STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH <p>Journal of Statistics, Computing and Interdisciplinary Research is a leading journal which provides a forum for communication among statisticians and practitioners for judicious application of statistical principles and innovations of statistical methodology motivated by current and important real-world examples across a wide range of disciplines, including, but not limited to:</p> <ul> <li style="box-sizing: border-box;">Survey Sampling</li> <li style="box-sizing: border-box;">Machine Learning</li> <li style="box-sizing: border-box;">Neural Networking</li> <li style="box-sizing: border-box;">Bio-Statistics</li> <li style="box-sizing: border-box;">Operations Research</li> <li style="box-sizing: border-box;">Geo-Statistics</li> <li style="box-sizing: border-box;">Biological and Biomedical Sciences.</li> <li style="box-sizing: border-box;">Business, Economics, Management and Finance.</li> <li style="box-sizing: border-box;">Computer Science and Information Technology</li> <li style="box-sizing: border-box;">Data Science</li> <li style="box-sizing: border-box;">Ecology</li> <li style="box-sizing: border-box;">Education</li> <li style="box-sizing: border-box;">Engineering</li> <li style="box-sizing: border-box;">Genetics and Genomics </li> <li style="box-sizing: border-box;">Medicine and Related Disciplines</li> <li style="box-sizing: border-box;">Social Sciences</li> </ul> <p> </p> <p><strong>PATRON IN CHIEF</strong><br /><strong>Prof. Dr Uzma Quraishi</strong><br />Vice Chancellor,<br />The Women University Multan, Pakistan<br /><strong>Email:</strong> <a href=""></a></p> <p> </p> <p><strong>EDITOR-IN-CHIEF</strong><br /><strong>Dr. Sohail, F.</strong><br />Faculty of Social Sciences,<br />The Women University Multan, Pakistan.<br /><br /></p> The Women University Multan en-US STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH 2707-7101 The Advantages of Using Artificial Intelligence in Urban Planning – A Review of Literature <p>Urban planning plays a crucial role in shaping communities for the future, analyzing their strengths and weaknesses, and proposing improvement solutions.<br>This systematic literature review process used in this study ensured the inclusion of a wide range of sources, providing a comprehensive overview of the advantages of using artificial intelligence in urban planning. After a careful review of the literature, it is found that artificial intelligence is primarily used to enhance efficiency in urban planning processes, contributing to sustainability. The potential for future challenges has accelerated the adoption of artificial intelligence in urban planning by emphasizing the need for resilient, technology-driven cities. It supports more efficient urban design by automating tasks through generative design software, which subdivides plots and applies housing solutions. Artificial intelligence's role expands to multiple areas within the urban planning obligations of a city, including transportation, environment, waste management, education, healthcare, agriculture, risk management, and security. It guides urban planners by improving analytical models for understanding future physical and social community conditions. Artificial intelligence empowers planners to explore new data collection and analysis methods for predicting urban behavior, saving time and resources. It addresses climate change challenges by supporting transportation, waste management, and water use applications. Artificial intelligence technologies continue to advance, urban planning stands to benefit from increased efficiency, data-driven decision-making, and the creation of more sustainable, resilient cities. Urban planners must adapt themselves by acquiring the necessary skills to ensure successful city planning for the future. Overall, Artificial intelligence is contributing heavily to achieving sustainability by integrating past practices, trends, and new AI-based models.</p> Muhammad Mashhood Hamna Salman Romaiza Amjad Hassan Nisar Copyright (c) 2023 2023-10-20 2023-10-20 5 2 1 12 Thermal Performance of Autocatalytic Chemical Reaction for Hybrid Nanomaterial Fluid Flow <p>The mass and heat transfer in hybrid magnetohydrodynamic (MHD) nanofluids is controlled by an autocatalytic chemical mechanism, which is the subject of the current study's thorough analysis. The focus of the work is on the nanolayers at interfaces, which link nanoparticles with the supporting fluids and enable coupled mass and heat transfer phenomena.To further investigate its effects, a uniform transverse magnetic field is added to the study.By using similarity methods, the governing nonlinear coupled partial differential equation that describes this sophisticated system is converted into a collection of ordinary differential equations (ODEs). Two numerical approaches( Bvp4c) and the Shooting method, are used to solve the ODEs in order to get precise answers and do a comparison study. One interesting finding about the improvement of thermal performance is that a rise in nanolayer thickness (between 1 and 4) considerably adds to the enhancement. Additionally, it is discovered that improvements in the chemical reaction parameter, which ranges from 0.15 to 0.27, cause the Sherwood number to rise.It is noteworthy that the results of this study add to a better understanding of the complex interactions between magnetohydrodynamics, chemical processes, and nanofluid dynamics. The numerical techniques used highlight the significance of accurate mathematical modeling in illuminating the complexity of such systems. In addition to strengthening the theoretical foundation, this study offers useful information that could have an impact on heat transfer and nanofluid technology applications.</p> Tahir Mahmood Tanzila Riasat Toheed Jillani Copyright (c) 2023 STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH 2023-10-20 2023-10-20 5 2 13 27 10.52700/scir.v5i2.124 Analyzing the Impact of Subsectors and Population Growth on Agricultural Sector in Pakistan <p>The agriculture sector in Pakistan plays its vital role for providing food security and economic stability. Pakistan's agriculture sector comprises various subsectors i.e. crops, livestock, fisheries, and forestry. Agriculture growth is threatened by the growth of these subsectors and the impact of population growth on it. No significant research has been conducted in Pakistan to study the statistical significance of these subsectors and their relationship with population growth. This study analyzes data from 2005 to 2023. Regression analysis is used to identify and to compare the statistical significance of all sub sectors of agriculture growth with population growth rate. Average growth rates found positive for agriculture, major crops, livestock, fisheries, and population, while negative for forestry. Model found good fit with R2 0.936. Major crops, livestock, and fisheries have positive and statistically significant impacts on Pakistan's agricultural growth, with coefficients of 0.316, 0.426, and 0.015, while forestry and population reported negative and statistically insignificant results with coefficients of -0.019 and -0.273. This research laid out good policy decisions aimed at boosting agricultural growth in Pakistan.</p> Muhammad Islam Syed Ijaz Hussain Shah Syeda Amna Wajahat Muhammad Faheem Bhatti Noor Ul Ain Copyright (c) 2023 2023-11-13 2023-11-13 5 2 29 37 10.52700/scir.v5i2.128 Optimal Solution for Segmentation of Malignant Melanoma Dermoscopic Images <p>Melanoma Malignant (MM) is the most common and dangerous form of skin cancer, which is analyzed by using Dermoscopic images in computer sciences. Segmentation technique is used to separate lesion part from healthy part in Dermoscopic images. In this research, comparison of different most popular segmented Dermoscopic image technique like Type-2 Fuzzy, Hybrid Threshold, Wavelet, Gradient Vector Flow (GVF), and Watershed etc. is approached and then better segmentation technique is proposed. In these segmentation techniques different issues like problem of hair, different color lesion, specular reflection and smoothing transaction between lesion and skin were not taken under consideration. Our methodology involves three levels of hierarchy. In the preprocessing step, it deals with problem of hair, bubble noise, smoothing and reflection noise in Dermoscopic images. These noise removals are achieved by using different filters like “Derivative of Gaussian filter and Bootomhat filter”. After region of interest is extracted then combination of threshold, image enhancement and morphological filter are used to produce the efficient algorithm for segmentation. At the end step, segmented crop image is compared with dice coefficient and experimental results of gross error rate are evaluated. For this purpose, PH² Dataset is used that contains 200 Dermoscopic images with the lesion images. The lesion images are extracted by the expert dermatologists.</p> Tahir Abbas Muhammad Kashan Basit Jamshaid Iqbal Janjua Bushra Tanveer Naqvi Muhammad Irfan Copyright (c) 2023 STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH 2023-11-13 2023-11-13 5 2 39 49 10.52700/scir.v5i2.127