82 research outputs found
An application of genetic algorithms to chemotherapy treatment.
Get PDFThe present work investigates methods for optimising cancer chemotherapy within the bounds of clinical acceptability and making this optimisation easily accessible to oncologists. Clinical oncologists wish to be able to improve existing treatment regimens in a systematic, effective and reliable way. In order to satisfy these requirements a novel approach to chemotherapy optimisation has been developed, which utilises Genetic Algorithms in an intelligent search process for good chemotherapy treatments. The following chapters consequently address various issues related to this approach. Chapter 1 gives some biomedical background to the problem of cancer and its treatment. The complexity of the cancer phenomenon, as well as the multi-variable and multi-constrained nature of chemotherapy treatment, strongly support the use of mathematical modelling for predicting and controlling the development of cancer. Some existing mathematical models, which describe the proliferation process of cancerous cells and the effect of anti-cancer drugs on this process, are presented in Chapter 2. Having mentioned the control of cancer development, the relevance of optimisation and optimal control theory becomes evident for achieving the optimal treatment outcome subject to the constraints of cancer chemotherapy. A survey of traditional optimisation methods applicable to the problem under investigation is given in Chapter 3 with the conclusion that the constraints imposed on cancer chemotherapy and general non-linearity of the optimisation functionals associated with the objectives of cancer treatment often make these methods of optimisation ineffective. Contrariwise, Genetic Algorithms (GAs), featuring the methods of evolutionary search and optimisation, have recently demonstrated in many practical situations an ability to quickly discover useful solutions to highly-constrained, irregular and discontinuous problems that have been difficult to solve by traditional optimisation methods. Chapter 4 presents the essence of Genetic Algorithms, as well as their salient features and properties, and prepares the ground for the utilisation of Genetic Algorithms for optimising cancer chemotherapy treatment. The particulars of chemotherapy optimisation using Genetic Algorithms are given in Chapter 5 and Chapter 6, which present the original work of this thesis. In Chapter 5 the optimisation problem of single-drug chemotherapy is formulated as a search task and solved by several numerical methods. The results obtained from different optimisation methods are used to assess the quality of the GA solution and the effectiveness of Genetic Algorithms as a whole. Also, in Chapter 5 a new approach to tuning GA factors is developed, whereby the optimisation performance of Genetic Algorithms can be significantly improved. This approach is based on statistical inference about the significance of GA factors and on regression analysis of the GA performance. Being less computationally intensive compared to the existing methods of GA factor adjusting, the newly developed approach often gives better tuning results. Chapter 6 deals with the optimisation of multi-drug chemotherapy, which is a more practical and challenging problem. Its practicality can be explained by oncologists' preferences to administer anti-cancer drugs in various combinations in order to better cope with the occurrence of drug resistant cells. However, the imposition of strict toxicity constraints on combining various anticancer drugs together, makes the optimisation problem of multi-drug chemotherapy very difficult to solve, especially when complex treatment objectives are considered. Nevertheless, the experimental results of Chapter 6 demonstrate that this problem is tractable to Genetic Algorithms, which are capable of finding good chemotherapeutic regimens in different treatment situations. On the basis of these results a decision has been made to encapsulate Genetic Algorithms into an independent optimisation module and to embed this module into a more general and user-oriented environment - the Oncology Workbench. The particulars of this encapsulation and embedding are also given in Chapter 6. Finally, Chapter 7 concludes the present work by summarising the contributions made to the knowledge of the subject treated and by outlining the directions for further investigations. The main contributions are: (1) a novel application of the Genetic Algorithm technique in the field of cancer chemotherapy optimisation, (2) the development of a statistical method for tuning the values of GA factors, and (3) the development of a robust and versatile optimisation utility for a clinically usable decision support system. The latter contribution of this thesis creates an opportunity to widen the application domain of Genetic Algorithms within the field of drug treatments and to allow more clinicians to benefit from utilising the GA optimisation
Anomaly monitoring framework based on intelligent data analysis.
Get PDFReal-time data processing has become an increasingly important challenge as the need for faster analysis of big data widely manifests itself. In this research, several Computational Intelligence methods have been applied for identifying possible anomalies in two real world sensor-based datasets. By achieving similar results to those of well respected methods, the proposed framework shows a promising potential for anomaly detection and its lightweight, real-time features make it applicable to a range of in-situ data analysis scenarios
Automated inferential measurement system for traffic surveillance: enhancing situation awareness of UAVs by computational intelligence.
Get PDFAn adaptive inferential measurement framework for control and automation systems has been proposed in the paper and tested on simulated traffic surveillance data. The use of the framework enables making inferences related to the presence of anomalies in the surveillance data with the help of statistical, computational and clustering analysis. Moreover, the performance of the ensemble of these tools can be dynamically tuned by a computational intelligence technique. The experimental results have demonstrated that the framework is generally applicable to various problem domains and reasonable performance is achieved in terms of inferential accuracy. Computational intelligence can also be effectively utilised for identifying the main contributing features in detecting anomalous data points within the surveillance data
Securing cyber-physical systems with two-level anomaly detection strategy.
Get PDFCyber-physical system (CPS) represents the integration of digital technologies with physical processes to revolutionize Industry 4.0 by optimizing the industrial processes. However, due to the integration of interconnected devices, the internet, and physical processes, CPS is more susceptible to cyber and physical anomalies. Anomaly detection systems can be implemented to enhance CPS security by actively identifying both physical and cyber irregularities through continuous data monitoring. To this end, this study proposes a two-level detection strategy to secure CPS from all types of anomalies. The first level uses a hybrid Convolutional Neural Network and Long Short-Term Memory to perform the binary classification. Whereas the second level uses a Gradient Boosting Machine to detect the exact type of anomaly. The proposed methodology is evaluated on the physical and network hardware-in-the-loop dataset obtained from a Water Distribution Testbed. The evaluation results demonstrated a high F1-score of 100% and 97.3% on network and physical data respectively, exhibiting its efficiency in accurately predicting anomalies while capturing the most relevant instances to achieve high accuracy
Bayesian optimized autoencoder for predictive maintenance of smart packaging machines.
Get PDFSmart packaging machines incorporate various components (blades, motors, films) to accomplish the packaging process and are involved in almost all types of the manufacturing industry. Proper maintenance and monitoring of the components over time can help industries to maintain a sustainable production environment. On the contrary, a faulty system may degrade production efficiency and increase the cost. Smart packaging machines comprising several sensors can generate time series data and leverage data driven condition monitoring models to overcome faulty conditions. In this work, we have studied the application of Autoencoder as a data driven condition monitoring tool for the predictive maintenance of packaging machines. The trained Autoencoder on the new system's data can detect worn or degraded components over time. We have also used the Bayesian optimization algorithm to tune the hyper-parameters of the Autoencoder for better predictive performance. Moreover, the reconstruction error is analyzed to identify the worn components in the packaging machine
Topology for preserving feature correlation in tabular synthetic data.
Get PDFTabular synthetic data generating models based on Generative Adversarial Network (GAN) show significant contributions to enhancing the performance of deep learning models by providing a sufficient amount of training data. However, the existing GAN-based models cannot preserve the feature correlations in synthetic data during the data synthesis process. Therefore, the synthetic data become unrealistic and creates a problem for certain applications like correlation-based feature weighting. In this short theoretical paper, we showed a promising approach based on the topology of datasets to preserve correlation in synthetic data. We formulated our hypothesis for preserving correlation in synthetic data and used persistent homology to show that the topological spaces of the original and synthetic data have dissimilarity in topological features, especially in 0th and 1st Homology groups. Finally, we concluded that minimizing the difference in topological features can make the synthetic data space locally homeomorphic to the original data space, and the synthetic data may preserve the feature correlation under homeomorphism conditions
Inferential measurements for situation awareness: enhancing traffic surveillance by machine learning.
Get PDFThe paper proposes a generic approach to building inferential measurement systems. The large amount of data needed to be acquired and processed by such systems necessitates the use of machine learning techniques. In this study, an inferential measurement system aimed at enhancing situation awareness has been developed and tested on simulated traffic surveillance data. The performance of several Computational Intelligence techniques within this system has been examined and compared on the data containing anomalous driving patterns
Advanced persistent threats detection based on deep learning approach.
Get PDFAdvanced Persistent Threats (APTs) have been a major challenge in securing both Information Technology (IT) and Operational Technology (OT) systems. APT is a sophisticated attack that masquerade their actions to navigates around defenses, breach networks, often, over multiple network hosts and evades detection. It also uses "low-and-slow" approach over a long period of time. Resource availability, integrity, and confidentiality of the operational cyber-physical systems (CPS) state and control is highly impacted by the safety and security measures in place. A framework multi-stage detection approach termed "APTDASAC" to detect different tactics, techniques, and procedures (TTPs) used during various APT steps is proposed. Implementation was carried out in three stages: (i) Data input and probing layer - this involves data gathering and preprocessing, (ii) Data analysis layer; applies the core process of "APTDASAC" to learn the behaviour of attack steps from the sequence data, correlate and link the related output and, (iii) Decision layer; the ensemble probability approach is utilized to integrate the output and make attack prediction. The framework was validated with three different datasets and three case studies. The proposed approach achieved a significant attacks detection capability of 86.36% with loss as 0.32%, demonstrating that attack detection techniques applied that performed well in one domain may not yield the same good result in another domain. This suggests that robustness and resilience of operational systems state to withstand attack and maintain system performance are regulated by the safety and security measures in place, which is specific to the system in question
Ensemble common features technique for lightweight intrusion detection in industrial control system.
Get PDFThe integration of the Industrial Control System (ICS) with corporate intranets and the internet has exposed the previously isolated SCADA system to a wide range of cyberattacks. Interestingly, the vulnerabilities in the Modbus protocol, with which the ICS communicates, make data obfuscation and communication between component entities less secure. In this work, we propose a Common Features Technique (CFT) for Lightweight Intrusion Detection based on an ensembled feature selection approach. Our Common Features Technique, which used fewer features, was able to detect intrusion at the same level as models using information gain, Chi-Squared, and Gini Index feature selection techniques datasets after fitting Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbour (KNN) models. More importantly, when p-values were computed, the CFT model computation time and memory usage were statistically significantly different at 95% and 90% Confidence Interval (CI) when compared to the model on the other techniques
Detection of false command and response injection attacks for cyber physical systems security and resilience.
Get PDFThe operational cyber-physical system (CPS) state, safety and resource availability is impacted by the safety and security measures in place. This paper focused on i) command injection (CI) attack that alters the system behaviour through injection of false control and configuration commands into a control system and ii) response injection (RI) attacks that modifies the response from server to client, thereby providing false information about system state. In this project, we implemented deep learning (DL) multi-layered security model approach for securing industrial control system (ICS) against malicious CI and RI attacks. We validated this approach with two case studies: i) network transactions between a Remote Terminal Unit (RTU) and a Master Control Unit (MTU) in-house SCADA gas pipeline control system and ii) a case study of command and response injection attacks. Based on this project result, we show that the proposed approach achieved a significant attacks detection capability of 96.50%. Also, demonstrated that performance of attack detection techniques applied can be influences by the nature of network transactions with respect to the domain of application. Hence, robustness and resilience of operational CPS state and performance are influenced by the safety and security measures in place which is specific to the CPS device in question
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