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1. Exploring learning environments for label\-efficient cancer diagnosis

Author

Rani, Samta, Ahmad, Tanvir, Masood, Sarfaraz, and Saxena, Chandni

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Despite significant research efforts and advancements, cancer remains a leading cause of mortality. Early cancer prediction has become a crucial focus in cancer research to streamline patient care and improve treatment outcomes. Manual tumor detection by histopathologists can be time consuming, prompting the need for computerized methods to expedite treatment planning. Traditional approaches to tumor detection rely on supervised learning, necessitates a large amount of annotated data for model training. However, acquiring such extensive labeled data can be laborious and time\-intensive. This research examines the three learning environments: supervised learning (SL), semi\-supervised learning (Semi\-SL), and self\-supervised learning (Self\-SL): to predict kidney, lung, and breast cancer. Three pre\-trained deep learning models (Residual Network\-50, Visual Geometry Group\-16, and EfficientNetB0) are evaluated based on these learning settings using seven carefully curated training sets. To create the first training set (TS1), SL is applied to all annotated image samples. Five training sets (TS2\-TS6) with different ratios of labeled and unlabeled cancer images are used to evaluateSemi\-SL. Unlabeled cancer images from the final training set (TS7) are utilized for Self\-SL assessment. Among different learning environments, outcomes from the Semi\-SL setting show a strong degree of agreement with the outcomes achieved in the SL setting. The uniform pattern of observations from the pre\-trained models across all three datasets validates the methodology and techniques of the research. Based on modest number of labeled samples and minimal computing cost, our study suggests that the Semi\-SL option can be a highly viable replacement for the SL option under label annotation constraint scenarios., Comment: Submitted to the journal

Published
2024

2. Joint Stream: Malignant Region Learning for Breast Cancer Diagnosis

Author

Rehman, Abdul, Hussein, Sarfaraz, and Sultani, Waqas

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Early diagnosis of breast cancer (BC) significantly contributes to reducing the mortality rate worldwide. The detection of different factors and biomarkers such as Estrogen receptor (ER), Progesterone receptor (PR), Human epidermal growth factor receptor 2 (HER2) gene, Histological grade (HG), Auxiliary lymph node (ALN) status, and Molecular subtype (MS) can play a significant role in improved BC diagnosis. However, the existing methods predict only a single factor which makes them less suitable to use in diagnosis and designing a strategy for treatment. In this paper, we propose to classify the six essential indicating factors (ER, PR, HER2, ALN, HG, MS) for early BC diagnosis using H\&E stained WSI's. To precisely capture local neighboring relationships, we use spatial and frequency domain information from the large patch size of WSI's malignant regions. Furthermore, to cater the variable number of regions of interest sizes and give due attention to each region, we propose a malignant region learning attention network. Our experimental results demonstrate that combining spatial and frequency information using the malignant region learning module significantly improves multi-factor and single-factor classification performance on publicly available datasets., Comment: Under Review (Biomedical Signal Processing and Control)

Published
2024
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3. Geometric Bipartite Matching is in NC

Author

Bhore, Sujoy, Equbal, Sarfaraz, and Gurjar, Rohit

Subjects
Computer Science - Computational Geometry, Computer Science - Computational Complexity
Abstract

In this work, we study the parallel complexity of the Euclidean minimum-weight perfect matching (EWPM) problem. Here our graph is the complete bipartite graph $G$ on two sets of points $A$ and $B$ in $\mathbb{R}^2$ and the weight of each edge is the Euclidean distance between the corresponding points. The weighted perfect matching problem on general bipartite graphs is known to be in RNC [Mulmuley, Vazirani, and Vazirani, 1987], and Quasi-NC [Fenner, Gurjar, and Thierauf, 2016]. Both of these results work only when the weights are of $O(\log n)$ bits. It is a long-standing open question to show the problem to be in NC. First, we show that for EWPM, a linear number of bits of approximation is required to distinguish between the minimum-weight perfect matching and other perfect matchings. Next, we show that the EWPM problem that allows up to $\frac{1}{poly(n)}$ additive error, is in NC., Comment: 15 pages, 4 figures

Published
2024

4. Fair Interval Scheduling of Indivisible Chores

Author

Equbal, Sarfaraz, Gurjar, Rohit, Kumar, Yatharth, Nath, Swaprava, and Vaish, Rohit

Subjects
Computer Science - Computer Science and Game Theory
Abstract

We study the problem of fairly assigning a set of discrete tasks (or chores) among a set of agents with additive valuations. Each chore is associated with a start and finish time, and each agent can perform at most one chore at any given time. The goal is to find a fair and efficient schedule of the chores, where fairness pertains to satisfying envy-freeness up to one chore (EF1) and efficiency pertains to maximality (i.e., no unallocated chore can be feasibly assigned to any agent). Our main result is a polynomial-time algorithm for computing an EF1 and maximal schedule for two agents under monotone valuations when the conflict constraints constitute an arbitrary interval graph. The algorithm uses a coloring technique in interval graphs that may be of independent interest. For an arbitrary number of agents, we provide an algorithm for finding a fair schedule under identical dichotomous valuations when the constraints constitute a path graph. We also show that stronger fairness and efficiency properties, including envy-freeness up to any chore (EFX) along with maximality and EF1 along with Pareto optimality, cannot be achieved.

Published
2024

15. Tailoring Adversarial Attacks on Deep Neural Networks for Targeted Class Manipulation Using DeepFool Algorithm

Author

Labib, S. M. Fazle Rabby, Mondal, Joyanta Jyoti, Manab, Meem Arafat, Newaz, Sarfaraz, and Xiao, Xi

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

The susceptibility of deep neural networks (DNNs) to adversarial attacks undermines their reliability across numerous applications, underscoring the necessity for an in-depth exploration of these vulnerabilities and the formulation of robust defense strategies. The DeepFool algorithm by Moosavi-Dezfooli et al. (2016) represents a pivotal step in identifying minimal perturbations required to induce misclassification of input images. Nonetheless, its generic methodology falls short in scenarios necessitating targeted interventions. Additionally, previous research studies have predominantly concentrated on the success rate of attacks without adequately addressing the consequential distortion of images, the maintenance of image quality, or the confidence threshold required for misclassification. To bridge these gaps, we introduce the Enhanced Targeted DeepFool (ET DeepFool) algorithm, an evolution of DeepFool that not only facilitates the specification of desired misclassification targets but also incorporates a configurable minimum confidence score. Our empirical investigations demonstrate the superiority of this refined approach in maintaining the integrity of images and minimizing perturbations across a variety of DNN architectures. Unlike previous iterations, such as the Targeted DeepFool by Gajjar et al. (2022), our method grants unparalleled control over the perturbation process, enabling precise manipulation of model responses. Preliminary outcomes reveal that certain models, including AlexNet and the advanced Vision Transformer, display commendable robustness to such manipulations. This discovery of varying levels of model robustness, as unveiled through our confidence level adjustments, could have far-reaching implications for the field of image recognition. Our code will be made public upon acceptance of the paper., Comment: 18 pages, 5 figures

Published
2023

29. Uncovering local aggregated air quality index with smartphone captured images leveraging efficient deep convolutional neural network

Author

Mondal, Joyanta Jyoti, Islam, Md. Farhadul, Islam, Raima, Rhidi, Nowsin Kabir, Newaz, Sarfaraz, Manab, Meem Arafat, Islam, A. B. M. Alim Al, and Noor, Jannatun

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

The prevalence and mobility of smartphones make these a widely used tool for environmental health research. However, their potential for determining aggregated air quality index (AQI) based on PM2.5 concentration in specific locations remains largely unexplored in the existing literature. In this paper, we thoroughly examine the challenges associated with predicting location-specific PM2.5 concentration using images taken with smartphone cameras. The focus of our study is on Dhaka, the capital of Bangladesh, due to its significant air pollution levels and the large population exposed to it. Our research involves the development of a Deep Convolutional Neural Network (DCNN), which we train using over a thousand outdoor images taken and annotated. These photos are captured at various locations in Dhaka, and their labels are based on PM2.5 concentration data obtained from the local US consulate, calculated using the NowCast algorithm. Through supervised learning, our model establishes a correlation index during training, enhancing its ability to function as a Picture-based Predictor of PM2.5 Concentration (PPPC). This enables the algorithm to calculate an equivalent daily averaged AQI index from a smartphone image. Unlike, popular overly parameterized models, our model shows resource efficiency since it uses fewer parameters. Furthermore, test results indicate that our model outperforms popular models like ViT and INN, as well as popular CNN-based models such as VGG19, ResNet50, and MobileNetV2, in predicting location-specific PM2.5 concentration. Our dataset is the first publicly available collection that includes atmospheric images and corresponding PM2.5 measurements from Dhaka. Our codes and dataset are available at https://github.com/lepotatoguy/aqi., Comment: 18 pages, 7 figures, published to Nature Scientific Reports

Published
2023
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30. Go Together: Bridging the Gap between Learners and Teachers

Author

Irfan, Asim, Nawaz, Atif, Turab, Muhammad, Azeem, Muhmmad, Adnan, Mashal, Mehmood, Ahsan, Ahmed, Sarfaraz, and Ashraf, Adnan

Subjects
Computer Science - Human-Computer Interaction
Abstract

After the pandemic, humanity has been facing different types of challenges. Social relationships, societal values, and academic and professional behavior have been hit the most. People are shifting their routines to social media and gadgets, and getting addicted to their isolation. This sudden change in their lives has caused an unusual social breakdown and endangered their mental health. In mid-2021, Pakistan's first Human Library was established under HelpingMind to overcome these effects. Despite online sessions and webinars, HelpingMind needs technology to reach the masses. In this work, we customized the UI or UX of a Go Together Mobile Application (GTMA) to meet the requirements of the client organization. A very interesting concept of the book (expert listener or psychologist) and the reader is introduced in GTMA. It offers separate dashboards, separate reviews or rating systems, booking, and venue information to engage the human reader with his or her favorite human book. The loyalty program enables the members to avail discounts through a mobile application and its membership is global where both the human-reader and human-books can register under the platform. The minimum viable product has been approved by our client organization.

Published
2023

35. Faculty application and perceived effectiveness of cognitive psychology principles in medical education. A mixed method study

Author

Shaur Sarfaraz, Ambreen Surti, Rabiya Ali, Rehana Rehman, Artak Heboyan, and Naseer Ahmed

Subjects
Cognitive psychology, Student learning, Validation, Undergraduate, Medical education, Faculty, Special aspects of education, LC8-6691, Medicine
Abstract

Abstract Objective The objective of the study was to explore the extent to which faculty members in undergraduate medical education apply cognitive psychology principles in their teaching practices, and to evaluate the perceived effectiveness of these principles in enhancing student learning. Methods This mixed method study was conducted on faculty members of public and private sector universities. Focused group discussions were conducted, and the responses of the participants were measured on a 5-point- Likert Scale. The instrument was tested for validity and reliability by measuring Cronbach’s alpha and performing exploratory factor analysis. Mann–Whitney U test was applied on the attributes scores to compare 13 attributes by institutions, designations and domains. Results One theme, 7 sub-themes and 19 categories were identified from the focused group discussion. as Kaiser- Meyer-Olkin index was 0.88 and Bartlett’s test of sphericity was significant. Reliability was established at 0.95. The faculty self-perceived practicing psychology principles mostly scored in the middle level, where they think they apply less strategies. While highly significant results were noted for using Meyer’s multimedia principles. Conclusion Faculty members followed the principles of cognitive psychology to some extent. However, they all felt that using these principles can transform the teaching and learning of medical education.

Published
2024
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36. Histopathology and Immunohistochemical Profile of Breast Cancer amongst Young Females below 40 years of Age: A Cross-sectional Study

Author

Prakhar Garg, Amit Varma, Sasmal Prasanjit Rampada, Pooja Nathani, Syed Sarfaraz Ali, Kamal Malukani, and Nancy Jain

Subjects
histological grade, invasive breast carcinoma, molecular subtypes, triple-negative breast cancer, Medicine
Abstract

Introduction: Breast cancer is a diverse and heterogeneous disease affecting women in both developing and developed countries. In 2022, Global Cancer Observatory (GLOBOCAN) reported 2.3 million cases of breast carcinoma, which accounted for 11.6% of all cancer cases worldwide. The incidence of breast cancer in India has been steadily rising and has surpassed cervical cancer as the most common cancer in women. Invasive ductal carcinoma is the most common histologic type found in all age groups. Grading is done according to Elston Ellis’s modification of the Scarff Bloom Richardson Scoring system. Hormonal {Estrogen Receptor (ER), Progesterone Receptor (PR)} and Human Epidermal growth factor Receptor-2 (HER2/neu) immunohistochemical expression are used for molecular typing of breast carcinoma. Young women account for up to 25% of all breast carcinoma cases in India. Aim: To assess the clinical symptoms, histological type, grade, Immunohistochemistry (IHC) profile (ER, PR, HER2neu), and their relationships in females below 40 years of age with breast carcinoma. Materials and Methods: An ambispective cross-sectional study with a total of 99 cases of breast carcinoma in young females were included in the study, which was conducted in the Department of Pathology at Sri Aurobindo Medical College and PG Institute in Indore, Madhya Pradesh, India, between April 2022 and March 2024. Biopsies, lumpectomy, and total or modified mastectomy specimens were fixed in formalin and paraffin embedded. Sections were stained with Haematoxylin and Eosin (H&E) and IHC markers ER, PR, and HER2neu using Estrogen Receptor Alpha Antibody (EP1), EP2, and EP3 antibodies, respectively, on a fully automated workstation. Carcinomas were classified according to the World Health Organisation (WHO) classification and graded based on Elston Ellis’s modification of the Scarff Bloom Richardson System Scoring. The proportion of tumour cells and intensity were studied, and evaluation was done with appropriate controls. Results: The most common symptom was a breast lump (84.84%), while Invasive breast cancer of {No Special Type (NST)} was the most common histologic type (96.96%) and histological Grade III was predominant (53.53%) in the majority of patients. ER, PR, and HER2neu were positive in 57.57%, 51.51%, and 33.33% of cases, respectively. HER2neu expression was negative in the majority of cases (66.66%). The most common molecular subtype was Luminal A (35.35%), followed by Triple-negative Breast Cancer (TNBC) (29.29%), Luminal B (22.22%), and the least common was the HER2neu enriched subtype (13.13%). Conclusion: Invasive breast carcinoma (NST) is the most common histologic type, with higher histologic grade and a greater number of triple-negative cases seen in young females (

Published
2024
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37. Characteristics and motivational factors of candidates applying to the Master in Health Professions Education (MHPE) program

Author

Samira Adnan, FCPS, Shaur Sarfaraz, MHPE, Syeda K. Ali, PhD, and Fatima Abid, PhD

Subjects
Applicant, Characteristic, Health Professions Education, Master's program, Motivational factor, Medicine (General), R5-920
Abstract

الملخص: أهداف البحث: هدفت الدراسة لتحديد الخصائص والعوامل التحفيزية للمتقدمين لبرنامج الماجستير في تعليم المهن الصحية في جامعة تابعة للقطاع العام، كراتشي. طرق البحث: شملت هذه الدراسة الوصفية المقطعية، التي أجريت من سبتمبر حتى أكتوبر 2022، جميع المتقدمين البالغ عددهم 60 متقدم لبرنامج الماجستير في تعليم المهن الصحية في جامعة جناح السند الطبية. تم توزيع استبانة عبر نموذج غوغل تضمن أسئلة لجمع المعلومات الديموغرافية، وعدد قليل من الأسئلة ذات الخيارات للاختيار، و34 عبارة للاستجابة بمقياس من خمس نقاط. كما تضمن سؤالا مفتوحا حول العوامل التحفيزية. تم تحليل البيانات باستخدام برنامج الحزمة الإحصائية للعلوم الاجتماعية واستخدام اختبار ''تي'' للعينات المستقلة لتحديد الفروقات بين مجموعات المتقدمين بناء على العوامل الديموغرافية. النتائج: استجاب 48 مشاركا، بمتوسط عمر 35.8±7.3 سنة، وكان الغالبية من الذكور (81.3%)، وخريجي برامج الطب وطب الأسنان (عددهم 29)، ويعملون في أقسام التعليم الطبي (عددهم 36). معظم المتقدمين أرادوا الانضمام إلى البرنامج لتحسين مهاراتهم في التدريس، خاصة لتدريس المهارات النفسحركية (96%) والبحث التعليمي (90%). علمت الأغلبية (64.6%) عن البرنامج من خلال الزملاء، بينما اختار 29.9% متابعة البرنامج بشكل مستقل. كانت دوافعهم الرئيسية نابعة من عدم قدرتهم على الحصول على قبول في أماكن أخرى (متوسط الدرجات 4.58±0.87) والاعتقاد بأن الجهد المطلوب للحصول على الدرجة كان ضئيلا (متوسط الدرجات 4.42±0.94). بالنسبة لخريجي الطب وطب الأسنان، ظهرت استجابات متشابهة، مما أظهر ارتباطا ذا دلالة إحصائية. ومع ذلك، كان هناك عدم اتفاق مشترك حول هيكل البرنامج وأعضاء هيئة التدريس كعوامل تحفيزية. الاستنتاجات: كانت الدوافع الرئيسية للحصول على درجة الماجستير في تعليم المهن الصحية هي تحسين مهارات التدريس. كانت الدوافع للتقدم لهذا البرنامج تحديدا هي الفشل في القبول في أماكن أخرى والاعتقاد بأن الجهد المطلوب لإكمال هذا البرنامج كان ضئيلا. يجب أن يتم نشر أهداف وغرض البرنامج بشكل أفضل مع إدخال تغييرات في معايير وإجراءات القبول من أجل تشجيع الأفراد الذين لديهم دافع لمتابعة التعليم الطبي كمسار مهني. Abstract: Objective: To determine characteristics and motivational factors of applicants applying for MHPE program at a Public-sector University, Karachi. Methods: This cross-sectional descriptive study, conducted from September till October 2022, included all 60 applicants of MHPE program at Jinnah Sindh Medical University. The survey questionnaire was administered (google form) and comprised questions seeking demographic information, few questions with options to select and 34 statements to be responded on 5-point scale. One open-ended question asking motivational factor/s. Data was analyzed using SPSS and independent sample t-test was used to determine the differences between groups of applicants based on demographic factors. Results: 48 participants responded, mean age was 35.8 ± 7.3 years, with predominantly males (81.3%), graduates of medical and dental programs (n = 29), and employed in medical education departments (n = 36). Most of the applicants wanted to join the program to improve their teaching skills, especially psychomotor skills teaching (96%) and educational research [90%]. Majority (64.6%) learned about the program through peers, whereas 29.9% independently chose to pursue the program. Their primary motivation stemmed from their inability to secure admission elsewhere and the belief that minimal effort was needed to acquire the degree (mean score 4.58 ± 0.87 and 4.42 ± 0.94), respectively. For medical and dental graduates, comparable responses emerged, displaying a statistically significant correlation (p < 0.05). However, there was mutual disagreement regarding the program's structure and faculty as motivating factors. Conclusion: Primary motivations to earn an MHPE degree were to improve teaching skills. Motivations to apply to this particular program was the failure to be accepted elsewhere and the perception that minimal efforts were needed to complete this program. The program goals and purpose need to be better propagated along with changes in the admission processes in order to encourage those individuals to apply who are motivated to pursue medical education as a career.

Published
2024
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38. Indirect adaptive observer control (I-AOC) design for truck–trailer model based on T–S fuzzy system with unknown nonlinear function

Author

Muhammad Shamrooz Aslam, Hazrat Bilal, Wer-jer Chang, Abid Yahya, Irfan Anjum Badruddin, Sarfaraz Kamangar, and Mohamed Hussien

Subjects
Adaptive observer control, Indirect method, T–S fuzzy models, Lyapunov–Krasovskii candidate, Feedback control design, Electronic computers. Computer science, QA75.5-76.95, Information technology, T58.5-58.64
Abstract

Abstract Tracking is a crucial problem for nonlinear systems as it ensures stability and enables the system to accurately follow a desired reference signal. Using Takagi–Sugeno (T–S) fuzzy models, this paper addresses the problem of fuzzy observer and control design for a class of nonlinear systems. The Takagi–Sugeno (T–S) fuzzy models can represent nonlinear systems because it is a universal approximation. Firstly, the T–S fuzzy modeling is applied to get the dynamics of an observational system in order to estimate the unmeasurable states of an unknown nonlinear system. There are various kinds of nonlinear systems that can be modeled using T–S fuzzy systems by combining the input state variables linearly. Secondly, the T–S fuzzy systems can handle unknown states as well as parameters known to the indirect adaptive fuzzy observer. A simple feedback method is used to implement the proposed controller. As a result, the feedback linearization method allows for solving the singularity problem without using any additional algorithms. A fuzzy model representation of the observation system comprises parameters and a feedback gain. The Lyapunov function and Lipschitz conditions are used in constructing the adaptive law. This method is then illustrated by an illustrative example to prove its effectiveness with different kinds of nonlinear functions. A well-designed controller is effective and its performance index minimizes network utilization—this factor is particularly significant when applied to wireless communication systems.

Published
2024
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39. Association between CYP1A2 gene variants −163 C/A (rs762551) and −3860 G/A (rs2069514) and bladder cancer susceptibility

Author

Muhammad Sarfaraz Iqbal, Nimra Sardar, Kaoqing Peng, Layla A. Almutairi, Xialo Duan, Fouzia Tanvir, Kotb A. Attia, Gouhua Zeng, and Di Gu

Subjects
CYP1A2-3860 G/A bladder cancer (BLCA), Muscle-invasive bladder cancer (MIBLCA), Non-muscle invasive bladder cancer (NMIBLCA), Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Background Bladder cancer (BLCA) poses a significant global health challenge due to its high incidence, poor prognosis, and limited treatment options. Aims and objectives This study aims to investigate the association between two specific polymorphisms, CYP1A2-163 C/A and CYP1A2-3860G/A, within the Cytochrome P450 1A2 (CYP1A2) gene and susceptibility to BLCA. Methods The study employed a case-control design, genotyping 340 individuals using Polymerase Chain Reaction-High-Resolution Melting Curve (PCR-HRM). Various genetic models were applied to evaluate allele and genotype frequencies. Genetic linkage analysis was facilitated using R packages. Results The study reveals a significant association with the − 163 C/A allele, particularly in the additive model. Odds ratio (OR) analysis links CYP1A2-163 C/A (rs762551) and CYP1A2-3860G/A(rs2069514) polymorphisms to BLCA susceptibility. The rs762551 C/A genotype is prevalent in 55% of BLCA cases and exhibits an OR of 2.21. The A/A genotype has an OR of 1.54. Regarding CYP1A2-3860G/A, the G/A genotype has an OR of 1.54, and the A/A genotype has an OR of 2.08. Haplotype analysis shows a predominant C-C haplotype at 38.2%, followed by a C-A haplotype at 54.7%, and a less frequent A-A haplotype at 7.1%. This study underscores associations between CYP1A2 gene variants, particularly rs762551 (CYP1A2-163 C/A), and an increased susceptibility to BLCA. Haplotype analysis of 340 individuals reveals a predominant C-C haplotype at 38.2%, followed by a C-A haplotype at 54.7%, and a less frequent A-A haplotype at 7.1%. Conclusion In conclusion, the − 163 C/A allele, C/A genotype of rs762551, and G/A genotype of rs2069514 emerge as potential genetic markers associated with elevated BLCA risk.

Published
2024
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40. Prediction of land use changes at a metropolitan city using integrated cellular automata: past and future

Author

Shahfahad, Mohd Waseem Naikoo, Tanmoy Das, Swapan Talukdar, Md. Sarfaraz Asgher, Asif, and Atiqur Rahman

Subjects
Future urban expansion, land use change, artificial neural network, cellular automata, delhi, Ecology, QH540-549.5, Geology, QE1-996.5
Abstract

In the cities of developing countries like India, rapid and uncontrolled urbanization has been taking place due to continuous population growth in last few decades. As a result, land use/land cover (LU/LC) is changing very fast in the cities of developing countries. Therefore, this study aims to examine the changes in LU/LC pattern in Delhi during 1991–2018 and simulate the future LU/LC pattern of Delhi for 2030. The LU/LC pattern mapping was done from Landsat datasets using k-means clustering technique. The cellular automata (CA) technique was integrated with artificial neural network (ANN) for simulating the future LU/LC patterns. The projected LU/LC pattern shows that Delhi’s built-up area will increase to nearly 60% of the total area of city, while cropland and open land will decrease to 19.86 and 0.15%, respectively. The highest increase in built-up area was observed in the northern, western, and south-western sub-districts of Delhi. Outcomes of the study may be used for future land use planning in Delhi and other cities. In addition, they can also provide valuable insights for the development of transportation network and other facilities and amenities in the areas of future urban expansion.

Published
2024
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