Novel neural approaches to data topology analysis and telemedicine

1noL'abstract è presente nell'allegato / the abstract is in the attachmentopen676. INGEGNERIA ELETTRICAnoopenRandazzo, Vincenz

Applications of multivariate statistics in honey bee research, analysis of metabolomics data from samples of honey bee propolis

This thesis was previously held under moratorium from 20/04/2020 to 20/04/2022Honey bees play a significant role both ecologically and economically, through the pollination of flowering plants and crops. Additionally, honey is an ancient food source that is highly valued by different religions and cultures and has been shown to possess a wide range of beneficial uses, including cosmetic treatment, eye disease, bronchial asthma and hiccups. In addition to honey, honey bees also produce beeswax, pollen, royal jelly and propolis. In this thesis, data is studied which comes from samples of propolis from various geographical locations. Propolis is a resinous product, which consists of a combination of beeswax, saliva and resins that have been gathered by honey bees from the exudates of various surrounding plants. It is used by the bees to seal small gaps and maintain the hives, but is also an anti-microbial substance that may protect them against disease. The appearance and consistency of propolis changes depending on the temperature; it becomes elastic and sticky when warm, but hard and brittle when cold. Furthermore, its composition and colour varies from yellowish-green to dark brown, depending on its age and the sources of resin from the environment. Propolis is a highly biochemically active substance with many potential benefits in health care, which have attracted much attention. Biochemical analysis of propolis leads to highly multivariate metabolomics data. The main benefit of metabolomics is to generate a spectrum, in which peaks correspond to different chemical components, making possible the detection of multiple substances simultaneously. Relevant spectral features may be used for pattern recognition. The purpose of this research is to study methods used for statistical analysis of biochemical data arising from propolis samples. We investigate the use of different statistical methods for metabolomics data from chemical analysis of propolis samples using Mass Spectrometry (MS). Methods studied will include pre-treatment methods and multivariate analysis techniques including principal component analysis (PCA), multidimensional scaling (MDS), and clustering methods including hierarchical cluster analysis (HCA), k-means clustering and self organising maps (SOMs). Background material and results of data analysis will be presented from samples of propolis from beehives in Scotland, Libya and Europe. Conclusions are drawn in terms of the data sets themselves as well as the properties of the different methods studied for analysing such metabolomics data.Honey bees play a significant role both ecologically and economically, through the pollination of flowering plants and crops. Additionally, honey is an ancient food source that is highly valued by different religions and cultures and has been shown to possess a wide range of beneficial uses, including cosmetic treatment, eye disease, bronchial asthma and hiccups. In addition to honey, honey bees also produce beeswax, pollen, royal jelly and propolis. In this thesis, data is studied which comes from samples of propolis from various geographical locations. Propolis is a resinous product, which consists of a combination of beeswax, saliva and resins that have been gathered by honey bees from the exudates of various surrounding plants. It is used by the bees to seal small gaps and maintain the hives, but is also an anti-microbial substance that may protect them against disease. The appearance and consistency of propolis changes depending on the temperature; it becomes elastic and sticky when warm, but hard and brittle when cold. Furthermore, its composition and colour varies from yellowish-green to dark brown, depending on its age and the sources of resin from the environment. Propolis is a highly biochemically active substance with many potential benefits in health care, which have attracted much attention. Biochemical analysis of propolis leads to highly multivariate metabolomics data. The main benefit of metabolomics is to generate a spectrum, in which peaks correspond to different chemical components, making possible the detection of multiple substances simultaneously. Relevant spectral features may be used for pattern recognition. The purpose of this research is to study methods used for statistical analysis of biochemical data arising from propolis samples. We investigate the use of different statistical methods for metabolomics data from chemical analysis of propolis samples using Mass Spectrometry (MS). Methods studied will include pre-treatment methods and multivariate analysis techniques including principal component analysis (PCA), multidimensional scaling (MDS), and clustering methods including hierarchical cluster analysis (HCA), k-means clustering and self organising maps (SOMs). Background material and results of data analysis will be presented from samples of propolis from beehives in Scotland, Libya and Europe. Conclusions are drawn in terms of the data sets themselves as well as the properties of the different methods studied for analysing such metabolomics data

Projection-Based Clustering through Self-Organization and Swarm Intelligence

It covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cluster analysis, the Databionic swarm (DBS). DBS consists of the 3D landscape visualization and clustering of data. The 3D landscape enables 3D printing of high-dimensional data structures. The clustering and number of clusters or an absence of cluster structure are verified by the 3D landscape at a glance. DBS is the first swarm-based technique that shows emergent properties while exploiting concepts of swarm intelligence, self-organization and the Nash equilibrium concept from game theory. It results in the elimination of a global objective function and the setting of parameters. By downloading the R package DBS can be applied to data drawn from diverse research fields and used even by non-professionals in the field of data mining

A Multi Agent System for Flow-Based Intrusion Detection

The detection and elimination of threats to cyber security is essential for system functionality, protection of valuable information, and preventing costly destruction of assets. This thesis presents a Mobile Multi-Agent Flow-Based IDS called MFIREv3 that provides network anomaly detection of intrusions and automated defense. This version of the MFIRE system includes the development and testing of a Multi-Objective Evolutionary Algorithm (MOEA) for feature selection that provides agents with the optimal set of features for classifying the state of the network. Feature selection provides separable data points for the selected attacks: Worm, Distributed Denial of Service, Man-in-the-Middle, Scan, and Trojan. This investigation develops three techniques of self-organization for multiple distributed agents in an intrusion detection system: Reputation, Stochastic, and Maximum Cover. These three movement models are tested for effectiveness in locating good agent vantage points within the network to classify the state of the network. MFIREv3 also introduces the design of defensive measures to limit the effects of network attacks. Defensive measures included in this research are rate-limiting and elimination of infected nodes. The results of this research provide an optimistic outlook for flow-based multi-agent systems for cyber security. The impact of this research illustrates how feature selection in cooperation with movement models for multi agent systems provides excellent attack detection and classification

Multi-objective Optimization of Industrial Ammonia Synthesis

The thesis describes modelling and optimization work of an industrial ammonia synthesis. Author developed first-principle mathematical model of the commercial converter based on gas-solid reaction and heat transfer within the system. The model is validated with industrial data and showed satisfactory accuracy. Further, optimization study is performed in multi-objective manner to intensify ammonia production and decrease heat duty of the process. Result have revealed a potential to improve current operating condition int terms of both objectives

Projection-Based Clustering through Self-Organization and Swarm Intelligence: Combining Cluster Analysis with the Visualization of High-Dimensional Data

Cluster Analysis; Dimensionality Reduction; Swarm Intelligence; Visualization; Unsupervised Machine Learning; Data Science; Knowledge Discovery; 3D Printing; Self-Organization; Emergence; Game Theory; Advanced Analytics; High-Dimensional Data; Multivariate Data; Analysis of Structured Dat

Protein-Ligand Binding Affinity Directed Multi-Objective Drug Design Based on Fragment Representation Methods

Drug discovery is a challenging process with a vast molecular space to be explored and numerous pharmacological properties to be appropriately considered. Among various drug design protocols, fragment-based drug design is an effective way of constraining the search space and better utilizing biologically active compounds. Motivated by fragment-based drug search for a given protein target and the emergence of artificial intelligence (AI) approaches in this field, this work advances the field of in silico drug design by (1) integrating a graph fragmentation-based deep generative model with a deep evolutionary learning process for large-scale multi-objective molecular optimization, and (2) applying protein-ligand binding affinity scores together with other desired physicochemical properties as objectives. Our experiments show that the proposed method can generate novel molecules with improved property values and binding affinities