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Security issues in statistical databases and implementation of audit expert

- Ahmad, Abeed, Miller, Mirka

The influence of probiotics on gastrointestinal tract infections among children attending childcare : a systematic review and meta-analysis

- Ahmad, Hafiz, Peck, Blake, Terry, Daniel

  • Authors: Ahmad, Hafiz , Peck, Blake , Terry, Daniel
  • Date: 2022
  • Type: Text , Journal article , Review
  • Relation: Journal of Applied Microbiology Vol. 132, no. 3 (2022), p. 1636-1651
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  • Description: Current literature related to the impact of probiotics on the incidence of gastrointestinal tract infections (GITIs) has shown mixed results and no systematic review available with pooled analysis exists. Thus, the aim of this systematic review was to provide contemporary evidence regarding the overall and strain-specific influence of probiotics in preventing GITIs among infants and children attending childcare centres. The review shortlisted 18 RCTs after screening through the initial search results of 779 articles. However, only 15 trials were deemed eligible, addressing at least one outcome in the pooled analysis. It is concluded that the supplementation of probiotics (overall effect) may reduce the risk of GITI episode by 26%, with Lacticaseibacillus paracasei, Limosilactobacillus reuteri and Lacticaseibacillus rhamnosus GG being specifically potent probiotic strains in reducing GITI episode, duration of infection and absence from childcare respectively. There is insufficient evidence to determine the effect of Bifidobacterium animalis subsp. lactis BB-12 based on the findings of the trials included in this review. © 2021 The Society for Applied Microbiology

Rerouting in advance for preempted IR calls in QoS-enabled networks

- Ahmad, Iftekhar, Kamruzzaman, Joarder, Habibi, Daryoush

  • Authors: Ahmad, Iftekhar , Kamruzzaman, Joarder , Habibi, Daryoush
  • Date: 2008
  • Type: Text , Journal article
  • Relation: Computer Communications Vol. 31, no. 17 (2008), p. 3922-3928
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  • Description: When network resources are shared between Instantaneous Request (IR) and Book-Ahead (BA) connections, activation of future BA connections may cause preemption of on-going IR connections due to resource scarcity. Rerouting of preempted calls via alternative feasible paths is often considered as the final option to restore and maintain service continuity. Existing rerouting techniques, however, do not ensure acceptably low service disruption time and suffer from high failure rate and low network utilization. In this work, a new rerouting strategy is proposed that estimates the future resource scarcity, identifies the probable candidate connections for preemption and initiates the rerouting process in advance for those connections. Simulations on a widely used network topology suggest that the proposed rerouting scheme achieves a higher successful rerouting rate with lower service disruption time, while not compromising other network performance metrics like utilization and call blocking rate.

Application of artificial intelligence to improve quality of service in computer networks

- Ahmad, Iftekhar, Kamruzzaman, Joarder, Habibi, Daryoush

  • Authors: Ahmad, Iftekhar , Kamruzzaman, Joarder , Habibi, Daryoush
  • Date: 2012
  • Type: Text , Journal article
  • Relation: Neural Computing & Applications Vol. 21, no. 1 (2012), p. 81-90
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  • Description: Resource sharing between book-ahead (BA) and instantaneous request (IR) reservation often results in high preemption rates for ongoing IR calls in computer networks. High IR call preemption rates cause interruptions to service continuity, which is considered detrimental in a QoS-enabled network. A number of call admission control models have been proposed in the literature to reduce preemption rates for ongoing IR calls. Many of these models use a tuning parameter to achieve certain level of preemption rate. This paper presents an artificial neural network (ANN) model to dynamically control the preemption rate of ongoing calls in a QoS-enabled network. The model maps network traffic parameters and desired operating preemption rate by network operator providing the best for the network under consideration into appropriate tuning parameter. Once trained, this model can be used to automatically estimate the tuning parameter value necessary to achieve the desired operating preemption rates. Simulation results show that the preemption rate attained by the model closely matches with the target rate.

An intelligent model to control preemption rate of instantaneous request calls in networks with book- ahead reservation

- Ahmad, Iftekhar, Kamruzzaman, Joarder, Habibi, Daryoush, Islam, Farzana

  • Authors: Ahmad, Iftekhar , Kamruzzaman, Joarder , Habibi, Daryoush , Islam, Farzana
  • Date: 2008
  • Type: Text , Conference proceedings
  • Relation: 2008 Australasian Telecommunication Networks and Applications Conference, ATNAC, Adelaide 2008. Published in Proceedings of Australasian Telecommunication Networks and Applications conference , IEEE (pp.344-34)
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  • Description: Resource sharing between Book-Ahead (BA) and Instantaneous Request (IR) reservation often results in high preemption rate of on-going IR calls. High IR call preemption rate causes interruption to service continuity which is considered as detrimental in a QoS-enabled network. A number of call admission control models have been proposed in literature to reduce the preemption rate of on-going IR calls. Many of these models use a tuning parameter to achieve certain level of preemption rate. This paper presents an Artificial Neural Network (ANN) model to dynamically control the preemption rate of on-going calls in a QoS-enabled network. The model maps network traffic parameters and desired level of preemption rate into appropriate tuning parameter. Once trained, this model can be used to automatically estimate the tuning parameter value necessary to achieve the desired level of preemption rate. Simulation results show that the preemption rate attained by the model closely matches with the target rat

Towards intrusion detection to secure VANET-assisted healthcare monitoring system

- Ahmad, Iftikhar, Ahmad, Irshad, Amin, Fazal,, Imran, Muhammad, Sattar, Kashif

  • Authors: Ahmad, Iftikhar , Ahmad, Irshad , Amin, Fazal, , Imran, Muhammad , Sattar, Kashif
  • Date: 2017
  • Type: Text , Journal article
  • Relation: Journal of Medical Imaging and Health Informatics Vol. 7, no. 6 (2017), p. 1391-1398
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  • Description: Remote healthcare monitoring (RHM) has been used in contemporary disease management to improve the quality of life. A Secure VANET-Assisted Remote Healthcare Monitoring System is very importance due to privacy and sensitivity of the health data. In wireless network different kind of attacks produce and involve the core functionality of VANET-Assisted Remote Healthcare Monitoring System. DoS and DDoS are two important attacks and both are related to the network availability. This study provides the detail description and working mechanism of DoS and DDoS attack in VANET-Assisted Remote Healthcare Monitoring System. Further, it proposes the possible solutions to detect and prevent such attacks in network and make sure for end users to continuous access the network. Moreover, the random forest algorithm is proposed and applied to the benchmarked dataset for the detection of the attacks that may improve the security of RHM systems. The performance is addressed in accuracy, precision and recall metrics. © 2017 American Scientific Publishers All rights reserved. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Muhammad Imran” is provided in this record**

VANET–LTE based heterogeneous vehicular clustering for driving assistance and route planning applications

- Ahmad, Iftikhar, Noor, Rafidah, Ahmedy, Ismail, Shah, Syed, Imran, Muhammad

  • Authors: Ahmad, Iftikhar , Noor, Rafidah , Ahmedy, Ismail , Shah, Syed , Imran, Muhammad
  • Date: 2018
  • Type: Text , Journal article
  • Relation: Computer Networks Vol. 145, no. (2018), p. 128-140
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  • Description: The Internet of vehicles incorporates multiple access networks and technologies to connect vehicles on roads. These vehicles usually require the use of individual long-term evolution (LTE) connections to send/receive data to/from a remote server to make smart decisions regarding route planning and driving. An increasing number of vehicles on the roads may not only overwhelm LTE network usage but also incur added cost. Clustering helps minimize LTE usage, but the high speed of vehicles renders connections unstable and unreliable not only among vehicles but also between vehicles and the LTE network. Moreover, non-cooperative behavior among vehicles within a cluster is a bottleneck in sharing costly data acquired from the Internet. To address these issues, we propose a novel destination- and interest-aware clustering (DIAC) mechanism. DIAC primarily incorporates a strategic game-theoretic algorithm and a self-location calculation algorithm. The former allows vehicles to participate/cooperate and enforces a fair-use policy among the cluster members (CMs), whereas the latter enables CMs to calculate their location coordinates in the absence of a global positioning system under an urban topography. DIAC strives to reduce the frequency of link failures not only among vehicles but also between each vehicle and the 3G/LTE network. The mechanism also considers vehicle mobility and LTE link quality and exploits common interests among vehicles in the cluster formation phase. The performance of the DIAC mechanism is validated through extensive simulations, whose results demonstrate that the performance of the proposed mechanism is superior to that of similar and existing approaches. © 2018 Elsevier B.V.

Characterizing the role of vehicular cloud computing in road traffic management

- Ahmad, Iftikhar, Noor, Rafidah, Ali, Ihsan, Imran, Muhammad, Vasilakos, Athanasios

  • Authors: Ahmad, Iftikhar , Noor, Rafidah , Ali, Ihsan , Imran, Muhammad , Vasilakos, Athanasios
  • Date: 2017
  • Type: Text , Journal article
  • Relation: International Journal of Distributed Sensor Networks Vol. 13, no. 5 (2017), p.
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  • Description: Vehicular cloud computing is envisioned to deliver services that provide traffic safety and efficiency to vehicles. Vehicular cloud computing has great potential to change the contemporary vehicular communication paradigm. Explicitly, the underutilized resources of vehicles can be shared with other vehicles to manage traffic during congestion. These resources include but are not limited to storage, computing power, and Internet connectivity. This study reviews current traffic management systems to analyze the role and significance of vehicular cloud computing in road traffic management. First, an abstraction of the vehicular cloud infrastructure in an urban scenario is presented to explore the vehicular cloud computing process. A taxonomy of vehicular clouds that defines the cloud formation, integration types, and services is presented. A taxonomy of vehicular cloud services is also provided to explore the object types involved and their positions within the vehicular cloud. A comparison of the current state-of-the-art traffic management systems is performed in terms of parameters, such as vehicular ad hoc network infrastructure, Internet dependency, cloud management, scalability, traffic flow control, and emerging services. Potential future challenges and emerging technologies, such as the Internet of vehicles and its incorporation in traffic congestion control, are also discussed. Vehicular cloud computing is envisioned to have a substantial role in the development of smart traffic management solutions and in emerging Internet of vehicles. © The Author(s) 2017.

An efficient network intrusion detection and classification system

- Ahmad, Iftikhar, Haq, Qazi, Imran, Muhammad, Alassafi, Madini, Alghamdi, Rayed

  • Authors: Ahmad, Iftikhar , Haq, Qazi , Imran, Muhammad , Alassafi, Madini , Alghamdi, Rayed
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Mathematics Vol. 10, no. 3 (2022), p.
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  • Description: Intrusion detection in computer networks is of great importance because of its effects on the different communication and security domains. The detection of network intrusion is a challenge. Moreover, network intrusion detection remains a challenging task as a massive amount of data is required to train the state-of-the-art machine learning models to detect network intrusion threats. Many approaches have already been proposed recently on network intrusion detection. However, they face critical challenges owing to the continuous increase in new threats that current systems do not understand. This paper compares multiple techniques to develop a network intrusion detection system. Optimum features are selected from the dataset based on the correlation between the features. Furthermore, we propose an AdaBoost-based approach for network intrusion detection based on these selected features and present its detailed functionality and performance. Unlike most previous studies, which employ the KDD99 dataset, we used a recent and comprehensive UNSW-NB 15 dataset for network anomaly detection. This dataset is a collection of network packets exchanged between hosts. It comprises 49 attributes, including nine types of threats such as DoS, Fuzzers, Exploit, Worm, shellcode, reconnaissance, generic, and analysis Backdoor. In this study, we employ SVM and MLP for comparison. Finally, we propose AdaBoost based on the decision tree classifier to classify normal activity and possible threats. We monitored the network traffic and classified it into either threats or non-threats. The experimental findings showed that our proposed method effectively detects different forms of network intrusions on computer networks and achieves an accuracy of 99.3% on the UNSW-NB15 dataset. The proposed system will be helpful in network security applications and research domains. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

An optimized hybrid deep intrusion detection model (HD-IDM) for enhancing network security

- Ahmad, Iftikhar, Imran, Muhammad, Qayyum, Abdul, Ramzan, Muhammad, Alassafi, Madini

  • Authors: Ahmad, Iftikhar , Imran, Muhammad , Qayyum, Abdul , Ramzan, Muhammad , Alassafi, Madini
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Mathematics Vol. 11, no. 21 (2023), p.
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  • Description: Detecting cyber intrusions in network traffic is a tough task for cybersecurity. Current methods struggle with the complexity of understanding patterns in network data. To solve this, we present the Hybrid Deep Learning Intrusion Detection Model (HD-IDM), a new way that combines GRU and LSTM classifiers. GRU is good at catching quick patterns, while LSTM handles long-term ones. HD-IDM blends these models using weighted averaging, boosting accuracy, especially with complex patterns. We tested HD-IDM on four datasets: CSE-CIC-IDS2017, CSE-CIC-IDS2018, NSL KDD, and CIC-DDoS2019. The HD-IDM classifier achieved remarkable performance metrics on all datasets. It attains an outstanding accuracy of 99.91%, showcasing its consistent precision across the dataset. With an impressive precision of 99.62%, it excels in accurately categorizing positive cases, crucial for minimizing false positives. Additionally, maintaining a high recall of 99.43%, it effectively identifies the majority of actual positive cases while minimizing false negatives. The F1-score of 99.52% emphasizes its robustness, making it the top choice for classification tasks requiring precision and reliability. It is particularly good at ROC and precision/recall curves, discriminating normal and harmful network activities. While HD-IDM is promising, it has limits. It needs labeled data and may struggle with new intrusion methods. Future work should find ways to handle unlabeled data and adapt to emerging threats. Also, making HD-IDM work faster for real-time use and dealing with scalability challenges is key for its broader use in changing network environments. © 2023 by the authors.

A cooperative heterogeneous vehicular clustering mechanism for road traffic management

- Ahmad, Iftikhar, Noor, Rafidah, Zaba, Muhammad, Qureshi, Muhammad, Imran, Muhammad, Shoaib, Muhammad

  • Authors: Ahmad, Iftikhar , Noor, Rafidah , Zaba, Muhammad , Qureshi, Muhammad , Imran, Muhammad , Shoaib, Muhammad
  • Date: 2020
  • Type: Text , Journal article
  • Relation: International Journal of Parallel Programming Vol. 48, no. 5 (2020), p. 870-889
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  • Description: The vehicular ad-hoc networks integrates with long-term evolution (LTE) forming a heterogeneous network, capable of providing seamless connectivity, which meets the communication requirements of intelligent transportation systems. However, heterogeneous network-based applications involve LTE resource (data and spectrum) usage cost and must be taken care while developing such a solution. One of the scenarios is the access of the information to/from remote server over the internet via LTE for road traffic management applications. Although clustering of the vehicle is significant to minimize the data and LTE network usage, however, the problem of non-cooperation of the vehicles in clustering process and within a cluster are major issues in sharing costly data acquired from the internet. Because, who and why one (vehicle) should pay the cost is the big question, proliferating the non-cooperative behavior among the cluster members. To solve these issues, strategic game-theoretic based clustering mechanism named as cooperative interest-aware clustering (CIAC) is developed. The proposed CIAC not only balance the cost of usage by controlling non-cooperative behavior among the vehicles within the cluster but at the same time motivate vehicles to participate in the clustering process to share the data and cost as well. It consists of a cluster head selection process based on the strategic game-theoretic approach and a fair-use policy. The implementation results show superiority in performance of our protocol over the existing approaches. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

Constraint-based evolutionary learning approach to the non-normal process performance evaluation

- Ahmad, S., Huda, Shamsul, Bakir, S., Abdollahian, Mali, Zeephongsekul, P.

  • Authors: Ahmad, S. , Huda, Shamsul , Bakir, S. , Abdollahian, Mali , Zeephongsekul, P.
  • Date: 2009
  • Type: Text , Conference paper
  • Relation: Paper presented at 3rd International Conference on Informatics and Technology 2009: ICT Opportunities in the Current Global Recession, Kuala Lumpur, Malaysia : 27th-28th October 2009 p. 26-33
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  • Description: Performance of industrial products is very important for an industry. Conventional methods for performance analysis consider a normality assumption and limited to low dimensional data. Different manufacturing processes very often have products with quality characteristics that do not follow normal distribution. In such cases fitting a known non-normal distribution to these quality characteristics would lead to erroneous results while assessing the performance of products. In this paper, we propose a novel method for non-normal multivariate process performance analysis. We have proposed a Constraint-based Evolutionary Algorithm (EA) approach for optimal estimation of the parameters of non-normal multivariate process. Furthermore, a geometric distance based method has been employed to reduce higher dimensionality of data to lower dimension. The efficacy of the proposed method is assessed by using the proportion of nonconformance (PNC) criterion to summarize the performance of EA approach. The experimental results from constraint-based EA have been compared to those obtained using steepest descent and simulated annealing (SA) approaches.
  • Description: 2003007898

Process performance evaluation using evolutionary algorithm

- Ahmad, S., Huda, Shamsul, Bakir, S., Abdollahian, Mali, Zeephongsekul, P.

  • Authors: Ahmad, S. , Huda, Shamsul , Bakir, S. , Abdollahian, Mali , Zeephongsekul, P.
  • Date: 2009
  • Type: Text , Conference paper
  • Relation: Paper presented at 2009 International Conference on Information & Knowledge Engineering, IKE 2009, Las Vegas, Nevada, U.S.A. : 13th-16th July 2009 p. 731-737
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  • Description: Nowadays every business is using different quantitative measures and techniques to assess performance of their products / services. It is well known that different manufacturing processes very often manufacture products with quality characteristics that do not follow normal distribution. In such cases, fitting a known non-normal distribution to these quality characteristics would lead to erroneous results. Furthermore, there is always more than one characteristic Critical to Quality (CTQ) in the process outcomes and very often these quality characteristics are correlated with each other. In this paper, we assess performance of such a bivariate process data which is non-normal as well as correlated. We will use the geometric distance approach to reduce the dimension of the correlated non-normal bivariate data and then fit Burr distribution to the geometric distance variable. The optimal parameters of the fitted Burr distribution are estimated using Evolutionary Algorithm (EA). The results are compared with those using Simulated Annealing (SA) algorithm. The proportion of nonconformance (PNC) for process measurements is then obtained by using the fitted Burr distributions based on the two methods. The results based on both search algorithms are then compared with the exact proportion of nonconformance of the data. Finally, a case study using real data is presented.
  • Description: 2003008140

Performance evaluation of multivariate non-normal process using metaheuristic approaches

- Ahmad, S., Abdollahian, Mali, Bhatti, M.I., Huda, Shamsul, Yearwood, John

  • Authors: Ahmad, S. , Abdollahian, Mali , Bhatti, M.I. , Huda, Shamsul , Yearwood, John
  • Date: 2014
  • Type: Text , Journal article
  • Relation: Journal of Applied Statistical Science Vol. 20, no. 3 (2014), p. 299-315
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  • Description: Multivariate process performance indices generally rely on the assumption that the process follow normal distribution but in practice its non-normal with correlated characteristics patterns. This paper proposes two metaheuristic-based approaches to fit Burr distribution to such data; a single candidate model based approach using a Simulated Annealing (SA) technique and a population based approach using a constraint-based Evolutionary Alogorithn (EA). The fitted Burr distribution is then used to estimate the proportion of Non-conforming (PNC) which is then used to fit an appropiate Burr distribution to individual Geometric distance variables. Empirical performance of the proposed methods have been evaluated on real industrial data set using PNC criterion. Experimental results demonstrate that the new approach perform well than the existing.

Development of a unified railway track stability management tool to enhance track safety

- Ahmad, Shah, Mandal, Nirmal, Chattopadhyay, Gopinath, Powell, John

  • Authors: Ahmad, Shah , Mandal, Nirmal , Chattopadhyay, Gopinath , Powell, John
  • Date: 2013
  • Type: Text , Journal article
  • Relation: Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit Vol. 227, no. 5 (2013), p. 493-516
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  • Description: Track buckling is a serious problem for railways. High longitudinal rail stresses contribute to problems such as track buckling, rail joint failure, rail breakage and failure of turnouts. The direct and indirect costs of track buckling problems are very high. The influences of rail temperature, stress-free temperature (SFT) and lateral misalignment of track on track buckling need comprehensive investigation. In this paper, an experimental design comprising strain gauges, thermocouples and rail stress sensors has been implemented on the Queensland Rail heavy haul 60 kg/m rail network. A new creep measurement technique using internal rail stress has been developed. The changes in rail neutral temperature due to the variation of actual rail temperature and the occurrence of rail creep in straight and curved track is quantified. Modes of differences of SFT in the two rails at a location, and of SFT in straight track and in curved track are discussed. The relationship of SFT to rail temperature is also presented. Daily variation in rail temperature due to ambient air temperature is presented. Field trials showed that SFT can vary by 2-3 °C during the day. Based on this finding and the derivation of an equation for change of SFT, an improvement in utilising rail creep measurements for assessing track condition has resulted. This finding suggests that it is possible to determine the SFT throughout a day rather than just a single SFT value. This paper also presents a simple track stability management tool that is based on two major parameters, namely rail stress and track resistance. Each parameter in the tool has been given three levels of value to determine the required preventive measures. Overall, the tool decides the need for speed restriction during hot weather based on the quantified parameters from the field trials and rail standards. © IMechE 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Pattern recognition in bioinformatics : Girls lose out

- Ahmad, Shandar, Chetty, Madhu, Schmidt, Bertil

  • Authors: Ahmad, Shandar , Chetty, Madhu , Schmidt, Bertil
  • Date: 2010
  • Type: Text , Journal article
  • Relation: Pattern Recognition Letter Vol. 31, no. 14 (2010), p. 2071-2072
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  • Description: Editorial- With the advent of high speed computers, in-silico studies on biological patterns in recent years have been significantly impacted by the pattern recognition techniques. In this special issue, ‘Pattern Recognition in Bioinformatics’, we present various sophisticated algorithms for a wide range of pattern recognition problems from the world of complex biological systems, whether these are specific sequence signatures – motifs that stand out in discovering its partner – or substructures in an interaction network that determines an organisms’ response to external stimuli. The 12 high-quality articles included in this special issue are essentially based on significant extensions of the selected papers presented at the Third International Conference on Pattern Recognition in Bioinformatics (PRIB 2008) held in Melbourne, Australia. All these selected papers for special issue have again undergone a thorough review by at least three reviewers who are experts in the field. The fresh review process was followed to ensure that the papers met the high standards of scientific and technical merit of the Pattern Recognition Letters journal. The issue is broadly divided into three sections of four papers each, namely (1) Section 1: Interaction Networks and Feature-based Predictions (2) Section 2: Microarray and Transcription Data Analysis (3) Section 3: Sequence Analysis and Motif Discovery

Secure passive keyless entry and start system using machine learning

- Ahmad, Usman, Song, Hong, Bilal, Awais, Alazab, Mamoun, Jolfaei, Alireza

The role of immune cells in ovarian cancer

- Ahmady, Farah

  • Authors: Ahmady, Farah
  • Date: 2022
  • Type: Text , Thesis , PhD
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  • Description: Ovarian cancer remains the leading cause of gynaecological disease related death in women worldwide. Patients given standard treatment have low survival rates and immunotherapy remains unsuccessful. This is largely due to the suppressive tumour microenvironment (TME) and the interaction of the different cells being poorly understood. This has sparked interest in understanding the functions of immune cells and their contribution to the TME. In chapter 3, B and T cells were characterised in samples collected from chemo-naïve highgrade serous ovarian cancer (HGSOC) patients and compared to benign and healthy controls. B cells were able to express the T cell exhaustion marker T cell immunoglobulin and mucin domain-3 (TIM-3) on their surface. Blood derived B cells of benign patients expressed less TIM-3 compared to high-grade and healthy donors. Alterations in TIM-3 expression on B cells suggests that the TME may not be causing B cells to harbour an exhaustion phenotype. The frequency of circulating mucosal-associated invariant T cells (MAIT cells) was reduced in benign and high-grade patients compared to healthy donors. Blood derived MAIT cells of healthy donors stimulated with 5-OP-RU antigen and cultured with ovarian cancer cell line supernatants had a reduced capacity of producing tumour necrosis factor (TNF)-a compared to control. The reduced frequency of MAIT cells at the premalignant state of the disease indicates that malignancy of the disease is not necessarily the contributing factor in the reduction observed. As TNF-a is a key anti-tumour cytokine expressed by MAIT cells, the impaired production may indicate defects in their function. Low dose-dense (LDD) chemotherapy has recently shown promise as depicted in a clinical study by Kannourakis et al. where LDD treated HGSOC patients had longer survival than those treated with maximum tolerated dose (MTD). It is postulated that this may be due to aspects of the immune system, however the effects of LDD chemotherapy on immune cells remains unknown. In chpter 4, circulating B and T cells of LDD treated patients were characterised and compared to untreated controls. The key finding of this study was the immunostimulatory attributes of LDD regimen in a range of B and T cell populations via their increased expression of TNF-a compared to untreated controls and healthy donors. The frequency of MAIT cells, however, was reduced in LDD treated patients, suggesting that these cells may not contribute to the immunostimulatory aspects, and that this regimen may impact their frequency by creating an environment which may not be compatible for MAIT cell survival. In chapter 5, expression of immuno-oncology protein markers in benign and high-grade primary and metastasised tumours were determined for alterations in hot and cold tumour regions using Digital Spatial Profiling (DSP). An increased expression of immune cell, costimulatory and inhibitory markers were apparent in high-grade tumours compared to benign. The stimulator of interferon genes (STING) marker was studied further, and its expression was also higher in high-grade tumours compared to benign, with cisplatin further enhancing its expression in ovarian cancer cell lines. There were alterations in the expression of some downstream molecules, however interferon (IFN)-b, the key cytokine produced in the activated STING pathway, was virtually non-existent. This suggests a defect in the pathway which if restored, may contribute to some of the known anti-tumour functions of the pathway. The results from these studies provide an understanding of differences in the immune profile of non-malignant and malignant ovarian cancer. These findings work together to improve our understanding of the unique TME with the aim to identify potential immune therapeutic targets for future study.
  • Description: Doctor of Philosophy

Social media analytics, learning analytics and healthcare industry : risky drinking

- Ahmed, Ashir, Martin, Jennifer, McKay, Elspeth, Towl, David, Haussegger, Zac

  • Authors: Ahmed, Ashir , Martin, Jennifer , McKay, Elspeth , Towl, David , Haussegger, Zac
  • Date: 2022
  • Type: Text , Book chapter
  • Relation: Manage Your Own Learning Analytics : Implement a Rasch Modelling Approach Chapter 5 p. 113-136
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  • Description: Much is written about social media analytics, learning analytics and health care industry research projects. However, it is rare to find all three domains in the same study, as each discipline is usually kept separate from another. The concern with this isolation approach is the probability of missing essential synergies, leading to a combined effect more significant than the sum of their particular products. Social media analytics may uncover key social media search terms that reflect the language used by groups of people engaging in risky drinking behaviour. At the same time, the view of behaviour through a psychometric testing lens may reveal other things. This chapter describes both social media analytics and learning analytics from the perspective of the health care industry. The chapter explains the intersection of social media analytics and learning analytics; in the first instance, the Rasch measurement model is used to discover the probability of agreement relating to human behaviour issues; a case study amplifies health services provision outcomes. The results from the social media analytics revealed that groups’ risky drinking behaviour data collected by Talkwalker showed linguistic variations with the double meanings of words used, which affected results. The learning analytics of alcohol consumption included a post-hoc comparison using a Bonferroni correction t-test which revealed the mean of never-married people was significantly higher than married people. The significance of these findings demonstrates that data analysis should be open to using a mixture of data analytics tools to reach a finer-grained interpretation to model other complex problems. © 2022, Springer Nature Switzerland AG.

Risky drinking social worlds in Victoria: a social media analysis

- Ahmed, Ashir, Martin, Jennifer, Towl, David, Haussegger, Zac