Sim_Dsc: Simulator for Optimizing the Performance of Disk Scheduling Algorithms

Disk scheduling involves a careful examination of pending requests to determine the most efficient way to service these requests. A disk scheduler examines the positional relationship among waiting requests, then reorders the queue so that the requests will be serviced with minimum seek. The purpose of the study is to obtain the best scheduling algorithm based on the seek time, rotation time and transfer time for moveable head disks. Keeping in view an attempt has been made to design a simulator for optimizing the performance of disk scheduling algorithms using Box-Muller transformation. The input for the simulator has been derived by using an algorithm for generating pseudo random numbers which follows box-muller transformations. Simulator takes access time which is generated using seek time, rotation time and transfer time, as the request of cylinder numbers, current position of read/write head as inputs. On the basis of these inputs, total head movement of each disk scheduling algorithm is calculated under various loads

Deep Learning Paradigms for Existing and Imminent Lung Diseases Detection: A Review

Diagnosis of lung diseases like asthma, chronic obstructive pulmonary disease, tuberculosis, cancer, etc., by clinicians rely on images taken through various means like X-ray and MRI. Deep Learning (DL) paradigm has magnified growth in the medical image field in current years. With the advancement of DL, lung diseases in medical images can be efficiently identified and classified. For example, DL can detect lung cancer with an accuracy of 99.49% in supervised models and 95.3% in unsupervised models. The deep learning models can extract unattended features that can be effortlessly combined into the DL network architecture for better medical image examination of one or two lung diseases. In this review article, effective techniques are reviewed under the elementary DL models, viz. supervised, semi-supervised, and unsupervised Learning to represent the growth of DL in lung disease detection with lesser human intervention. Recent techniques are added to understand the paradigm shift and future research prospects. All three techniques used Computed Tomography (C.T.) images datasets till 2019, but after the pandemic period, chest radiographs (X-rays) datasets are more commonly used. X-rays help in the economically early detection of lung diseases that will save lives by providing early treatment. Each DL model focuses on identifying a few features of lung diseases. Researchers can explore the DL to automate the detection of more lung diseases through a standard system using datasets of X-ray images. Unsupervised DL has been extended from detection to prediction of lung diseases, which is a critical milestone to seek out the odds of lung sickness before it happens. Researchers can work on more prediction models identifying the severity stages of multiple lung diseases to reduce mortality rates and the associated cost. The review article aims to help researchers explore Deep Learning systems that can efficiently identify and predict lung diseases at enhanced accuracy

Handling of Congestion in Cluster Computing Environment Using Mobile Agent Approach

Computer networks have experienced an explosive growth over the past few years and with that growth have come severe congestion problems. Congestion must be prevented in order to maintain good network performance. In this paper, we proposed a cluster based framework to control congestion over network using mobile agent. The cluster implementation involves the designing of a server which manages the configuring, resetting of cluster. Our framework handles - the generation of application mobile code, its distribution to appropriate client, efficient handling of results, so generated and communicated by a number of client nodes and recording of execution time of application. The client node receives and executes the mobile code that defines the distributed job submitted by server and replies the results back. We have also the analyzed the performance of the developed system emphasizing the tradeoff between communication and computation overhead. The effectiveness of proposed framework is analyzed using JDK 1.5

Simulator for Resource Optimization of Job Scheduling in a Grid Framework

Traditionally, computer software2019;s has been written for serial computation. This software is to be run on a single computer with a single Central Processing Unit (CPU). A problem is broken into a discrete serial of instructions that executed in the exact order, one after another. Only one instruction can be executed at any moment of time on a single CPU. Parallel computing, on the other hand, is the simultaneous use of multiple computer resources to solve a computational problem. The program is to be run using multiple CPU2019;s. A problem is broken into discrete parts that can be solved concurrently and executed simultaneously on different CPU2019;s. The purpose of this proposed work is to develop a simulator using Java for the implementation of Job scheduling and shows that Parallel Execution is efficient with respect to serial execution in terms of time, speed and resources

Contemplation of tone mapping operators in high dynamic range imaging

The technique of tone mapping has found widespread popularity in the modern era owing to its applications in the digital world. There are a considerable number of tone mapping techniques that have been developed so far. One method may be better than the other in some cases which is determined by the requirement of the user. In this paper, some of the techniques for tone mapping/tone reproduction of high dynamic range images have been contemplated. The classification of tone mapping operators has also been given. However, it has been found that these techniques lack in providing quality of service visualization of high dynamic range images. This paper has tried to highlight the drawbacks in the existing traditional methods so that the tone-mapped techniques can be enhanced

An assessment of sociodemographic factors and family planning practices in Jamnagar, Gujarat, India: a cross sectional study

Background: India was the first country in the world to launch a National Family Planning Programme in 1952. It was purely a demographic programme with the sole objective of reducing the birth rate to stabilize the population. As per 2011 census population increased with the growth rate of 17.7%. Thus even after 63 years of programme & many more new advances & updates in programme, India could not reach its target of at least 60% of couple protection rate. So there would be some definite factors prohibiting its use.Methods: A cross section study of 450 reproductive age group women was conducted to find out the factors of unmet need of contraception & socio demographic profile.Results: In present study couple protection rate was 57.11%, majority being permanent sterilization. The selection of contraception was influenced by her husband in 43.24% & by mother-in-law in 62.16%.Conclusions: The study revealed that almost half of the couple population was using the contraception that included temporary as well as permanent methods of contraception