BITS Faculty Publications

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Subject: search.filters.subject.Algorithms

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    Modeling and Applications in Operations Research
    (Taylor & Francis, 2023) Shekhar, Chandra
    The text envisages novel optimization methods that significantly impact real-life problems, starting from inventory control to economic decision-making. It discusses topics such as inventory control, queueing models, timetable scheduling, fuzzy optimization, and the Knapsack problem. The book’s content encompass the following key aspects: Presents a new model based on an unreliable server, wherein the convergence analysis is done using nature-inspired algorithms Discusses the optimization techniques used in transportation problems, timetable problems, and optimal/dynamic pricing in inventory control Highlights single and multi-objective optimization problems using pentagonal fuzzy numbers Illustrates profit maximization inventory model for non-instantaneous deteriorating items with imprecise costs Showcases nature-inspired algorithms such as particle swarm optimization, genetic algorithm, bat algorithm, and cuckoo search algorithm The text covers multi-disciplinary real-time problems such as fuzzy optimization of transportation problems, inventory control with dynamic pricing, timetable problem with ant colony optimization, knapsack problem, queueing modeling using the nature-inspired algorithm, and multi-objective fuzzy linear programming. It showcases a comparative analysis for studying various combinations of system design parameters and default cost elements. It will serve as an ideal reference text for graduate students and academic researchers in the fields of industrial engineering, manufacturing engineering, production engineering, mechanical engineering, and mathematics.
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    Analysis of Hybrid Wavelet Transformed Images to Improve Image Pixel Quality
    (IEEE, 2022-08) Bhatt, Upendra Mohan
    Visual information exchange in the form of a digital picture has become ubiquitous in the world of communication. During the transmission of images, the information is distorted due to noise. This noise component distorts the quality of images. De-noising methods are used to expand the grade of picture pixels or to restore the original form of incoming data. In this article, we will be introducing a de-noising method for upgrading the excellence of the image. A multiscale LMMSE estimation method using un-decimated wavelet transforms (UWT) has been proposed. To generate the wavelet transformed images, Daubechies & Biorthogonal filters are used. In this proposed method, a Hybrid filter is generated using these two filters. Image produced using this method seems to be agreeable as compared to individual filters. The result is observed by the PSNR & MSE value for the quantitative measure.
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    Steno AI at SemEval-2023 Task 6: Rhetorical Role Labelling of Legal Documents using Transformers and Graph Neural Networks
    (Association for Computational Linguistics, 2023) Sharma, Yashvardhan
    A legal document is usually long and dense requiring human effort to parse it. It also contains significant amounts of jargon which make deriving insights from it using existing models a poor approach. This paper presents the approaches undertaken to perform the task of rhetorical role labelling on Indian Court Judgements as part of SemEval Task 6: understanding legal texts, shared subtask A (Modi et al., 2023). We experiment with graph based approaches like Graph Convolutional Networks and Label Propagation Algorithm, and transformer-based approaches including variants of BERT to improve accuracy scores on text classification of complex legal documents.
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    A Normalized Rank Based A* Algorithm for Region Based Path Planning on an Image
    (Springer, 2019-04) Viswanathan, Sangeetha
    With the development of many autonomous systems, the need for efficient and robust path planners are increasing every day. Inspired by the intelligence of the heuristic, a normalized rank-based A* algorithm has been proposed in this paper to find the optimal path between a start and destination point on a classified image. The input image is classified and a normalized rank value based on the priority of traversal on each class is associated with each point on the image. Using the modified A* algorithm, the final optimal path is obtained. The obtained results are compared with the traditional method and results are found to be far better than existing method.
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    Solving Vehicle Routing Problem Using a Hybridization of Gain-Based Ant Colony Optimization and Firefly Algorithms
    (Springer, 2023) Viswanathan, Sangeetha
    Vehicle Routing Problem is one of the classical NP hard and combinatorial optimization problems that has been a spark of interest in the operation research domain. Though many variations of classical VRP are being developed, there is still the need for developing algorithms to improve solutions for VRP. A hybrid gain-based ant colony optimization-firefly algorithm (GACO-FA) has been proposed to deal with VRP. A global search is initially performed using the gain-based ant colony optimization, and subsequently local search for promising solution is done in the fine-tuned search space using firefly algorithm. The strengths of GACO and weakness of FA are aptly managed with a finer trade-off between them. The proposed GACO-FA is compared with best-known solutions and existing algorithms for performance analysis using the benchmark dataset. Analysis has been performed using measures like route cost, standard deviation, and percentage variation in length. The results have also been statistically verified for their significance.
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    AnyStreamKM: Anytime k-medoids Clustering for Streaming Data
    (IEEE, 2022) Challa, Jagat Sesh; Goyal, Navneet; Goyal, Poonam
    Stream Clustering algorithms have gained a lot of importance in the recent past due to rapid rising utilities of IoT systems and applications. Anytime algorithms and frameworks play a key role in handling streams that have data arriving/generating at variable rates. They are capable of handling both slow and fast stream speeds, at the same time generate the result with highest possible accuracy. In this paper, we present AnyStreamKM, which is a framework for anytime k-medoids clustering of data streams. It uses a proposed hierarchical data indexing structure known as AnyKMTree that stores the incoming data from the stream in the form of hierarchy of micro-clusters. AnyKMTree is an adaptation of R-tree with its splitting strategy inspired from the design principles of k-medoids clustering. AnyKMTree not only supports anytime features but is also capable of filtering out noise and outliers. Our experimental analysis establishes that AnyKMTree produces micro-clusters that are more compact and purer than the state-of-the-art methods. Also, when offline k-medoids clustering such as PAM (Partitioning Around Medoids) is applied on the micro-clusters produced by AnyKMTree, the resultant clustering has been found to be of higher quality than the state-of-the-art methods.
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    Comparative Analysis of Impact of Cryptography Algorithms on Wireless Sensor Networks
    (2021-07) Bhatia, Ashutosh
    Cryptography techniques are essential for a robust and stable security design of a system to mitigate the risk of external attacks and thus improve its efficiency. Wireless Sensor Networks (WSNs) play a pivotal role in sensing, monitoring, processing, and accumulating raw data to enhance the performance of the actuators, micro-controllers, embedded architectures, IoT devices, and computing machines to which they are connected. With so much threat of potential adversaries, it is essential to scale up the security level of WSN without affecting its primary goal of seamless data collection and communication with relay devices. This paper intends to explore the past and ongoing research activities in this domain. An extensive study of these algorithms referred here, are studied and analyzed. Based on these findings this paper will illustrate the best possible cryptography algorithms which will be most suited to implement the security aspects of the WSN and protect it from any threat and reduce its vulnerabilities. This study will pave the way for future research on this topic since it will provide a comprehensive and holistic view of the subject.
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    Comparative Performance Study of CNN-based Algorithms and YOLO
    (IEEE, 2022) Bitragunta, Sainath
    Tasks such as image classification, object detection, to mention a few, play an important role in computer vision. Numerous algorithms have been developed to improve the performance of such tasks for benchmark datasets. Although advanced algorithms offer state-of-the-art performance on such tasks, it is also important to analyze their algorithmic feasibility over the time to make it practical for end-user applications. This paper analyzes two such groups of algorithms, namely, Convolutional Neural Networks (CNN) based algorithms with You Only Look Once (YOLO) in terms of speed and accuracy.
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    Automatic parallelization of representative-based clustering algorithms for multicore cluster systems
    (Springer, 2020-03) Goyal, Navneet; Goyal, Poonam
    Ease of programming and optimal parallel performance have historically been on the opposite side of a trade-off, forcing the user to choose. With the advent of the Big Data era and the rapid evolution of sequential algorithms, the data analytics community can no longer afford the trade-off. We observed that several clustering algorithms often share common traits—particularly, algorithms belonging to the same class of clustering exhibit significant overlap in processing steps. Here, we present our observation on domain patterns in representative-based clustering algorithms and how they manifest as clearly identifiable programming patterns when mapped to a Domain Specific Language (DSL). We have integrated the signatures of these patterns in the DSL compiler for parallelism identification and automatic parallel code generation. The compiler either generates MPI C++ code for distributed memory parallel processing or MPI–OpenMP C++ code for hybrid memory parallel processing, depending upon the target architecture. Our experiments on different state-of-the-art parallelization frameworks show that our system can achieve near-optimal speedup while requiring a fraction of the programming effort, making it an ideal choice for the data analytics community. Results are presented for both distributed and hybrid memory systems.