Efficient and Effective Methodologies for Exploring and Prediction Movement Patterns in Large Networks

In the era of Big Data the prevalence of networks of all kinds has grown dramatically, and analysing (mining) such networks to support decision-making processes has become an extremely important subject for research, typically with a view to some social and/or economic gain. This thesis describes research work within the theme of Movement Pattern Mining (MPM) as applied to large network data. MPM is a type of frequent pattern mining that provides observation into how information is exchanged between objects in large networks. In the context of the work described in this thesis, the focus is on how the concept of Movement Patterns (MPs) can be extracted from large networks efficiently and effectively, and how such movement patterns can best be utilised so as to predict future movement. The work describes how, by utilising big data facilities like Share/Distribute Memory Systems and Hadoop/MapReduce, novel data mining based techniques can be used, not only to extract MPs from large networks, but also how they can be utilised for prediction purposes. To this end, the works in this thesis are divided into two parts. The first part is concerned with an investigation of an efficient mechanism for MPM. The second part is concerned with the utilisation of the extracted MPs in the context of prediction. For evaluation purposes, two large network datasets were used: The Great Britain Cattle Tracking System database and the Jiayuan Social Network. The evaluation indicates that an efficient and effective mechanism for identifying and extracting MPs form large networks, and subsequently using then MPs for prediction purposes, has been established

Intelligent SDN Traffic Classification Using Deep Learning: Deep-SDN

Accurate traffic classification is fundamentally important for various network activities such as fine-grained network management and resource utilisation. Port-based approaches, deep packet inspection and machine learning are widely used techniques to classify and analyze network traffic flows. However, over the past several years, the growth of Internet traffic has been explosive due to the greatly increased number of Internet users. Therefore, both port-based and deep packet inspection approaches have become inefficient due to the exponential growth of the Internet applications that incurs high computational cost. The emerging paradigm of software-defined networking has reshaped the network architecture by detaching the control plane from the data plane to result in a centralised network controller that maintains a global view over the whole network on its domain. In this paper, we propose a new deep learning model for software-defined networks that can accurately identify a wide range of traffic applications in a short time, called Deep-SDN. The performance of the proposed model was compared against the state-of-the-art and better results were reported in terms of accuracy, precision, recall, and f-measure. It has been found that 96% as an overall accuracy can be achieved with the proposed model. Based on the obtained results, some further directions are suggested towards achieving further advances in this research area

Deep Learning Towards Intelligent Vehicle Fault Diagnosis

Recently, the rapid development of automotive industries has given rise to large multidimensional datasets both in the production sites and after-sale services. Fault diagnostic systems are one of the services that the automotive industries provide. As a consequence of the rapid development of cars features, traditional rule-based diagnostic systems became very limited. Therefore, more sophisticated AI approaches need to be investigated towards more efficient solutions. In this paper, we focus on utilising deep learning so as to build a diagnostic system that is able to estimate the required services in an efficient and effective way. We propose a new model, called Deep Symptoms-Based Model Deep-SBM, as an approach to predict a wide range of faults by relying on the deep learning technique. The new proposed model is validated through a set of experiments in order to demonstrate how the underlying model runs and its impact on improving the overall performance metrics. We have applied the Deep-SBM on a real historical diagnostic data provided by Cognitran Ltd. The performance of the Deep-SBM was compared against the state-of-the-art approaches and better result has been reported in terms of accuracy, precision, recall, and F-Score. Based on the obtained results, some further directions are suggested in this context. The final goal is having fault prediction data collected online relying on IoT

Implications of Isoprostanes and Matrix Metalloproteinase-7 Having Potential Role in the Development of Colorectal Cancer in Males

BackgroundColorectal cancer (CRC) is the third most common type of cancer and leading cause of death worldwide. Major risk factors involved in the development of CRC are increased dietary sources, genetics, and increasing age. Purpose of the study was to find the role of different variables in the progression of CRC.Methodology50 blood samples from CRC patients and 20 samples from control were collected. Serum was separated from the blood by centrifugation. This serum was assessed for several antioxidants like superoxide dismutase (SOD), glutathione, glutathione peroxidase, glutathione reductase, catalase, vitamin A, C, and E, and pro-oxidants such as malondialdehyde, advanced oxidation protein products (AOPPs), and AGEs according to their respective protocols. Matrix metalloproteinase-7 (MMP-7) and isoprostanes were assessed by ELISA kits.ResultsLower levels of GSH (4.86 ± 0.78 vs 9.65 ± 1.13 μg/dl), SOD (0.08 ± 0.012 vs 0.46 ± 0.017 μg/dl), CAT (2.45 ± 0.03 vs 4.22 ± 0.19 μmol/mol of protein), and GRx (5.16 ± 0.06 vs 7.23 ± 0.36 μmol/ml) in the diseased group were recorded as compared with control. Higher levels of GPx (6.64 ± 0.19 mmol/dl) were observed in the subjects in comparison with control group (1.58 ± 0.30 mmol/dl). Highly significant decreased levels of vitamin A (0.81 ± 0.07 vs 2.37 ± 0.15 mg/ml), vitamin E (15.42 ± 1.26 vs 25.96 ± 2.19 mg/ml), and vitamin C (47.67 ± 7.69 vs 80.37 ± 10.21 mg/ml) were observed in the patients in contrast to control group. The reversal of antioxidants in later stages of CRC may be due to compensatory mechanisms in cancerous cells. The levels of MDA (nmol/ml) were also assessed, which shows significantly increased level in CRC patients as compared with control groups (3.67 ± 0.19 vs 1.31 ± 0.27). The levels of protein oxidation products [AGEs (2.74 ± 0.16 vs 0.84 ± 0.05 IU) and AOPPs (1.32 ± 0.02 vs 0.82 ± 0.07 ng/ml)] were significantly increased in subjects as compared with control. The levels of MMP-7 (64.75 ± 3.03 vs 50.61 ± 4.09 ng/ml) and isoprostanes (0.71 ± 0.03 vs 0.16 ± 0.02 ng/ml) were also analyzed. This shows that the levels of isoprostanes increased due to high lipid peroxidation mediate higher levels of MMP-7, which promotes development of CRC.ConclusionFollowing study suggested that elevated oxidative and inflammatory status along with lipid peroxidation and matrix metalloproteinases are the chief contributors in the progression of CRC