Detection and Classification of Hemorrhages in Retinal Images

Damage of the blood vessels in retina due to diabetes is called diabetic retinopathy(DR).Hemorrhages is thefirst clinically visible symptoms of DR.This paper presents a new technique to extract and classify the hemorrhages in fundus images.The normal objects such as blood vessels,fovea and optic disc inside retinal images are masked to distinguish them from hemorrhages.For masking blood vessels,thresholding that separates blood vessels and background intensity followed by a newfilter to extract the border of vessels based on orienta-tions of vessels are used.For masking optic disc,the image is divided into sub-images then the brightest window with maximum variance in intensity is selected.Then the candidate dark regions are extracted based on adaptive thresholding and top-hat morphological techniques.Features are extracted from each candidate region based on ophthalmologist selection such as color and size and pattern recognition techniques such as texture and wavelet features.Three different types of Support Vector Machine(SVM),Linear SVM,Quadratic SVM and Cubic SVM classifier are applied to classify the candidate dark regions as either hemor-rhages or healthy.The efficacy of the proposed method is demonstrated using the standard benchmark DIARETDB1 database and by comparing the results with methods in silico.The performance of the method is measured based on average sensitivity,specificity,F-score and accuracy.Experimental results show the Linear SVM classifier gives better results than Cubic SVM and Quadratic SVM with respect to sensitivity and accuracy and with respect to specificity Quadratic SVM gives better result as compared to other SVMs.

Offshore Software Maintenance Outsourcing Process Model Validation:A Case Study Approach

The successful execution and management of Offshore Software Maintenance Outsourcing(OSMO)can be very beneficial for OSMO vendors and the OSMO client.Although a lot of research on software outsourcing is going on,most of the existing literature on offshore outsourcing deals with the outsourcing of software development only.Several frameworks have been developed focusing on guiding software systemmanagers concerning offshore software outsourcing.However,none of these studies delivered comprehensive guidelines for managing the whole process of OSMO.There is a considerable lack of research working on managing OSMO from a vendor’s perspective.Therefore,to find the best practices for managing an OSMO process,it is necessary to further investigate such complex and multifaceted phenomena from the vendor’s perspective.This study validated the preliminary OSMO process model via a case study research approach.The results showed that the OSMO process model is applicable in an industrial setting with few changes.The industrial data collected during the case study enabled this paper to extend the preliminary OSMO process model.The refined version of the OSMO processmodel has four major phases including(i)Project Assessment,(ii)SLA(iii)Execution,and(iv)Risk.

ILSM:Incorporated Lightweight Security Model for Improving QOS in WSN

In the network field,Wireless Sensor Networks(WSN)contain prolonged attention due to afresh augmentations.Industries like health care,traffic,defense,and many more systems espoused the WSN.These networks contain tiny sensor nodes containing embedded processors,TinyOS,memory,and power source.Sensor nodes are responsible for forwarding the data packets.To manage all these components,there is a need to select appropriate parameters which control the quality of service of WSN.Multiple sensor nodes are involved in transmitting vital information,and there is a need for secure and efficient routing to reach the quality of service.But due to the high cost of the network,WSN components have limited resources to manage the network.There is a need to design a lightweight solution that ensures the quality of service in WSN.In this given manner,this study provides the quality of services in a wireless sensor network with a security mechanism.An incorporated hybrid lightweight security model is designed in which random waypoint mobility(RWM)model and grey wolf optimization(GWO)is used to enhance service quality and maintain security with efficient routing.MATLAB version 16 andNetwork Stimulator 2.35(NS2.35)are used in this research to evaluate the results.The overall cost factor is reduced at 60%without the optimization technique and 90.90%reduced by using the optimization technique,which is assessed by calculating the signal-to-noise ratio,overall energy nodes,and communication overhead.

Tele-COVID: A Telemedicine SOA-Based Architectural Design for COVID-19 Patients

In Wuhan,China,a novel Corona Virus(COVID-19)was detected in December 2019;it has changed the entire world and to date,the number of diagnosed cases is 38,756,2891 and 1,095,2161 people have died.This happened because a large number of people got affected and there is a lack of hospitals for COVID-19 patients.One of the precautionary measures for COVID-19 patients is isolation.To support this,there is an urgent need for a platform that makes treatment possible from a distance.Telemedicine systems have been drastically increasing in number and size over recent years.This increasing number intensies the extensive need for telemedicine for the national healthcare system.In this paper,we present Tele-COVID which is a telemedicine application to treat COVID-19 patients from a distance.Tele-COVID is uniquely designed and implemented in Service-Oriented Architecture(SOA)to avoid the problem of interoperability,vendor lock-in,and data interchange.With the help of Tele-COVID,the treatment of patients at a distance is possible without the need for them to visit hospitals;in case of emergency,necessary services can also be provided.

Sentiment Analysis of Roman Urdu on E-Commerce Reviews Using Machine Learning

Sentiment analysis task has widely been studied for various languages such as English and French.However,Roman Urdu sentiment analysis yet requires more attention from peer-researchers due to the lack of Off-the-Shelf Natural Language Processing(NLP)solutions.The primary objective of this study is to investigate the diverse machine learning methods for the sentiment analysis of Roman Urdu data which is very informal in nature and needs to be lexically normalized.To mitigate this challenge,we propose a fine-tuned Support Vector Machine(SVM)powered by Roman Urdu Stemmer.In our proposed scheme,the corpus data is initially cleaned to remove the anomalies from the text.After initial pre-processing,each user review is being stemmed.The input text is transformed into a feature vector using the bag-of-word model.Subsequently,the SVM is used to classify and detect user sentiment.Our proposed scheme is based on a dictionary based Roman Urdu stemmer.The creation of the Roman Urdu stemmer is aimed at standardizing the text so as to minimize the level of complexity.The efficacy of our proposed model is also empirically evaluated with diverse experimental configurations,so as to fine-tune the hyper-parameters and achieve superior performance.Moreover,a series of experiments are conducted on diverse machine learning and deep learning models to compare the performance with our proposed model.We also introduced the largest dataset on Roman Urdu,i.e.,Roman Urdu e-commerce dataset(RUECD),which contains 26K+user reviews annotated by the group of experts.The RUECD is challenging and the largest dataset available of Roman Urdu.The experiments show that the newly generated dataset is quite challenging and requires more attention from the peer researchers for Roman Urdu sentiment analysis.

Time and Quantity Based Hybrid Consolidation Algorithms for Reduced Cost Products Delivery

In today’s competitive business environment,the cost of a product is one of the most important considerations for its sale.Businesses are heavily involved in research strategies to minimize the cost of elements that can impact on the final price of the product.Logistics is one such factor.Numerous products arrive from diverse locations to consumers in today’s digital era of online businesses.Clearly,the logistics sector faces several dilemmas from order attributes to environmental changes in this regard.This has specially been noted during the ongoing Covid-19 pandemic where the demands on online businesses have increased several fold.Consequently,the methodology to optimise delivery cost and its impact on environmental focus by reducing CO_(2) emissions has gained relevance.The resultant strategy of Shipment Consolidation that has evolved is an approach that combines one or more transport orders in the same vehicle for delivery.Shipment Consolidation has been categorized in three order scheduling approaches:Time based consolidation,Quantity based consolidation,and a Hybrid(Time-Quantity)based consolidation.In this paper,a new Hybrid Consolidation approach is presented.Using the Hybrid approach,it has been shown that order delivery can be facilitated by taking into account not only the order pick up time,but also the total order quantity.These results have shown that if a time window is available in respect of the order delivery time,then the order can be delayed from pickup to consolidate it with other orders for cost optimization.This hybrid approach is based on four consolidation principles,two of which work on fixed departure and two,on demand departure.Three of these rules have been implemented and tested here with an application case study.Statistical analysis of the results is illustrated with different planning evaluation indicators.The Result analyses indicate that consolidation of orders is increased with each implemented rule hence motivating us towards the implementation of the fourth rule.Testing with bigger data sets is required.