Performance modelling and optimization for video-analytic algorithms in a cloud-like environment using machine learning

CCTV cameras produce a large amount of video surveillance data per day, and analysing them require the use of significant computing resources that often need to be scalable. The emergence of the Hadoop distributed processing framework has had a significant impact on various data intensive applications as the distributed computed based processing enables an increase of the processing capability of applications it serves. Hadoop is an open source implementation of the MapReduce programming model. It automates the operation of creating tasks for each function, distribute data, parallelize executions and handles machine failures that reliefs users from the complexity of having to manage the underlying processing and only focus on building their application. It is noted that in a practical deployment the challenge of Hadoop based architecture is that it requires several scalable machines for effective processing, which in turn adds hardware investment cost to the infrastructure. Although using a cloud infrastructure offers scalable and elastic utilization of resources where users can scale up or scale down the number of Virtual Machines (VM) upon requirements, a user such as a CCTV system operator intending to use a public cloud would aspire to know what cloud resources (i.e. number of VMs) need to be deployed so that the processing can be done in the fastest (or within a known time constraint) and the most cost effective manner. Often such resources will also have to satisfy practical, procedural and legal requirements. The capability to model a distributed processing architecture where the resource requirements can be effectively and optimally predicted will thus be a useful tool, if available. In literature there is no clear and comprehensive modelling framework that provides proactive resource allocation mechanisms to satisfy a user's target requirements, especially for a processing intensive application such as video analytic. In this thesis, with the hope of closing the above research gap, novel research is first initiated by understanding the current legal practices and requirements of implementing video surveillance system within a distributed processing and data storage environment, since the legal validity of data gathered or processed within such a system is vital for a distributed system's applicability in such domains. Subsequently the thesis presents a comprehensive framework for the performance ii modelling and optimization of resource allocation in deploying a scalable distributed video analytic application in a Hadoop based framework, running on virtualized cluster of machines. The proposed modelling framework investigates the use of several machine learning algorithms such as, decision trees (M5P, RepTree), Linear Regression, Multi Layer Perceptron(MLP) and the Ensemble Classifier Bagging model, to model and predict the execution time of video analytic jobs, based on infrastructure level as well as job level parameters. Further in order to propose a novel framework for the allocate resources under constraints to obtain optimal performance in terms of job execution time, we propose a Genetic Algorithms (GAs) based optimization technique. Experimental results are provided to demonstrate the proposed framework's capability to successfully predict the job execution time of a given video analytic task based on infrastructure and input data related parameters and its ability determine the minimum job execution time, given constraints of these parameters. Given the above, the thesis contributes to the state-of-art in distributed video analytics, design, implementation, performance analysis and optimisation

Video Forensics in Cloud Computing: The Challenges & Recommendations

Forensic analysis of large video surveillance datasets requires computationally demanding processing and significant storage space. The current standalone and often dedicated computing infrastructure used for the purpose is rather limited due to practical limits of hardware scalability and the associated cost. Recently Cloud Computing has emerged as a viable solution to computing resource limitations, taking full advantage of virtualisation capabilities and distributed computing technologies. Consequently the opportunities provided by cloud computing service to support the requirements of forensic video surveillance systems have been recently studied in literature. However such studies have been limited to very simple video analytic tasks carried out within a cloud based architecture. The requirements of a larger scale video forensic system are significantly more and demand an in-depth study. Especially there is a need to balance the benefits of cloud computing with the potential risks of security and privacy breaches of the video data. Understanding different legal issues involved in deploying video surveillance in cloud computing will help making the proposed security architecture affective against potential threats and hence lawful. In this work we conduct a literature review to understand the current regulations and guidelines behind establishing a trustworthy, cloud based video surveillance system. In particular we discuss the requirements of a legally acceptable video forensic system, study the current security and privacy challenges of cloud based computing systems and make recommendations for the design of a cloud based video forensic system

Video forensics in cloud computing: the challenges & recommendations

Forensic analysis of large video surveillance datasets requires computationally demanding processing and significant storage space. The current standalone and often dedicated computing infrastructure used for the purpose is rather limited due to practical limits of hardware scalability and the associated cost. Recently Cloud Computing has emerged as a viable solution to computing resource limitations, taking full advantage of virtualisation capabilities and distributed computing technologies. Consequently the opportunities provided by cloud computing service to support the requirements of forensic video surveillance systems have been recently studied in literature. However such studies have been limited to very simple video analytic tasks carried out within a cloud based architecture. The requirements of a larger scale video forensic system are significantly more and demand an in-depth study. Especially there is a need to balance the benefits of cloud computing with the potential risks of security and privacy breaches of the video data. Understanding different legal issues involved in deploying video surveillance in cloud computing will help making the proposed security architecture affective against potential threats and hence lawful. In this work we conduct a literature review to understand the current regulations and guidelines behind establishing a trustworthy, cloud based video surveillance system. In particular we discuss the requirements of a legally acceptable video forensic system, study the current security and privacy challenges of cloud based computing systems and make recommendations for the design of a cloud based video forensic system

Validity of Cancer Antigen-125 (CA-125) and Risk of Malignancy Index (RMI) in the Diagnosis of Ovarian Cancer

Objective: We sought to determine the validity of cancer antigen 125 (CA-125) and the risk of malignancy index (RMI) in the diagnosis of ovarian cancer in women presenting with adnexal lesions of various histopathology types. Methods: This retrospective cross- sectional study included all women with adnexal lesions who were evaluated at the Royal Hospital, Oman, between January 2012 and December 2014. The inclusion criteria included women who underwent surgical intervention and who had preoperative CA-125 testing and pelvic ultrasound in the work-up plan of their management. The surgical intervention was usually followed by a histopathological diagnosis of the nature of the lesion, which was used as the gold standard for the evaluation of both CA-125 and RMI. Results: The cohort included 361 women who had serum CA-125 and pelvic ultrasound prior to the surgical intervention of the adnexal lesion. Of these women, 61 (17%) had malignant ovarian lesions. Using the proposed cut-off 35 U/ml for CA-125 and 200 for RMI, the CA-125 test was more sensitive for detecting the majority of malignant ovarian tumors compared to the RMI (69% vs. 57%). Both tests were more sensitive in detecting epithelial ovarian cancer compared to other ovarian cancers. However, RMI was more specific in excluding benign ovarian lesions compared to CA-125 (81% vs. 68%). Additionally, RMI had a better area under the curve compared to CA-125 (0.771 vs. 0.745; p<0.005). Lowering the RMI cut-off to 150 resulted in a better sensitivity (62% vs. 57%) and had an acceptable specificity (78% vs. 81%) compared to a cut-off of 200. Conclusion: Both CA-125 and RMI have good validity in the diagnosis of ovarian tumors. CA-125 has higher sensitivity; however, RMI has higher specificity. In combination, CA-125 might be more valid for the diagnosis of malignant ovarian cancer while RMI is more valid for excluding the diagnosis of these tumors. Differential use of these two tools will improve the triage of women with suspected ovarian tumors since both are measured in their work-up. We recommended the use of both tools in primary care to reduce referral to gynecology or oncology units

Evaluation of HE4, CA-125, Risk of Ovarian Malignancy Algorithm (ROMA) and Risk of Malignancy Index (RMI) in the Preoperative Assessment of Patients with Adnexal Mass

Objectives: To evaluate the validity and compare the performance of cancer antigen-125 (CA-125), human epididymis protein 4 (HE4), the risk of malignancy index (RMI), and the risk of ovarian malignancy algorithm (ROMA) in the diagnosis of ovarian cancer in patients with ovarian lesions discovered during their preoperative work-up investigations. Methods: This prospective, cross-sectional study looked at patients who attended the gynecology department at the Royal Hospital, Muscat, from 1 March 2014 to 30 April 2015, for the evaluation of an ovarian lesion. The inclusion criteria included women who underwent surgical intervention and who had a preoperative pelvic ultrasound with laboratory investigation for CA-125 and HE4. The study validated the diagnostic performance of CA-125, RMI, HE4, and ROMA using histopathological diagnosis as the gold standard. Results: The study population had a total of 213 cases of various types of benign (77%) and malignant (23%) ovarian tumors. CA-125 showed the highest sensitivity (79%) when looking at the total patient population. When divided by age, the sensitivity was 67% in premenopausal women. In postmenopausal women, CA-125 had lower sensitivity (89%) compared to RMI, HE4, and ROMA (93% each). A high specificity of 90% was found for HE4 in the total patient population, 93% in premenopausal women and 75% in postmenopausal women. CA-125 had the highest specificity (79%) in postmenopausal women. Both CA-125 and RMI were frequently elevated in benign gynecological conditions particularly in endometriosis when compared to HE4 and ROMA. We also studied modifications of the optimal cut-offs for the four parameters. Both CA-125 and RMI showed a significant increase in their specificity if the cut-off was increased to ≥ 60 U/mL for CA-125 and to ≥ 250 for RMI. For HE4, we noted an improvement in its specificity in postmenopausal women when its cut-off was increased to140 pmol/L. Conclusions: HE4 and ROMA showed a very high specificity, but were less sensitive than CA-125 and RMI in premenopausal women. However, they were of comparable sensitivity in postmenopausal women and were valuable in distinguishing benign ovarian tumors or endometriosis from ovarian cancer. Modifying the cut-off values of the different markers resulted in a higher accuracy compared to the standard cut-offs, but at the expense of reduced sensitivity

The Effect of Obesity on Pregnancy and its Outcome in the Population of Oman, Seeb Province

Objectives: The World Health Organization estimated that in 2011 worldwide 1.6 billion adults were overweight, and 400 million were obese. The obesity epidemic is a documented phenomenon and Oman is no exception. The aim of this study was to determine the effect of obesity on pregnancy and its prenatal and neonatal outcomes. Methods: A prospective cohort study was carried out among pregnant Omani women attending antenatal clinics in their first trimester in the Seeb province of Muscat, Oman. Results: A total of 700 pregnant women were enrolled in the study and were categorized according to their body mass index: 245 (35%) were normal weight, 217 (31%) were overweight, and 238 (34%) were obese. The relative risk (RR) of cesarean section among obese women compared to women of normal weight was 2.1 (95% confidence interval (CI) 1.2–3.2) and of overweight women was 1.4 (95% CI 0.9–2.3). The risk of elective cesarean section increased to 7.5 (95% CI 1.7–32.8) in obese women and was statistically significant in the obese group. In this study, 100 women (15.7%) developed gestational diabetes (11.8% of normal weight women, 17.8% of overweight women, and 17.9% of obese women). Miscarriages were more common among obese women 11.9% (n = 27) compared to the normal weight and overweight groups (6.7% and 9.4%, respectively). There was a weak yet statistically significant correlation between birth weight and body mass index. The risk of macrosomia was significantly higher in obese women compared to normal weight women. To evaluate the sensitivity of the oral glucose challenge test (OGCT), the oral glucose tolerance test (OGTT) was measured in 203 participants (29%) who had a normal OGCT result. It was found that 14.5% of overweight women and 13.5% of normal weight women had an abnormal OGTT result even when their OGCT result was normal.  Conclusions: Obesity is associated with an increased risk of cesarean section (especially elective cesarean), gestational hypertension, macrosomia, and miscarriage. It also increases the risk of gestational diabetes