Abstract

Automated text generation for low resource (LR) languages is a critical area of research because of lack of contributors to encyclopedic texts, notably on Wikipedia. The majority of the work done so far on Wikipedia text generation has concentrated on creating English-only Wikipedia articles by summarizing English reference articles. Monolingual text generation is unable to address this issue for low-resource languages due to the lack of reference materials. To start addressing these problems, we propose a benchmark dataset called XWikiRef that consists of ∼69K in Wikipedia articles from five different domains and eight different languages. Utilizing this dataset, we train a two-stage system that outputs a section-specific LR summary from an input of a set of citations and a section title. One crucial aspect of content organization is the creation of article outlines, which summarize the primary topics and subtopics covered in an article in a structured manner. We introduce a pipeline called XOutlineGen, which generates cross-lingual outlines for encyclopedic texts from reference articles. XOutlineGen uses the XWikiRef dataset, which consists of encyclopedic texts generated from reference articles and section titles. Our pipeline employs this dataset to train a two-step generation model, which takes the article title and set of references as inputs and produces the article outline. Commonsense question-answering (QA) methods combine the power of pre-trained Language Models (LM) with the reasoning provided by Knowledge Graphs (KG). A typical approach collects nodes relevant to the QA pair from a KG to form a Working Graph (WG) followed by reasoning using Graph Neural Networks (GNNs). This faces two major challenges: (i) it is difficult to capture all the information from the QA in the WG, and (ii) the WG contains some irrelevant nodes from the KG. To address these, we propose GrapeQA with two simple improvements on the WG: (i) Prominent Entities for Graph Augmentation identifies relevant text chunks from the QA pair and augments the WG with corresponding latent representations from the LM, and (ii) Context-Aware Node Pruning removes nodes that are less relevant to the QA pair. We evaluate our results on OpenBookQA, CommonsenseQA and MedQA-USMLE and see that GrapeQA shows consistent improvements over its LM + KG predecessor (QAGNN in particular) and large improvements on OpenBookQA. We utilize the idea of relevance scoring from this work in our next work which is called XWikiGen for performing neural extractive summarization With this study, we propose XWikiGen, a task of cross-lingual multi-document summarization of text from numerous reference articles written in different languages to produce Wikipedia-style material. The suggested approach is built on the novel idea of using neural unsupervised extractive summarization to roughly select salient information and then using a neural abstractive model to produce the section-specific text. Extensive experiments have revealed that multi-domain training generally outperforms a multi-lingual and multi-lingualmulti-domain perform best, even better then previous two settings. Overall, we propose a new dataset called XWikiRef for the task of encyclopedic text generation, a 2 stage pipeline XOutlineGen to generate article outline from references and a cross-lingual multi-document summarization based 2 stage pipeline XWikiGen to generate Wikipedia style text. Along with these, we also explore the idea of relevance scoring first in the domain of question answering with reasoning (GrapeQA) and then in the context of unsupervised extractive summarization.

Abstract

Metaverse has gained massive popularity these days owing to its potential in various industries and research fields such as the health sector, online social interaction, gaming and entertainment, education, eCommerce transactions, etc. Some of the challenges to be addressed in building a Metaverse, including human-computer interactions and scene understanding, rely on AI techniques for solutions. However, most of these methods rely on supervised learning that requires labor-intense annotated data and have limited generalizability. Self-supervised learning methods offer a viable alternative to overcome these limitations as well as leverage large amounts of readily available unlabeled raw data, which is often more abundant and easier to collect. These methods can learn to capture temporal or spatial relationships within data, modeling contextual information, which is valuable for several computer vision tasks. Hence, these methods can be useful in addressing the challenges of building the Metaverse. Within the Metaverse, users can interact with each other and a digital environment through avatars. Using UV parameterized maps of avatars, textures can be accurately applied, resulting in realistic skin, clothing, and other visual details. They also play a crucial role in the creation of virtual fashion and design within the Metaverse. This can also benefit the E-Commerce industry, particularly fashion and apparel, which have experienced tremendous growth in recent years. The Metaverse can provide immersive virtual environments for virtual try-on where users can explore and interact with products more realistically and engagingly than traditional online shopping. Given the above advantages, we intend to address the following two challenges in this thesis. First, we explore self-supervised data-driven methods for UV parameterization of general objects. The existing methods for surface parameterization of arbitrary 3D objects face challenges when dealing with closed surfaces and regions of extreme extrinsic curvature. Mapping a surface from 3D to 2D almost always introduces a certain amount of distortion, and the aim is to keep this distortion as low as possible. Finding optimal seams that lead to low distortion is another challenge. We present a novel framework for learning the discretization-agnostic surface parameterization of arbitrary 3D objects with closed and open surfaces. We evaluate our framework on multiple 3D objects from the publicly available dataset and report a comparison with conventional methods. Secondly, utilizing self-supervised methods, we aim to innovate a solution for a 3D virtual tryon system. The goal is to retarget real, non-parametric garment meshes over a target human body (parametric or non-parametric). This 3D virtual try-on system needs to generalize to arbitrary body shapes and poses, modeling topological differences among various categories of garments, along with realistic deformations arising out of the physical interaction with the underlying body and resolving the penetration/intersection of the garment with the underlying body. These systems need to run in real time with very less delay. We propose a self-supervised method for draping non-parametric, 3D garment meshes by first obtaining the initial alignment between the garment and the human body by establishing correspondences via Isomap Embeddings. Further, this coarse retargeting is refined by training an MLP that preserves the geometry of the garments guided by our novel losses. We propose a wrinkle generation module to obtain realistic details on the draped garments. We also contribute a new dataset of real-world reposed garments with realistic noise and topological deformations. Finally, we discuss the limitations of our work and lay down the potential solutions that can be explored. We also discuss the future directions that can be pursued based on the findings of our work. We believe this thesis advances the field of virtual try-on systems significantly while providing a learningbased solution for parameterization.

Abstract

Genome integrity is seriously threatened by UV radiation’s ability to cause damage in DNA. CPD and 6-4PP are two of the most frequent lesions brought on by Ultraviolet radiation. CPD is a DNA lesion occurring from the covalent linking of nearby pyrimidine bases through their C5 and C6 carbons, generating a cyclobutane ring. In contrast, 6-4PP develops when neighbouring pyrimidine bases in DNA become crosslinked following UV exposure, producing a covalent bond between their C6 and C4 carbons. If left untreated, these lesions can result in mutations and even the development of cancer. Clarifying the DNA repair process depends on our ability to comprehend the mechanisms underpinning DNA lesion identification. DNA damage repair proteins such as XPC/Rad4 recognize and facilitate repair of these damages. In order to better understand how XPC/Rad4 recognises DNA lesions in Nucleotide Excision Repair (NER), this thesis will focus on how it interacts with CPD and 6-4PP. The highly conserved repair process known as NER is in charge of locating and eradicating diverse DNA damages. The current thesis looks at the mechanism, energetics and dynamics of Rad4 sequence-specific lesion recognition. The flipping of a pair of partner bases that are present opposite to the lesion (CPD or 64PP) is one of the crucial biochemical steps that take place while XPC or Rad4 tries to identify and bind to a DNA lesion. Major events in NER like insertion of BHD3-β hairpin, flipping of partner bases and Rad4/XPC association with the damaged DNA were studied by formulating relevant reaction coordinates or Collective Variables. These CVs were then used in Molecular Dynamics and enhanced sampling method, Umbrella Sampling to produce energy profiles for the aforementioned events. A deeper understanding of these energy profiles reveal that insertion of BHD3-β hairpin takes place only after the flipping of partner bases has taken place and a cavity is induced in the active site of the DNA but 6-4PP containing complex promotes the insertion of BHD3-β hairpin with higher ease and flexibility compared to a CPD containing complex.This study also reveals that flipping of 5’-dA partner base takes place before flipping of 3’-dA partner base. And these observations were consistent for both lesions, CPD and 64PP. The results of this study will be useful to improve our knowledge of the molecular processes behind Rad4’s identification of DNA lesions and give light on the critical steps in the NER pathway. Finally, this information may aid in the creation of focused therapeutic approaches for preventing and treating UV-induced DNA damage, enhancing our capacity to protect genomic integrity and lessen the dangers of UV radiation exposure

Abstract

The semiconductor industry is driven by the need to design smaller, faster and low power con- suming circuits. A comparator is an integral part of any electronic system and by default the comparators exhibit hysteresis phenomenon. A Schmitt trigger is the most commonly used comparator in circuit design. Rigorous efforts have been made to speed up the circuit perfor- mance by various techniques at the system, circuit and transistor level and they have demon- strated incremental improvement. However, all these efforts are focused on reducing the switch- ing time between the logic levels and shortening the width of hysteretic loop while maintaining noise immunity. This thesis presents a new paradigm shift from the conventional techniques of circuit speed up wherein the hysteretic device itself is replaced with a proteretic one and efforts to improve the circuit speed have been demonstrated successfully. Hysteresis has an inherent switching delay, whereas, proteresis or inverse hysteresis demonstrates an early response to the switching action of the circuit. Proteresis is a known phenomenon across various domains like pharmacokinetics (PK) and pharmacodynamics (PD) drugs, ferro-electric materials and optical bi-stable devices, but their impact and study in area of Integrated circuits has been very limited. This is because proteresis is induced by feed forwarding the input to couple of hysteretic devices (Schmitt trigger) and this leads to output of circuit being stable for a small input ranges and prevalent instability over the remaining input range. However, this thesis proposes a novel mechanism of stabilizing the proteretic output over a wide input voltage range and hence leverages the benefit of early switching characteristics for improving the circuit speed by a factor of 25%. A detailed mathematical model for proteretic circuit design over various input ranges is presented and the trade-off in terms of area and power is also discussed with the ramp generator as a design example. Hysteretic and proteretic devices are bi-stable and their interstate switching depends on triggering voltages. Conventionally, devices can be classified to work as either hysteretic or proteretic. This thesis proposes the first attempt to describe a circuit that switches between two opposite characteristics of hysteresis and proteresis to present a new circuit called Prohys switch. The output of prohys switch demonstrates a limited amount of randomness in its first cycle of operation and this factor is a strong reason to employ a prohys switch for designing a Physical unclonable Function (PUF). In recent times PUF’s has gained immense popularity for securing the IC’s by providing unique identification code to each chip. The key design parameters of a PUF are uniqueness and reliability and designing a highly efficient PUF with optimal values of uniqueness and reliability is quite a challenge. Reliability depends on the chip’s ability to resist changes to supply voltage and temperature variations, whereas, uniqueness depends on process variations during chip fabrication. Multiple PUF designs that employ reliability enhancement circuits and security algorithms achieve these design characteristics. Nonetheless, these techniques are design overheads. Finally, this thesis presents a novel PUF based on the proHys switch called the prohys PUF. The proposed prohys PUF befittingly satisfies both uniqueness and reliability criteria, without any additional circuitry or security algorithms.

Abstract

The safety improvements in the medical systems or devices, specifically non-invasive patient monitoring systems (PMS) and point-of-care (POC) devices of human health monitoring (HHMS) systems, are in great need to perform precise measurements of vital parameters with uninterrupted continuous monitoring for diagnosis of significant human health ailments. During critical or non-critical nursing times, frequent monitoring of short and long-duration measures of vital parameters (like ECG, EEG, Respiratory, SpO2, Blood-pressure, Temperature, etc.) and non-vital parameters (like Glucose levels, Hemoglobin, Urea in blood, etc.) will allow for better nursing and early detection of diseases like cardiovascular diseases, respiratory diseases, liver dysfunction, diabetes, renal diseases, and other psychological disorders. A non-invasive medical instrumentation system for accomplishing these tasks must meet several criteria like accuracy and precision of the measurement, reduction of false alarms, effective detection of faults, and fault tolerance to systemic or random failures with an uninterrupted performance during nursing times. Many research groups have made many efforts worldwide to make strategies and guidelines and standardize the procedures for medical systems based on safety design approaches to raise alerts for any deviance during non-invasive monitoring of health parameters. In addition, health monitoring instruments should be portable, low-cost, and reliable over time. Many modes of diagnosis and related instrument maintenance procedures satisfy these criteria. However, most of them are still in the improvement stage for developing resilient instruments for integrating with critical auto-robotic surgery instruments with minimal human intervention. Several approaches have been proposed, out of which safety-related design approaches like using 2oo2, 2oo3, 1oo2, etc., along with AI-based data analytics, have the advantage of meeting these rigorous demands in fundamental safety improvements of Medical Systems. Based on the safety-related design 2oo2 concept, a configurable system prototype of the cardiac health monitoring system (CHMS) is developed and evaluated to meet the set objectives, such as fault detection effectiveness and fault tolerance with improved safety configurability. This configurable system uses various sensors to collect the bio-medical data in parallel. Primarily, three diverse sensors are used non-invasively in sensing the bio-signals in different forms like electric potential, light, and sound signals for computations. These diverse sensors are used to detect biomedical signals to obtain data from electrocardiogram (ECG), Photo-plethysmogram (PPG), and Phonocardiogram (PCG). Therefore, the accuracy of the vital estimates and the fundamental safety improvements were evaluated using this multimodal system with AI-based fault detection and predictive maintenance techniques. Traditional statistical and AI-based techniques have acquired authentic measurements of human health parameters from diverse signals received simultaneously from various sensing circuits like PPG/ECG/EEG. Secondly, these bio-signals are calibrated independently with known algorithms in diverse. Finally, these obtained parameters, along with the built-in-test (BIT) system for health signals, are processed with implemented safety functions, and the algorithms to generate the correct human health parameters and prognosticate abnormalities of human health 200 patients were available for instrument evaluation trials for HR parameter monitoring and tested after taking informed consent. A MATLAB-based CHMS tool is developed for configurable and then implemented on a field-programmable gate array (FPGA) to minimize the designed circuitry with improved resiliency of the instrument system. The CHMS has been configured to 2oo2 with the selected HR parameter to estimate the system's availability and health. The collected HR output was subjected to data analytics against individually collected data. We found a significant reduction in the generation of unimportant alarms and increased uninterruptable System availability by 45% to 55%, along with normal and abnormal artifact data. The measured normal artifacts are more than 99% accurate and are used for prognosis. The abnormal data is used for edge-AI-based analytics to infer the system's health for prescriptive maintenance. An experimental study has been carried out to effectively segregate normal and abnormal signals in 2oo2 and 2oo3 configurations. A detailed analysis is carried out in various sensor configurations as proposed. Similarly, in the 2oo3 configuration, we significantly improved the system's availability from 55% to 95% by eliminating spurious alarms with reduced downtime and improved accurate data vital parameters. Further, a reliability assessment is performed on CHMS on identified parameters, such as measuring Avai

Abstract

Blockchain and Reinforcement Learning (RL) are two game-changing research areas that have received a lot of attention recently. In recent years, significant advances in RL have resulted in tremendous success in solving various sequential decision-making problems in machine learning. The two most successful RL applications are discussed in this work, unmanned aerial vehicles, and blockchain. Blockchain is a distributed ledger technology with its first application in Bitcoin. The main challenge in blockchain-based cryptocurrencies is to provide a distributed trust environment with high security like in a centralized financial system. The current throughput of Bitcoin is around 4 to 7 transactions per second and confirmation latency is about one hour. If Bitcoin has to go mainstream, the throughput has to be in the order of thousands of transactions per second with very low latency in the order of a few seconds. Recent advances in blockchain research proposed consensus algorithms that scale bitcoin, such as sharding and Prism-based blockchain. However, the security of Bitcoin is very high that it can tolerate up to 50% adversarial nodes and avoids double spending attacks. The current blockchain size is over 260 GB and is growing at an astonishing rate imposing a huge storage requirement on the nodes. Recent developments improving the Bitcoin consensus have shown that there is a tradeoff between decentralization, scaling, and security. In order to scale blockchain, we leverage coding theory and RL in this thesis. Due to the increasing storage requirement for blockchains, the computation can be afforded by only a few miners. Sharding has been proposed to scale blockchains so that the storage and transaction efficiency of the blockchain improves at the cost of a security guarantee. Incorporating coding theory into existing consensus algorithms has demonstrated improvements in terms of storage efficiency and low latency. A Secure-Repair-Blockchain (SRB) is proposed which aims to decrease the storage cost at the miners. In addition, SRB also decreases the bootstrapping cost, which allows for new miners to easily join a sharded blockchain. In order to reduce storage, coding-theoretic techniques are used in SRB. In order to decrease the amount of data that is transferred to the new node joining a shard, the concept of exact repair secure regenerating codes is used. The proposed blockchain protocol achieves lower storage than those that do not use coding and achieves lower bootstrapping costs as compared to the different baselines. Prism is a recent blockchain algorithm that achieves the physical limit on throughput and latency without compromising security. However, like the traditional blockchain systems, Prism also has a trade-off between security, latency, and cost. In recent days, reinforcement learning approaches are investigated in traditional blockchains, to improve performance. In this work, we apply Deep Reinforcement Learning (DRL) to one of the promising blockchain protocols, Prism, to optimize its performance. We propose a Deep Reinforcement Learning-based Prism Blockchain (DRLPB) scheme which dynamically optimizes the parameters of Prism blockchain and helps in achieving a better performance. In DRLPB, we apply two widely used DRL algorithms, Dueling Deep Q Networks (DDQN) and Proximal Policy Optimization (PPO). This work presents a novel approach to applying DDQN and PPO to a blockchain protocol and comparing the performance. The analysis of Prism in terms of latency, and security level considering other blockchain parameters is provided. Using the analysis, the DRLPB scheme adapts the Prism blockchain parameters to enhance the security upto 84% more than Prism, while still preserving the performance guarantees of Prism. The recent advancements in the field of Internet of Things (IoT) motivate the development of a secure infrastructure for storing and sharing vast amounts of data. Blockchain, a distributed and immutable ledger, is best known as a potential solution to data security and privacy for IoT. The scalability of blockchain, which should optimize the throughput and handle the dynamics of the IoT environment, becomes a challenge due to the enormous amount of IoT data. The critical challenge in scaling blockchain is to guarantee decentralization, latency, and security of the system while optimizing the transaction throughput. this paper presents a deep reinforcement learning (DRL)-based performance optimization for blockchain-enabled IoT. We consider one of the recent promising blockchains, Prism as the underlying blockchain system because of its good performance guarantees. We integrate the IoT data to Prism Blockchain and optimize the performance of the system by leveraging Proximal Policy Optimization (PPO) method. The DRL method helps to optimize the blockchain parameters like mining rate and mined blocks to adapt to the environment dynamics of

Abstract

Hardware accelerators are being designed to offload compute-intensive tasks such as deep neural networks from the CPU to improve the overall performance of an application, specifically on the performance-per-watt metric. With the evolution of speech recognition based applications, many deep learning models for Automatic Speech Recognition have been proposed. Encoder-decoder-based sequenceto-sequence models such as the Transformer model have demonstrated state-of-the-art results in end-toend automatic speech recognition systems (ASRs). However, the Transformer model being intensive on memory and computation poses a challenge for an FPGA implementation. This thesis proposes an end-to-end architecture to accelerate a Transformer for an ASR system. The host CPU orchestrates the computations from different encoder and decoder stages of the Transformer architecture on the designed hardware accelerator with no necessity for intervening FPGA reconfiguration. The intermediate stages, like data pre-processing and feature extraction, are performed on the host while the complex recognizer, i.e., the Transformer model, is offloaded onto an FPGA. The communication latency is hidden by prefetching the weights of the next encoder/decoder block while the current block is being processed. The larger computations in the model are split across both the Super Logic Regions (SLRs) of the FPGA, mitigating the inter-SLR communication. The proposed design presents an optimal latency, exploiting the available resources. The accelerator design is realized using Vitis high-level synthesis tool, using OpenCL, a language for heterogeneous computing, and evaluated on an Alveo U-50 FPGA card. The end-to-end ASR system has a latency of ∼120ms, which is suitable for real-time applications. The design demonstrates an average speed-up of 32× compared to an Intel Xeon E5-2640 CPU and 8.8× compared to NVIDIA GeForce RTX 3080 Ti Graphics card for a 32-bit floating point single precision model.

Abstract

Monitoring and analyzing air quality is a challenging task without understanding influencing factors. Conventional air pollution monitoring is limited to few locations and accessing data is difficult. Internet of Things (IoT) provides a solution by enabling cost-efficient networks of particulate matter (PM) monitoring devices that are easily connected to the internet. PM monitoring portable sensors combined with IoT offer a more efficient and widespread PM monitoring solution, overcoming issues with bulky and expensive traditional systems. This thesis focuses on development and deployment of PM monitoring device and the establishment of a dense PM monitoring network. This thesis focuses mainly on four aspects. In the first aspect, the development of an end-to-end low-cost IoT system for a densely deployed PM monitoring network carried out an extensive field study in a region of the Indian metropolitan city of Hyderabad. A total of 49 devices were deployed in an area of 4 km2 , with 43 of them developed specifically for this study and the remaining six obtained from external sources, a density never realized in any metropolitan city worldwide and mostly in developing countries like India in the past. A total of 15 devices were connected to Wi-Fi, primarily within the campus area and wherever Wi-Fi connectivity was available, whereas 34 devices were equipped with 2G eSIM. The low-cost sensors were carefully calibrated to account for seasonal variations by utilizing a highly precise reference sensor. Also, a robust device was made that can cache data to avoid loss due to communication outages. The second aspect of this thesis focuses on gathering a significant quantity of data, nearly 20.7 million. This unique dataset offers great opportunities for future research and is examined to evaluate whether a concentrated deployment of PM monitoring devices was necessary. Third, various analytical methods, such as mean, variance, spatial interpolation, and correlation, were utilized to produce informative findings about the fluctuations of PM over time and across seasons. In order to understand the impact of firecracker detonations during Diwali festival evenings, a spatio-temporal analysis of PM values was conducted. As part of our analysis, we also tried to answer an important question concerning decision-makers about the optimum density required for effectively monitoring street-level pollution. For the considered scenario, we demonstrated that PM monitoring devices should be deployed at most 350 m apart to accurately capture the spatial variability of PM. Finally, this thesis examines various challenges encountered during the pre-deployment and postdeployment of PM monitoring devices. It addresses issues such as the design of low-cost devices, pre-deployment calibration, and seasonal calibration. Also explores challenges related to power supply, theft, environmental factors affecting sensor performance, and hardware failures. Additionally, the study investigates hardware reset and corrupt or redundant data. This thesis examines the challenges that arise during the deployment of PM monitoring devices and to propose feasible solutions to mitigate these issues, thereby offering valuable insights into the effective implementation of PM monitoring technology. Overall, the thesis highlights the importance of dense deployment and addresses the challenges to ensure reliable and accurate data collection.

Abstract

GPCRs are the most prominent family of membrane proteins that serve as major targets for one-third of the drugs produced. A detailed understanding of the molecular mechanism of drug-induced activation and inhibition of GPCRs is crucial for the rational design of novel therapeutics. The binding of the neurotransmitter adrenaline to the β2-adrenergic receptor (β2AR) is known to induce a flight or fight cellular response, but much remains to be understood about binding-induced dynamical changes in β2AR and adrenaline. In Chapter 3, we examine the potential of mean force (PMF) for the unbinding of adrenaline from the orthosteric binding site of β2AR and the associated dynamics using umbrella sampling and molecular dynamics (MD) simulations. The calculated PMF reveals a global energy minimum, which corresponds to the crystal structure of β2AR-adrenaline complex, and a meta-stable state in which the adrenaline is moved slightly deeper into the binding pocket with a different orientation compared to that in the crystal structure. The orientational and conformational changes in adrenaline during the transition between these two states and the underlying driving forces of this transition are also explored. Based on clustering of MD configurations and machine learning-based statistical analyses of time series of relevant collective variables, the structures and stabilizing interactions of these two states of the β2AR-adrenaline complex are also investigated. In Chapter 4, the PMF of G-Protein dissociation, and GDP dissociation are studied. In order to study the entire GPCR activation pathway, models have been constructed for 3 different states (inactive, intermediate, and active states) of the G-Protein-β2AR complex. The results suggest that the binding of GDP to G-Protein favours G-Protein dissociation from β2AR. In summary, the findings enhance our understanding of GPCR activation and the advancement of new therapies aimed at GPCRs by offering comprehensive insights into conformational changes, energetics, and critical residues involved. The methodologies and discoveries presented in this study establish the groundwork for an automated process to explore ligand dynamics and interactions between drugs and GPCRs, thereby facilitating future research endeavors in this domain.

Abstract

The satellite imagery has different types of objects like buildings, vegetation, lakes, roads, grounds etc. Each type of object has unique characteristicslike texture, colour,spatial arrangement etc that differentiates them from other types of objects. Using a single method to extract all these types of objects might not be logical. Detecting buildings from satellite imagery is of major importance for supporting government related activities and a great support in crisis situations for disaster management. This work presents a new way of automating the extraction of buildings from high resolution satellite images using Object Based Image Analysis (OBIA). The proposed algorithm utilizes an active contour model called Chan-Vese segmentation that is capable of accurately locating various objects in the image. Additionally, to ensure accuracy, the algorithm employs Normalized Difference Vegetation Index (NDVI) mask to remove vegetation areas. Moreover, these detected objects pass through a careful filtering process based on regional characteristics like minimum area and object width thereby improving the precision of the identified structures. The correctness of results is confirmed by conducting rigorous quantitative assessments for validation purposes. This means that not only does this comprehensive approach guarantee error-free building identification but also suggests a strong framework for automatic building extraction from high resolution satellite imagery.