A Fuzzy Logic Based Software Cost Estimation Model

Ziauddin,Shahid Kamal,Shafiullah khan,Jamal Abdul Nasir 보안공학연구지원센터 2013 International Journal of Software Engineering and Vol.7 No.2

A Hybrid Query Disambiguation Adaptive Approach for Web Information Retrieval

( Roliana Ibrahim ),( Shahid Kamal ),( Imran Ghani ),( Seung Ryul Jeong ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.7

In web searching, trustable and precise results are greatly affected by the inherent uncertainty in the input queries. Queries submitted to search engines are by nature ambiguous and constitute a significant proportion of the instances given to web search engines. Ambiguous queries pose real challenges for the web search engines due to versatility of information. Temporal based approaches whereas somehow reduce the uncertainty in queries but still lack to provide results according to users aspirations. Web search science has created an interest for the researchers to incorporate contextual information for resolving the uncertainty in search results. In this paper, we propose an Adaptive Disambiguation Approach (ADA) of hybrid nature that makes use of both the temporal and contextual information to improve user experience. The proposed hybrid approach presents the search results to the users based on their location and temporal information. A Java based prototype of the systems is developed and evaluated using standard dataset to determine its efficacy in terms of precision, accuracy, recall, and F1-measure. Supported by experimental results, ADA demonstrates better results along all the axes as compared to temporal based approaches.

Multi-response Optimization in the Development of a Superhydrophobic Cotton Fabric Using ZnO Nanoparticles Mediated Resin Finish under Taguchi Based Grey Relational Analysis and Fuzzy Logics Approaches

Naseer Ahmad,Shahid Kamal,Zulfiqar Ali Raza,Sharjeel Abid,Muhammad Zeshan 한국섬유공학회 2020 Fibers and polymers Vol.21 No.5

Present work investigates multi-response optimization in development of super-oleohydrophobic cotton fabricunder pad-dry-cure method. A bleached cotton fabric was treated with ZnO nanoparticles (NPs) incorporated oil and waterrepellent finish (Oleophobol CP-C®) to impart in it antibacterial activity, UV protection and super oleo/hydrophobicity. Taguchi based fuzzy logics and grey relational analytical techniques were employed to obtain simultaneous optimum settingsof input parameters including concentrations of ZnO NPs, O-CPC® finish and Knittex FEL®, and curing temperature formultiple responses. The fuzzy logics and grey relational analysis were employed on the experimental data to determinesignificant process parameters for optimization of multiple responses. The present set of techniques was effectively used todevelop super-hydrophobic (WCA: 162 o) and oleophobic (OCA: 140 o) cotton fabric along with appropriate textileproperties as reported in the text. The developed fabric has potential uses in various domestic and house-hold applicationsdue to its antibacterial, self-cleaning, non-staining and UV-protection properties.

Characteristics of Annual and Seasonal Trends of Rainfall and Temperature in Iraq

Saleem A. Salman,Shamsuddin Shahid,Tarmizi Ismail,Kamal Ahmed,Eun-Sung Chung,Xiao-Jun Wang 한국기상학회 2019 Asia-Pacific Journal of Atmospheric Sciences Vol.55 No.3

Changes in the temperature and precipitation have significantly affected water resources and agricultural productions in many countries across the world. The objective of the present study is to analyze the changing patterns of annual and seasonal precipitation and temperature in Iraq for the period 1961–2010. Monthly gridded precipitation and temperature data of Global precipitation climate center (GPCC) and climate research unit (CRU) respectively having a spatial resolution of 0.5° were used in this study to show the spatial pattern in trends. The rate of change in rainfall and temperature was estimated using Sen’s slope method while the significance of change was confirmed using Mann-Kendal test (MK) and the modified Mann-Kendall test (mMK). The results revealed large differences in the number of grid points showing significant changes in rainfall and temperature using MK and mMK methods. The mMK method revealed that the annual rainfall is decreasing at a rate of −1.0 to −5.0 mm/year in the northwest part of Iraq. The seasonal precipitations were found to decrease in spring (−0.4 to −2.56 mm/ year) and winter (−0.4 to −2.0 mm/year), increase in summer (0.06 to 0.21 mm/year) at a few grid points and no change in autumn. On the other hand, a sharp rise in annual average of daily mean (0.42 to 0.64 °C/decade), maximum (0.39 to 0.65 °C/ decade) and minimum (0.36 to 0.69 °C/decade) temperature was observed.

Sentiment Analysis for COVID-19 Vaccine Popularity

( Muhammad Saeed ),( Naeem Ahmed ),( Abid Mehmood ),( Muhammad Aftab ),( Rashid Amin ),( Shahid Kamal ) 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.5

Social media is used for various purposes including entertainment, communication, information search, and voicing their thoughts and concerns about a service, product, or issue. The social media data can be used for information mining and getting insights from it. The World Health Organization has listed COVID-19 as a global epidemic since 2020. People from every aspect of life as well as the entire health system have been severely impacted by this pandemic. Even now, after almost three years of the pandemic declaration, the fear caused by the COVID-19 virus leading to higher depression, stress, and anxiety levels has not been fully overcome. This has also triggered numerous kinds of discussions covering various aspects of the pandemic on the social media platforms. Among these aspects is the part focused on vaccines developed by different countries, their features and the advantages and disadvantages associated with each vaccine. Social media users often share their thoughts about vaccinations and vaccines. This data can be used to determine the popularity levels of vaccines, which can provide the producers with some insight for future decision making about their product. In this article, we used Twitter data for the vaccine popularity detection. We gathered data by scraping tweets about various vaccines from different countries. After that, various machine learning and deep learning models, i.e., naive bayes, decision tree, support vector machines, k-nearest neighbor, and deep neural network are used for sentiment analysis to determine the popularity of each vaccine. The results of experiments show that the proposed deep neural network model outperforms the other models by achieving 97.87% accuracy.

Arabic Words Extraction and Character Recognition from Picturesque Image Macros with Enhanced VGG-16 based Model Functionality Using Neural Networks

Ayed Ahmad Hamdan Al-Radaideh,Mohd Shafry bin Mohd Rahim,Wad Ghaban,Majdi Bsoul,Shahid Kamal,Naveed Abbas 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.7

Innovation and rapid increased functionality in user friendly smartphones has encouraged shutterbugs to have picturesque image macros while in work environment or during travel. Formal signboards are placed with marketing objectives and are enriched with text for attracting people. Extracting and recognition of the text from natural images is an emerging research issue and needs consideration. When compared to conventional optical character recognition (OCR), the complex background, implicit noise, lighting, and orientation of these scenic text photos make this problem more difficult. Arabic language text scene extraction and recognition adds a number of complications and difficulties. The method described in this paper uses a two-phase methodology to extract Arabic text and word boundaries awareness from scenic images with varying text orientations. The first stage uses a convolution auto-encoder, and the second uses Arabic Character Segmentation (ACS), which is followed by traditional two-layer neural networks for recognition. This study presents the way that how can an Arabic training and synthetic dataset be created for exemplify the superimposed text in different scene images. For this purpose a dataset of size 10K of cropped images has been created in the detection phase wherein Arabic text was found and 127k Arabic character dataset for the recognition phase. The phase-1 labels were generated from an Arabic corpus of quotes and sentences, which consists of 15kquotes and sentences. This study ensures that Arabic Word Awareness Region Detection (AWARD) approach with high flexibility in identifying complex Arabic text scene images, such as texts that are arbitrarily oriented, curved, or deformed, is used to detect these texts. Our research after experimentations shows that the system has a 91.8% word segmentation accuracy and a 94.2% character recognition accuracy. We believe in the future that the researchers will excel in the field of image processing while treating text images to improve or reduce noise by processing scene images in any language by enhancing the functionality of VGG-16 based model using Neural Networks.