Laxmi Bewoor | Savitribai Phule Pune University (original) (raw)

Papers by Laxmi Bewoor

International Journal on Recent and Innovation Trends in Computing and Communication

Major text summarization research is mainly focusing on summarizing short documents and very few ... more Major text summarization research is mainly focusing on summarizing short documents and very few works is witnessed for long document summarization. Additionally, extractive summarization is more addressed as compared with abstractive summarization. ive summarization, unlike extractive summarization, does not only copy essential words from the original text but requires paraphrasing to get close to human generated summary. The machine learning, deep learning models are adapted to contemporary pre-trained models like transformers. Transformer based Language models gaining a lot of attention because of self-supervised training while fine-tuning for Natural Language Processing (NLP) downstream task like text summarization. The proposed work is an attempt to investigate the use of transformers for abstraction. The proposed work is tested for book especially as a long document for evaluating the performance of the model.

Computational Intelligence and Neuroscience

The improper and excessive growth of brain cells may lead to the formation of a brain tumor. Brai... more The improper and excessive growth of brain cells may lead to the formation of a brain tumor. Brain tumors are the major cause of death from cancer. As a direct consequence of this, it is becoming more challenging to identify a treatment that is effective for a specific kind of brain tumor. The brain may be imaged in three dimensions using a standard MRI scan. Its primary function is to examine, identify, diagnose, and classify a variety of neurological conditions. Radiation therapy is employed in the treatment of tumors, and MRI segmentation is used to guide treatment. Because of this, we are able to assess whether or not a piece that was spotted by an MRI is a tumor. Using MRI scans, this study proposes a machine learning and medically assisted multimodal approach to segmenting and classifying brain tumors. MRI pictures contain noise. The geometric mean filter is utilized during picture preprocessing to facilitate the removal of noise. Fuzzy c-means algorithms are responsible for s...

2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)

In order to ensure safety measures on roads of India, the identification of traffic rule violator... more In order to ensure safety measures on roads of India, the identification of traffic rule violators is highly desirable but challenging job due to numerous difficulties such as occlusion, illumination, etc. In this paper we propose an end to end framework for detection of violations, notifying violators, and also storing them for analyzing and generating statistics for better decision making regarding traffic rules policy. In the proposed approach, we first detect vehicles using object detection which is performed using YOLO, and then accordingly each vehicle is checked against appropriate violations viz. not wearing a helmet, violation of crosswalks. Helmet violation is detected using a CNN (Convolutional neural network) based classifier. Crosswalk violation is detected using Instance Segmentation by Mask R-CNN architecture. After violations are detected, vehicle numbers are obtained of respective violators using OCR, and violators are notified. Thus an end to end autonomous system will help enforcing strong regulation of traffic rules.

Artificial Intelligence, 2021

international journal of engineering trends and technology, 2016

Scheduler is one of the important components of operating system which decides system’s performan... more Scheduler is one of the important components of operating system which decides system’s performance. Scheduler’s job is to allocate CPU to each process. Paper focuses on system optimization algorithms. Literature survey shows work done to improve performance of system. The paper proposed an idea to improve system’s performance by optimizing workload on CPU by meta-heuristic algorithm. Keywords—Operating System, CPU scheduling, Scheduling Algorithms, Workload, Metaheuristics.

2017 International Conference on Computer Communication and Informatics (ICCCI), 2017

Job or task scheduling with shared resource is challenging. With the increase in the size of the ... more Job or task scheduling with shared resource is challenging. With the increase in the size of the problem manual or sequential approach fails. Scheduling becomes a costly and tedious process. Not only schedules are ineffective, but also the task to prepare schedules becomes overhead. As the time increases, the associated cost also increases. The allocation of shared resources (M) to jobs (J) such that a specific optimization criterion is met is called job shop scheduling(JSS). In this study the focused criteria are makespan, average flow time & average cost. JSS has complexity (J!)⁁M, which makes it NP hard. Researchers have been applying many different to solve the JSS problem. Metaheuristic techniques like Genetic Algorithm (GA) have shown good results and have been proven to be better performers than other techniques.

A flow shop with no-wait schedules jobs continuously through all machines without any wait at con... more A flow shop with no-wait schedules jobs continuously through all machines without any wait at consecutive machines. This scheduling problem is combinatorial optimization problem and observed as NP-hard as appropriate sequence of jobs for scheduling from all possible combination of sequences is to be determined for reducing total completion time (makespan). This paper presents an effective hybrid Particle Swarm Optimization algorithm for solving no wait flow shop scheduling problem with the objective of minimization of makespan. This Proposed Hybrid Particle Swarm Optimization Makespan (PHPSOM) algorithm represents discrete job permutation by converting the continuous position information values of particles with random key representation rule. The proposed algorithm balances global exploration and local exploitation with evolutionary search guided by the mechanism of PSO, and local search by the mechanism of Simulated Annealing (SA) along with efficient population initialization wit...

2017 IEEE 7th International Advance Computing Conference (IACC), 2017

Kernel tracing facilitates to demonstrate various activities running inside the Operating System.... more Kernel tracing facilitates to demonstrate various activities running inside the Operating System. Kernel tracing tools like LTT, LTTng, DTrace, FTrace provide details about processes and their resources but these tools lack to extract knowledge from it. Pattern recognition is a major field of data mining and knowledge discovery. This paper presents a survey of widely used algorithms like Apriori, Tree-projection, FPgrowth, Eclat for finding frequent pattern over the database. This paper presents a comparative study of frequent pattern mining algorithm and suggests that the FP-growth algorithm is suitable for finding patterns in kernel trace data.

2016 International Conference on Computing Communication Control and automation (ICCUBEA), 2016

Text Summarization is the process of creating a condensed form of text document which maintains s... more Text Summarization is the process of creating a condensed form of text document which maintains significant information and general meaning of source text. Automatic text summarization becomes an important way of finding relevant information precisely in large text in a short time with little efforts. Text summarization approaches are classified into two categories: extractive and abstractive. This paper presents the comprehensive survey of both the approaches in text summarization.

2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), 2017

To develop effective, efficient scheduling methods is an important interdisciplinary challenge fo... more To develop effective, efficient scheduling methods is an important interdisciplinary challenge for any enterprise to sustain in a competitive position of fast changing markets. The goal of scheduling is to optimize different criteria of a facility such as makespan, mean flow time, resource idle time, total tardiness, number of tardy jobs/projects, in-process inventory cost, cost of being late etc. As problem size increases, performance decreases and finding optimal solution to this problem within reasonable time under some constraints turns this problem NP-Hard. Brute force algorithm normally fails to find optimum solution for large problem size and hence approximate solutions are proposed for near optimal value for given objective criterion. This paper presents a brief survey on hybrid metaheuristics for solving job shop problem for minimizing the makespan.

SSRN Electronic Journal, 2019

International Journal of Engineering & Technology, 2018

In software development life cycle, testing plays the significant role to verify requirement spec... more In software development life cycle, testing plays the significant role to verify requirement specification, analysis, design, coding and to estimate the reliability of software system. A test manager can write a set of test cases manually for the smaller software systems. However, for the extensive software system, normally the size of test suite is large, and the test suite is prone to an error committed like omissions of important test cases, duplication of some test cases and contradicting test cases etc. When test cases are generated automatically by a tool in an intelligent way, test errors can be eliminated. In addition, it is even possible to reduce the size of test suite and thereby to decrease the cost & time of software testing.It is a challenging job to reduce test suite size. When there are interacting inputs of Software under Test (SUT), combinatorial testing is highly essential to ensure higher reliability from 72 % to 91 % or even more than that. A meta-heuristic algo...

Algorithms, 2017

The no-wait flow shop is a flowshop in which the scheduling of jobs is continuous and simultaneou... more The no-wait flow shop is a flowshop in which the scheduling of jobs is continuous and simultaneous through all machines without waiting for any consecutive machines. The scheduling of a no-wait flow shop requires finding an appropriate sequence of jobs for scheduling, which in turn reduces total processing time. The classical brute force method for finding the probabilities of scheduling for improving the utilization of resources may become trapped in local optima, and this problem can hence be observed as a typical NP-hard combinatorial optimization problem that requires finding a near optimal solution with heuristic and metaheuristic techniques. This paper proposes an effective hybrid Particle Swarm Optimization (PSO) metaheuristic algorithm for solving no-wait flow shop scheduling problems with the objective of minimizing the total flow time of jobs. This Proposed Hybrid Particle Swarm Optimization (PHPSO) algorithm presents a solution by the random key representation rule for converting the continuous position information values of particles to a discrete job permutation. The proposed algorithm initializes population efficiently with the Nawaz-Enscore-Ham (NEH) heuristic technique and uses an evolutionary search guided by the mechanism of PSO, as well as simulated annealing based on a local neighborhood search to avoid getting stuck in local optima and to provide the appropriate balance of global exploration and local exploitation. Extensive computational experiments are carried out based on Taillard's benchmark suite. Computational results and comparisons with existing metaheuristics show that the PHPSO algorithm outperforms the existing methods in terms of quality search and robustness for the problem considered. The improvement in solution quality is confirmed by statistical tests of significance.

Procedia Manufacturing, 2018

Under the concept of "Industry 4.0", production processes will be pushed to be increasingly inter... more Under the concept of "Industry 4.0", production processes will be pushed to be increasingly interconnected, information based on a real time basis and, necessarily, much more efficient. In this context, capacity optimization goes beyond the traditional aim of capacity maximization, contributing also for organization's profitability and value. Indeed, lean management and continuous improvement approaches suggest capacity optimization instead of maximization. The study of capacity optimization and costing models is an important research topic that deserves contributions from both the practical and theoretical perspectives. This paper presents and discusses a mathematical model for capacity management based on different costing models (ABC and TDABC). A generic model has been developed and it was used to analyze idle capacity and to design strategies towards the maximization of organization's value. The trade-off capacity maximization vs operational efficiency is highlighted and it is shown that capacity optimization might hide operational inefficiency.

2015 International Conference on Computing Communication Control and Automation, 2015

Production scheduling is an interdisciplinary challenge of addressing optimality criteria such as... more Production scheduling is an interdisciplinary challenge of addressing optimality criteria such as minimizing makespan, mean flow time, idle machine time, total tardiness, number of tardy jobs, in-process inventory cost, cost of being late. Research till date used various AI techniques, heuristics and metaheuristics to optimize scheduling criteria. If problem size goes on increasing heuristics is not able to give optimal results. The enumerations for finding the probabilities for improving the utilization of resources turn this problem towards NP-Hard. This paper presents comprehensive coverage of PSO application in solving optimization problems in the area of production scheduling. The paper discusses about use of PSO for improvement in the results of optimality criteria.

2012 International Conference on Communication, Information & Computing Technology (ICCICT), 2012

ABSTRACT The ISO 9001 quality management system has been widely accepted and adapted as a nationa... more ABSTRACT The ISO 9001 quality management system has been widely accepted and adapted as a national standard by most industrial countries. Despite its high popularity and the urgent demand from customers to implement ISO 9001, some major concerns for those organizations that are seeking registration to ISO 9001 include the expensive cost and the lengthy time to implement. The purpose of this paper is to describe an expert advisory system for ISO 9001 implementation by using an expert system. This expert advisory system integrated the ISO 9001 quality system guidelines and domain experience a into a knowledge-based expert system. By identifying the critical ISO elements and comparing the company's current quality performance with ISO standards, this advisory system provides assessment results and implementation suggestions in terms of corrective and preventive action reports to the organization. The advisory system has been validated by its implementation at Pune based small scale company. The following contains a description of the system and a discussion of the validation results. Limitations of the system and recommendations for future research are also discussed.

International Journal of Scientific Research in Computer Science, Engineering and Information Technology

In today's world, deep learning fields are getting boosted with increasing speed. Lot of inno... more In today's world, deep learning fields are getting boosted with increasing speed. Lot of innovations and different algorithms are being developed. In field of computer vision, related to autonomous driving sector, traffic signs play an important role to provide real time data of an environment. Different algorithms were developed to classify these Signs. But performance still needs to improve for real time environment. Even the computational power required to train such model is high. In this paper, Convolutional Neural Network model is used to Classify Traffic Sign. The experiments are conducted on a real-world data set with images and videos captured from ordinary car driving as well as on GTSRB dataset [15] available on Kaggle. This proposed model is able to outperform previous models and resulted with accuracy of 99.6% on validation set. This idea has been granted Innovation Patent by Australian IP to Authors of this Research Paper. [24]