic Mapper Plus (ETM+),or Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) for the years 1993, 2000, 2010 and 2015. The results of this study highlighted the rapid urbanization in Dhaka which caused significant decline in vegetation cover and increase of built-up or paved surfaces. The land-cover classified images highlighted, that in 1993 only 22% per cent area of the study area was covered with built-up surface. However, in 2015, it increased up to 48%. This substantial decline in vegetation cover and upsurge in built-up area of Dhaka city may put petrifying effect on local environments.

2015-12-14T00:00:00Z Maqsood, M Habib, H.A Nawaz, T http://localhost:80/xmlui/handle/123456789/13755 2022-10-26T10:05:08Z 2015-12-13T00:00:00Z

Title: SELECTION OF DISCRIMINATIVE FEATURES FOR ARABIC PHONEME’S MISPRONUNCIATION DETECTION Authors: Maqsood, M; Habib, H.A; Nawaz, T Abstract: Pronunciation training is an important part of Computer Assisted Pronunciation Training (CAPT) systems. Mispronunciation detection systems recognized pronunciation mistakes from user’s speech and provided them feedback about their pronunciation. Acoustic phonetic features plays a vital role in speech classification based applications. This research work investigated the suitability of various acoustic features: pitch, energy, spectrum flux, zero-crossing, Entropy and MelFrequency Cepstral Coefficients (MFCCs). Sequential Forward Selection (SFS) was used to find out most suitable acoustic features from the computed feature set. This study used K-Nearest Neighbors (K-NN) classifier was used to detect the pronunciation mistakes from Arabic phonemes. This research selected the set of most discriminative acoustic features for each phoneme. K-NN achieved accuracy of 92.15% for mispronunciation detection of Arabic Phonemes

2015-12-13T00:00:00Z Tahir, M Shaukat, A Kanwal, N http://localhost:80/xmlui/handle/123456789/13754 2022-10-26T10:04:39Z 2015-12-12T00:00:00Z

Title: CAN MULTIPLE MODELS IMPROVE BAYESIAN'S PERFORMANCE? AN INVESTIGATION USING MCNEMAR'S TEST Authors: Tahir, M; Shaukat, A; Kanwal, N Abstract: Machine learning algorithms have been widely used for classification purposes in a number of research domains; however, very few researches paid any attention to statistically validate the performance of these algorithms for different data. This paper attempted to study the Naïve Bayes algorithm’s performance for dataset of different sizes. Furthermore, a known theory has also been investigated, that building multiple models such as Bagging, Boosting and Stacking tend to improve a classifier’s performance. The analysis has been performed using McNemar’s test; a well known nonparametric statistical test in the medical analysis domain. Results showed that not all ensemble methods work as expected and therefore, needs to be selected carefully. Moreover, the use of McNemar’s test appeared to be simple, but gave statistically valid results.

2015-12-12T00:00:00Z