Prospects & Challenges of Implementing Knowledge Management System in IT Industry

Recent past has seen an epidemic growth in the adoption of strategic information systems. In order to be successful, enterprises are putting in huge investments into implementation of information technology (IT) and knowledge management systems (KMS). KMS implementation in an IT industry has been discussed in this paper. Several challenges including multiple information sources, access control, and employee’s mistrust among others are being identified along with their possible solutions. Later foreseen benefits of KMS implementation including quicker problem identification, faster response time, and cost saving among others are being highlighted. The paper concludes with revealing future research possibilities

Software Quality Assurance A Study Based on Pakistan’s Software Industry

This paper investigates the role of quality management practices in software industry of Pakistan. We present a comparison between the more-experienced and less-experienced firms with respect to the critical factors of quality management. The critical factors of quality management practices in the software industry are first identified from the literature survey and validated through an empirical study. The study attempts to probe the influence of “age of quality” and “use of software” over software quality management practices and programs. The results of the study shows that the ‘age of quality” and “use of software” have partial influence over the software quality management

Survival Analysis of Tumor using 7 Tesla MRI

Magnetic resonance imaging (MRI7) is a very powerful imaging technique for the assessment of stroke aetiology (Condition) and brain imaging. Another class of MRI is ultrahigh frequency based MRI using 7 Tesla is now developed by seamen’s for better imaging in humans. This study examines these MRI. Thisarticle highlights an alternative approach, denoted “interval monitoring,” whose aims is related with more timely detection of tumor cancer changes. The conceptual background and the computational realization of the proposed method are outlined, and its application is illustrated by an empirical example from the image-based photo science, cancer registry of America. Monitoring of cancer patient survival is the first step of its cure so across the globe practice routinely employed by many cancer registries, which is an essential component for its cure. However, changes in prognosis over time are disclosed withconsiderable delay, with traditional methods of monitoring cumulative survival. Our study took sequence of MRI images, GMPLS function locate the cancer after filtering and skeletonization. This study saves time and difference for calculation of cancer equation. This study uses statistical technique to get the desired matrix, further its inverse provides us real time mathematical equation which is unique for each patient. Further survivor analysis is employed to achieve the break or death of subject. The Aim of this research is to provide unique mathematical model of a cancer patient, provides real time graph aboutcancer health and survivor function depicts the death of subject respectively

Segmentation of Endothelial Cell Boundaries of Rabbit Aortic Images Using a Machine Learning Approach

This paper presents an automatic detection method for thin boundaries of silver-stained endothelial cells (ECs) imaged using light microscopy of endothelium mono-layers from rabbit aortas. To achieve this, a segmentation technique was developed, which relies on a rich feature space to describe the spatial neighbourhood of each pixel and employs a Support Vector Machine (SVM) as a classifier. This segmentation approach is compared, using hand-labelled data, to a number of standard segmentation/thresholding methods commonly applied in microscopy. The importance of different features is also assessed using the method of minimum Redundancy, Maximum Relevance (mRMR), and the effect of different SVM kernels is also considered. The results show that the approach suggested in this paper attains much greater accuracy than standard techniques; in our comparisons with manually labelled data, our proposed technique is able to identify boundary pixels to an accuracy of 93%. More significantly, out of a set of 56 regions of image data, 43 regions were binarised to a useful level of accuracy. The results obtained from the image segmentation technique developed here may be used for the study of shape and alignment of ECs, and hence patterns of blood flow, around arterial branches

Herpesviriae Infection of the Corneal Endothelium

Background - The corneal endothelium plays a vital role in maintaining corneal clarity by regulating the amount of fluid in the corneal stroma. - Corneal endotheliitis is defined as inflammation of the corneal endothelial layer that leads to corneal edema and haziness, and subsequent loss of vision. - Most common causes include cytomegalovirus (CMV), herpes simplex virus (HSV), and varicella zoster virus (VZV). - Because corneal endothelial cells cannot regenerate following injury, early diagnosis is essential in proper management and preventing loss of corneal endothelial cells. In this review we aim to gather the most recent knowledge on viral corneal endotheliitis, focusing on the most common viral causes, to help clinicians with clinical diagnosis, appropriate laboratory tests, and proper management of this potentially debilitating condition

Infections in patients with multiple myeloma treated with conventional chemotherapy: A single-center, 10-year experience in Pakistan

Introduction: Multiple myeloma (MM) is a common hematologic malignancy with variable degrees of immunodeficiency. Disease- and treatment-related compromise of the immune system predisposes patients to infections, which are a major cause of morbidity and mortality.Objective: We aimed to establish the incidence and main characteristics of infections in MM patients treated at our center over a 10-year period.Method and results: Of the 412 patients retrospectively analyzed, 154 (37.4%) were documented to have at least one episode of infection and were included in this study. A total of 244 infectious episodes were documented. The most common site of infection was the lung, followed by the genitourinary system. The most common infections were bacterial, followed by viral. Escherichia coli were the most common organism. In 160 (65.5%) episodes, the organism was not isolated. Thalidomide with dexamethasone was the most common treatment regimen, followed by melphalan with dexamethasone. Infection was the main cause of death in 26 (6.3%) out of all 412 patients.Conclusion: Infections are a notable cause of morbidity and mortality in the clinical course of MM patients. By considering patient and disease characteristics, a risk-adapted selection of the MM treatment should be employed, with special attention toward patient age and disease-associated organ dysfunction. Patient education, access to healthcare and physician vigilance are also essential. Vaccination and antimicrobial prophylaxis may be considered prior to or during therapy

The impact of socioeconomic factors on the outcome of childhood acute lymphoblastic leukemia (ALL) treatment in a low/middle income country (LMIC)

Survival for childhood acute lymphoblastic leukemia (ALL) has improved significantly, but these benefits may not be available to many children from low and middle income countries, where reasons for treatment failure may be unique to their environment. We retrospectively reviewed data on pediatric (1 to 18 y or younger) patients with newly diagnosed ALL treated over 5 years at a children\u27s cancer hospital in Pakistan. Patients were treated with modified Berlin-Frankfurt-Muenster -based therapy without risk stratification. There were 255 children with a median age of 7 years (mean, 7.65 y) and a male preponderance (M:F=1.6:1). 20% had T-ALL, one-third had white blood cells \u3e50×10/L and 13.7% central nervous system disease. A majority (56.5%) was malnourished. In total, 49 (19.2%) died before the end of induction and 21 died in complete remission. Most deaths were infection-related. A total of 50 patients relapsed and 19 abandoned therapy after complete remission. Five-year overall survival is 52.9% with abandonment censored and 45.8% with abandonment as an event. Overall survival was related to socioeconomic status but not to known risk factors. The outcome of ALL at our center is suboptimal and associated with factors not commonly seen in developed countries. Special attention to early diagnosis, infection control, and parental educational are needed to improve the survival

Risk Prediction by Using Artificial Neural Network in Global Software Development

The demand for global software development is growing. The nonavailability of software experts at one place or a country is the reason for the increase in the scope of global software development. Software developers who are located in different parts of the world with diversified skills necessary for a successful completion of a project play a critical role in the field of software development. Using the skills and expertise of software developers around the world, one could get any component developed or any IT-related issue resolved. The best software skills and tools are dispersed across the globe, but to integrate these skills and tools together and make them work for solving real world problems is a challenging task. The discipline of risk management gives the alternative strategies to manage risks that the software experts are facing in today’s world of competitiveness. This research is an effort to predict risks related to time, cost, and resources those are faced by distributed teams in global software development environment. To examine the relative effect of these factors, in this research, neural network approaches like Levenberg–Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient have been implemented to predict the responses of risks related to project time, cost, and resources involved in global software development. Comparative analysis of these three algorithms is also performed to determine the highest accuracy algorithms. The findings of this study proved that Bayesian Regularization performed very well in terms of the MSE (validation) criterion as compared with the Levenberg–Marquardt and Scaled Conjugate Gradient approaches

Predictors of treatment abandonment for patients with pediatric cancer at Indus children cancer hospital, Karachi, Pakistan

Background: Abandonment of treatment is one of the toughest challenges to deal with in pediatric oncology. It leads to unnecessary mortality and morbidity in patients from low- and middle-income countries.Procedure: The objective of our retrospective study was to determine the prevalence and predictors for abandonment among children with cancer at our hospital in Karachi, Pakistan. We analyzed data on patients younger than 18 years, diagnosed with any malignancy between November 2014 and May 2016.Results: From a total of 821 patients, one hundred and eighty-two (22.2%) patients abandoned treatment at various stages, 92 (11.2%) patients did not initiate treatment at all, and the remaining 90 (11.0%) left during treatment. The gender ratio at registration was skewed toward males but not statistically significant for abandonment. Of 295 registered females, 74 (25.1%) abandoned treatment compared to 108 (20.5%) abandonments among 526 males. In multivariable regression analysis, the type of malignancy, guardian\u27s profession, and travelling from outside the city of Karachi (odds ratio [OR]: 1.48; 95% confidence interval [CI] 1.02-2.15; P = 0.039) correlated with increased abandonment. Treatment abandonment was higher among patients with brain tumors (45.7%) and solid tumors (30.8%) and among those whose guardians were associated with a rural profession (24.7%). Monthly income, age, and number of siblings had no impact on the decision to abandon treatment.Conclusion: Despite the provision of free treatment, the prevalence of abandonment was high. More qualitative data need to be collected to identify and target groups of individuals who may be likely to abandon treatment, thus improving outcome of patients