IDENTIFYING POSSIBLE RUMOR SPREADERS ON TWITTER USING THE SVM AND FEATURE LEVEL EXTRACTION

In everyday life, many events occur and give rise to various kinds of information, which are also rumors. Rumors can cause fear and influence public opinion about the event in question. Identifying possible rumor spreaders is extremely helpful in preventing the spread of rumors. Feature extraction can be done to expand the feature set, which consists of conversational features in the form of social networks formed from user replies, user features such as following, tweet count, verified, etc., and tweet features with text analysis such as punctuation and sentiment values. These features become instances used for classification. This study aims to identify possible spreaders of rumors on Twitter with the SVM classification model. This instance-based classification algorithm is good for linear and non-linear classification. In the non-linear classification, additional kernels are used, such as linear, RBF, and sigmoid. The research focuses on getting the best model with high performance values from all the models and kernel functions that have been defined. It was found that the SVM classification model with the RBF kernel has a high overall performance value for each data combination with a ratio of the amount of data is 1:1 or the difference is very large. This model gives accurate results with an average of 97.02%. With a wide distribution of data, the SVM classification model with the RBF kernel is able to map the data properly

4OR comes of age: Editorial note

This is the traditional triennial note used by the Editors to give the readers of 4OR information on the state of the journal and its future. In the three years that have passed since the last editorial note, three volumes (each containing four issues) of the journal have been published: vol. 16 (2018), vol. 17 (2019), and vol. 18 (2020)

Margin Optimal Classification Trees

In recent years there has been growing attention to interpretable machine learning models which can give explanatory insights on their behavior. Thanks to their interpretability, decision trees have been intensively studied for classification tasks, and due to the remarkable advances in mixed-integer programming (MIP), various approaches have been proposed to formulate the problem of training an Optimal Classification Tree (OCT) as a MIP model. We present a novel mixed-integer quadratic formulation for the OCT problem, which exploits the generalization capabilities of Support Vector Machines for binary classification. Our model, denoted as Margin Optimal Classification Tree (MARGOT), encompasses the use of maximum margin multivariate hyperplanes nested in a binary tree structure. To enhance the interpretability of our approach, we analyse two alternative versions of MARGOT, which include feature selection constraints inducing local sparsity of the hyperplanes. First, MARGOT has been tested on non-linearly separable synthetic datasets in 2-dimensional feature space to provide a graphical representation of the maximum margin approach. Finally, the proposed models have been tested on benchmark datasets from the UCI repository. The MARGOT formulation turns out to be easier to solve than other OCT approaches, and the generated tree better generalizes on new observations. The two interpretable versions are effective in selecting the most relevant features and maintaining good prediction quality

Evaluation of machine learning classifiers for mineralogy mapping based on near infrared hyperspectral imaging

The exploration of mineral resources is a major challenge in a world that seeks sustainable energy, renewable energy, advanced engineering, and new commercial technological devices. The rapid decrease in mineral reserves shifted the focus to under-explored and low accessibility areas that led to the use of on-site portable techniques for mineral mapping purposes, such as near infrared hyperspectral image sensors. The large datasets acquired with these instruments needs data pre-processing, a series of mathematical manipulations that can be achieved using machine learning. The aim of this thesis is to improve an existing method for mineralogy mapping, by focusing on the mineral classification phase. More specifically, a spectral similarity index was utilized to support machine learning classifiers. This was introduced because of the inability of the employed classification models to recognize samples that are not part of a given database; the models always classified samples based on one of the known labels of the database. This could be a problem in hyperspectral images as the pure component found in a sample could correspond to a mineral but also to noise or artefacts due to a variety of reasons, such as baseline correction. The spectral similarity index calculates the similarity between a sample spectrum and its assigned database class spectrum; this happens through the use of a threshold that defines whether the sample belongs to a class or not. The metrics utilized in the spectral similarity index were the spectral angler mapper, the correlation coefficient and five different distances. The machine learning classifiers used to evaluate the spectral similarity index were the decision tree, k-nearest neighbor, and support vector machine. Simulated distortions were also introduced in the dataset to test the robustness of the indexes and to choose the best classifier. The spectral similarity index was assessed with a dataset of nine minerals acquired from the Geological Survey of Finland retrieved from a Specim SWIR camera. The validation of the indexes was assessed with two mine samples obtained with a VTT active hyperspectral sensor prototype. The support vector machine was chosen after the comparison between the three classifiers as it showed higher tolerance to distorted data. With the evaluation of the spectral similarity indexes, was found out that the best performances were achieved with SAM and Chebyshev distance, which maintained high stability with smaller and bigger threshold changes. The best threshold value found is the one that, in the dataset analysed, corresponded to the number of spectra available for each class. As for the validation procedure no reference was available; because of this reason, the results of the mine samples obtained with the spectral similarity index were compared with results that can be obtained through visual interpretation, which were in agreement. The method proposed can be useful to future mineral exploration as it is of great importance to correctly classify minerals found during explorations, regardless the database utilized

A Filter-SQP strategy for training Support Vector Machine models

This paper introduces a filtering strategy for addressing optimization problems arising in binary Support Vector Machine classification. The training optimization problem aims to solve the dual formulation which involves a quadratic objective function subjected to a linear and box constraints. Our approach employs a Filter algorithm with Sequential Quadratic Programming iterations that minimize the quadratic Lagrangian approximations. Notably, we utilize the exact Hessian matrix in our numerical experiments to seek the desired classification function. Moreover, we present a Filter algorithm combined with the Augmented Lagrangian method aiming to accelerate the algorithm convergence. To substantiate our method's effectiveness, we conduct numerical experiments through MATLAB, comparing outcomes with alternative methodologies detailed in existing literature. Numerical experiments shows that the Filter--SQP combined with Augmented Lagrangian method is competitive and efficient method compared with an interior-point based solver and LIBSVM software in relation of classification metrics and CPU-time

Enhancing Classification and Regression Tree-Based Models by means of Mathematical Optimization

This PhD dissertation bridges the disciplines of Operations Research and Machine Learning by developing novel Mathematical Optimization formulations and numerical solution approaches to build classification and regression tree-based models. Contrary to classic classification and regression trees, built in a greedy heuristic manner, formulating the design of the tree model as an optimization problem allows us to easily include, either as hard or soft constraints, desirable global structural properties. In this PhD dissertation, we illustrate this flexibility to model: sparsity, as a proxy for interpretability, by controlling the number of non-zero coefficients, the number of predictor variables and, in the case of functional ones, the proportion of the domain used for prediction; an important social criterion, the fairness of the model, which aims to avoid predictions that discriminate against race, or other sensitive features; and the cost-sensitivity for groups at risk, by ensuring an acceptable accuracy performance for them. Moreover, we provide in a natural way the impact that continuous predictor variables have on each individual prediction, thus enhancing the local explainability of tree models. All the approaches proposed in this thesis are formulated through Continuous Optimization problems that are scalable with respect to the size of the training sample, are studied theoretically, are tested in real data sets and are competitive in terms of prediction accuracy against benchmarks. This, together with the good properties summarized above, is illustrated through the different chapters of this thesis. This PhD dissertation is organized as follows. The state of the art in the field of (optimal) decision trees is fully discussed in Chapter 1, while the next four chapters state our methodology. Chapter 2 introduces in detail the general framework that threads the chapters in this thesis: a randomized tree with oblique cuts. Particularly, we present our proposal to deal with classification problems, which naturally provides probabilistic output on class membership tailored to each individual, in contrast to the most popular existing approaches, where all individuals in the same leaf node are assigned the same probability. Preferences on classification rates in critical classes are successfully handled through cost-sensitive constraints. Chapter 3 extends the methodology for classification in Chapter 2 to additionally handle sparsity. This is modeled by means of regularizations with polyhedral norms added to the objective function. The sparsest tree case is theoretically studied. Our ability to easily trade in some of our classification accuracy for a gain in sparsity is shown. In Chapter 4, the findings obtained in Chapters 2 and 3 are adapted to construct sparse trees for regression. Theoretical properties of the solutions are explored. The scalability of our approach with respect to the size of the training sample, as well as local explanations on the continuous predictor variables, are illustrated. Moreover, we show how this methodology can avoid the discrimination of sensitive groups through fairness constraints. Chapter 5 extends the methodology for regression in Chapter 4 to consider functional predictor variables instead. Simultaneously, the detection of a reduced number of intervals that are critical for prediction is performed. The sparsity in the proportion of the domain of the functional predictor variables to be used is also modeled through a regularization term added to the objective function. The obtained trade off between accuracy and sparsity is illustrated. Finally, Chapter 6 closes the thesis with general conclusions and future lines of research.Esta tesis combina las disciplinas de Investigación Operativa y Aprendizaje Automático a través del desarrollo de formulaciones de Optimización Matemática y algoritmos de resolución numérica para construir modelos basados en árboles de clasificación y regresión. A diferencia de los árboles de clasificación y regresión clásicos, generados de manera heurística y voraz, construir un árbol a través de un problema de optimización nos permite incluir fácilmente propiedades estructurales globales deseables. En esta tesis, ilustramos esta flexibilidad para modelar los siguientes aspectos: sparsity, como sinónimo de interpretabilidad, controlando el número de coeficientes no nulos, el número de variables predictoras y, si son funcionales, la proporción de dominio usado en la predicción; un criterio social importante, la equidad del modelo, evitando predicciones que discriminen a algunos individuos por su etnia u otras características sensibles; y la sensibilidad al coste de grupos de riesgo, asegurando un rendimiento aceptable para ellos. Además, con este enfoque se obtiene de manera natural el impacto que las variables predictoras continuas tienen en la predicción de cada individuo, mejorando así la explicabilidad local de los modelos de clasificación y regresión basados en árboles. Todos los enfoques propuestos en esta tesis se formulan a través de problemas de Optimización Continua que son escalables con respecto al tamaño de la muestra de entrenamiento, se estudian desde el punto de vista teórico, se evalúan en conjuntos de datos reales y son competitivos frente a los procedimientos habituales. Esto, junto a las buenas propiedades resumidas en el párrafo anterior, se ilustra a lo largo de los diferentes capítulos de esta tesis. La tesis se estructura de la siguiente manera. El estado del arte sobre árboles de decisión (óptimos) se discute ampliamente en el Capítulo 1, mientras que los cuatro capítulos siguientes exponen nuestra metodología. El Capítulo 2 introduce de forma detallada el marco general que hila los capítulos de esta tesis: un árbol aleatorizado con cortes oblicuos. En particular, presentamos nuestra propuesta para tratar problemas de clasificación, la cual construye la probabilidad de pertenencia a cada clase ajustada a cada individuo, a diferencia de las técnicas más populares existentes, en las que a todos los individuos en el mismo nodo hoja se les asigna la misma probabilidad. Se tratan con éxito preferencias en las tasas de clasificación en clases críticas mediante restricciones de sensibilidad al coste. El Capítulo 3 extiende la metodología de clasificación del Capítulo 2 para tratar adicionalmente sparsity. Esto se modela mediante regularizaciones con normas poliédricas que se añaden a la función objetivo. Se estudian propiedades teóricas del árbol más sparse, y se demuestra nuestra habilidad para sacrificar un poco de precisión en la clasificación por una ganancia en sparsity. En el Capítulo 4, los resultados obtenidos en los Capítulos 2 y 3 se adaptan para construir árboles sparse para regresión. Se exploran propiedades teóricas de las soluciones. Los experimentos numéricos demuestran la escalabilidad de nuestro enfoque con respecto al tamaño de la muestra de entrenamiento, y se ilustra cómo se generan las explicaciones locales en las variables predictoras continuas. Además, mostramos cómo esta metodología puede reducir la discriminación de grupos sensibles a través de las denominadas restricciones de justicia. El Capítulo 5 extiende la metodología de regresión del Capítulo 4 para considerar variables predictoras funcionales. De manera simultánea, la detección de un número reducido de intervalos que son críticos para la predicción es abordada. La sparsity en la proporción de dominio de las variables predictoras funcionales a usar se modela también a través de un término de regularización añadido a la función objetivo. De esta forma, se ilustra el equilibrio obtenido entre la precisión de predicción y la sparsity en este marco. Por último, el Capítulo 6 cierra la tesis con conclusiones generales y líneas futuras de investigación

Anticancer drug discovery using artificial intelligence: an application in pharmacological activity prediction

Hematological cancers are a heterogeneous family of diseases that can be divided into leukemias, lymphomas, and myelomas, often called “liquid tumors”. Since they cannot be surgically removable, chemotherapy represents the mainstay of their treatment. However, it still faces several challenges like drug resistance and low response rate, and the need for new anticancer agents is compelling. The drug discovery process is long-term, costly, and prone to high failure rates. With the rapid expansion of biological and chemical "big data", some computational techniques such as machine learning tools have been increasingly employed to speed up and economize the whole process. Machine learning algorithms can create complex models with the aim to determine the biological activity of compounds against several targets, based on their chemical properties. These models are defined as multi-target Quantitative Structure-Activity Relationship (mt-QSAR) and can be used to virtually screen small and large chemical libraries for the identification of new molecules with anticancer activity. The aim of my Ph.D. project was to employ machine learning techniques to build an mt-QSAR classification model for the prediction of cytotoxic drugs simultaneously active against 43 hematological cancer cell lines. For this purpose, first, I constructed a large and diversified dataset of molecules extracted from the ChEMBL database. Then, I compared the performance of different ML classification algorithms, until Random Forest was identified as the one returning the best predictions. Finally, I used different approaches to maximize the performance of the model, which achieved an accuracy of 88% by correctly classifying 93% of inactive molecules and 72% of active molecules in a validation set. This model was further applied to the virtual screening of a small dataset of molecules tested in our laboratory, where it showed 100% accuracy in correctly classifying all molecules. This result is confirmed by our previous in vitro experiments

Fault management in networks incorporating Superconducting Cables (SCs) using Artificial Intelligence (AI) techniques.

With the increasing penetration of renewable energy sources, the immense growth in energy demand and the ageing of existing system infrastructure, future power systems have started to face reliability and resiliency challenges. To mitigate these issues, the need for bulk power corridors which enable the effective sharing of the available power capacity, between countries and from remote renewable energy sources, is rendered imperative. In this context, the deployment of multi-layer Superconducting Cables (SCs) with High Temperature Superconducting (HTS) tapes have been considered as a promising solution towards the modernisation of power systems. As opposed to conventional copper cables, SCs are characterised by a plethora of technically-attractive features such as compact structure, higher current-carrying capability, lower losses, higher power transfer at lower operating voltages and over longer distances, and reduced environmental impact. The performance of SCs is mainly determined by the structure of the cable and the electro-magneto-thermal properties of the HTS tapes, accounting for the critical current, critical temperature and critical magnetic field. Particularly, during steady state conditions, HTS tapes operate in superconducting mode, providing tangible benefits to power system operation such as a current-flowing path with approximately zero resistance. However, under certain transient conditions (e.g., electric faults), when the fault current flowing through HTS tapes reaches values higher than the critical current, HTS tapes start to quench. The quenching phenomenon is accompanied by a rapid increase in the equivalent resistance and temperature of SCs, the generation of Joule heating and the subsequent reduction in fault current magnitudes. Consequently, the transition of SCs from superconducting state to resistive state, during transient conditions, introduces many variables in the fault management of such cable technologies. Therefore, in order to exploit the technological advantages offered by SC applications, accommodate their wide-scale deployment within future energy grids, and accelerate their commercialisation, the detailed evaluation of their transient response and the consequent development of reliable fault management solutions are vital prerequisites. On that front, one of the main objectives of this thesis is to provide a detailed fault signature characterisation of AC and DC SCs and develop effective and practically feasible solutions for the fault management of AC and High Voltage Direct Current (HVDC) grids which incorporate SCs. As the fault management (i.e., fault detection, fault location, and protection) of SCs has proven to be a multi-variable problem, considering the complex structure, the unique features of SCs, and the quenching phenomenon, there is a need for advanced methods with immunity to these factors. In this context, the utilisation of Artificial Intelligence (AI) methods can be considered a very promising solution due to their capability to expose hidden patterns and acquire useful insights from the available data. Specifically, data-driven methods exhibit multifarious characteristics which allow them to provide innovative solutions for complex problems. Given their capacity for advanced learning and extensive data analysis, these methods merit thorough investigation for the fault management of SCs. Their inherent potential to adapt and uncover patterns in large datasets presents a compelling rationale for their exploration in enhancing the reliability and performance of superconducting cable systems. Therefore, this thesis proposes the development of novel, data-driven protection schemes which incorporate fault detection and classification elements for AC and multi-terminal HVDC systems with SCs, by exploiting the advantages of the latest trends in AI applications. In particular this thesis utilises cutting-edge developments and innovations in the field of AI, such as deep learning algorithms (i.e., CNN), and state-of-the-art techniques such as the XGBoost model which is a powerful ensemble learning algorithm. The developed schemes have been validated using simulation-based analysis. The obtained results confirm the enhanced sensitivity, speed, and discrimination capability of the developed schemes under various fault conditions and against other transient events, highlighting their superiority over other proposed methods or existing techniques. Furthermore, the generalisation capability of AI-assisted schemes has been verified against many adverse factors such as high values of fault resistance and noisy measurement. To further evaluate the practical feasibility and assess the time performance of the proposed schemes, real-time Software In the Loop (SIL) testing has been utilised. Another very important task for the effective fault management of AC and DC SCs is the estimation of the accurate fault location. Identifying the precise location of faults is crucial for SCs, given their complex structure and the challenging repair process. As such, this thesis proposes the design of a data-driven fault location scheme for AC systems with SCs. The developed scheme utilises pattern recognition techniques, such as image analysis, for feature extraction. It also incorporates AI algorithms in order to formulate the fault location problem as an AI regression problem. It is demonstrated that the scheme can accurately estimate the fault location along the SCs length and ensure increased reliability against a wide range of fault scenarios and noisy measurements. Further comparative analysis with other data-driven schemes validates the superiority of the proposed approach. In the final chapter the thesis summarises the key observations and outlines potential steps for further research in the field of fault management of superconducting-based systems.With the increasing penetration of renewable energy sources, the immense growth in energy demand and the ageing of existing system infrastructure, future power systems have started to face reliability and resiliency challenges. To mitigate these issues, the need for bulk power corridors which enable the effective sharing of the available power capacity, between countries and from remote renewable energy sources, is rendered imperative. In this context, the deployment of multi-layer Superconducting Cables (SCs) with High Temperature Superconducting (HTS) tapes have been considered as a promising solution towards the modernisation of power systems. As opposed to conventional copper cables, SCs are characterised by a plethora of technically-attractive features such as compact structure, higher current-carrying capability, lower losses, higher power transfer at lower operating voltages and over longer distances, and reduced environmental impact. The performance of SCs is mainly determined by the structure of the cable and the electro-magneto-thermal properties of the HTS tapes, accounting for the critical current, critical temperature and critical magnetic field. Particularly, during steady state conditions, HTS tapes operate in superconducting mode, providing tangible benefits to power system operation such as a current-flowing path with approximately zero resistance. However, under certain transient conditions (e.g., electric faults), when the fault current flowing through HTS tapes reaches values higher than the critical current, HTS tapes start to quench. The quenching phenomenon is accompanied by a rapid increase in the equivalent resistance and temperature of SCs, the generation of Joule heating and the subsequent reduction in fault current magnitudes. Consequently, the transition of SCs from superconducting state to resistive state, during transient conditions, introduces many variables in the fault management of such cable technologies. Therefore, in order to exploit the technological advantages offered by SC applications, accommodate their wide-scale deployment within future energy grids, and accelerate their commercialisation, the detailed evaluation of their transient response and the consequent development of reliable fault management solutions are vital prerequisites. On that front, one of the main objectives of this thesis is to provide a detailed fault signature characterisation of AC and DC SCs and develop effective and practically feasible solutions for the fault management of AC and High Voltage Direct Current (HVDC) grids which incorporate SCs. As the fault management (i.e., fault detection, fault location, and protection) of SCs has proven to be a multi-variable problem, considering the complex structure, the unique features of SCs, and the quenching phenomenon, there is a need for advanced methods with immunity to these factors. In this context, the utilisation of Artificial Intelligence (AI) methods can be considered a very promising solution due to their capability to expose hidden patterns and acquire useful insights from the available data. Specifically, data-driven methods exhibit multifarious characteristics which allow them to provide innovative solutions for complex problems. Given their capacity for advanced learning and extensive data analysis, these methods merit thorough investigation for the fault management of SCs. Their inherent potential to adapt and uncover patterns in large datasets presents a compelling rationale for their exploration in enhancing the reliability and performance of superconducting cable systems. Therefore, this thesis proposes the development of novel, data-driven protection schemes which incorporate fault detection and classification elements for AC and multi-terminal HVDC systems with SCs, by exploiting the advantages of the latest trends in AI applications. In particular this thesis utilises cutting-edge developments and innovations in the field of AI, such as deep learning algorithms (i.e., CNN), and state-of-the-art techniques such as the XGBoost model which is a powerful ensemble learning algorithm. The developed schemes have been validated using simulation-based analysis. The obtained results confirm the enhanced sensitivity, speed, and discrimination capability of the developed schemes under various fault conditions and against other transient events, highlighting their superiority over other proposed methods or existing techniques. Furthermore, the generalisation capability of AI-assisted schemes has been verified against many adverse factors such as high values of fault resistance and noisy measurement. To further evaluate the practical feasibility and assess the time performance of the proposed schemes, real-time Software In the Loop (SIL) testing has been utilised. Another very important task for the effective fault management of AC and DC SCs is the estimation of the accurate fault location. Identifying the precise location of faults is crucial for SCs, given their complex structure and the challenging repair process. As such, this thesis proposes the design of a data-driven fault location scheme for AC systems with SCs. The developed scheme utilises pattern recognition techniques, such as image analysis, for feature extraction. It also incorporates AI algorithms in order to formulate the fault location problem as an AI regression problem. It is demonstrated that the scheme can accurately estimate the fault location along the SCs length and ensure increased reliability against a wide range of fault scenarios and noisy measurements. Further comparative analysis with other data-driven schemes validates the superiority of the proposed approach. In the final chapter the thesis summarises the key observations and outlines potential steps for further research in the field of fault management of superconducting-based systems

Deep learning of brain asymmetry digital biomarkers to support early diagnosis of cognitive decline and dementia

Early identification of degenerative processes in the human brain is essential for proper care and treatment. This may involve different instrumental diagnostic methods, including the most popular computer tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET) scans. These technologies provide detailed information about the shape, size, and function of the human brain. Structural and functional cerebral changes can be detected by computational algorithms and used to diagnose dementia and its stages (amnestic early mild cognitive impairment - EMCI, Alzheimer’s Disease - AD). They can help monitor the progress of the disease. Transformation shifts in the degree of asymmetry between the left and right hemispheres illustrate the initialization or development of a pathological process in the brain. In this vein, this study proposes a new digital biomarker for the diagnosis of early dementia based on the detection of image asymmetries and crosssectional comparison of NC (normal cognitively), EMCI and AD subjects. Features of brain asymmetries extracted from MRI of the ADNI and OASIS databases are used to analyze structural brain changes and machine learning classification of the pathology. The experimental part of the study includes results of supervised machine learning algorithms and transfer learning architectures of convolutional neural networks for distinguishing between cognitively normal subjects and patients with early or progressive dementia. The proposed pipeline offers a low-cost imaging biomarker for the classification of dementia. It can be potentially helpful to other brain degenerative disorders accompanied by changes in brain asymmetries