Automatic text summarization using pathfinder network scaling

Contém uma errataTese de Mestrado. Inteligência Artificial e Sistemas Inteligentes. Faculdade de Engenharia. Universidade do Porto, Faculdade de Economia. Universidade do Porto. 200

Self-Sustained Debacle Repression Using Zig-Bee Communication

If an accident happens, nature of the humans is to criticize somebody else instead understanding the mistake. Finally, European study says that in 80% of vehicle accidents especially motor vehicles, drivers criticize the other person instead of preventing the accident. In our assessment, we consider conditions of traffic in India. For any accident to happen, major cause is speed of the driver. So, we should go for new methods to eradicate such type of problems. Assessment of accidents are done to identify the reason or series of accidents that are occurred and to avoid fresh accidents of the same type. So, we are going for Self-sustained Debacle Repression method identified by the combination of different independent solutions. An SDR development shape tomorrow’s safe. It also comes with collision avoidance Mechanisms such as: Avoiding the collision using Ultrasonic Sensor and ARM processor and Collision Indication (car-to-Xs Communication) using ZIGBEE technology

Sequenzvergleich mit Hilfe der Genomsignatur für die taxonomische Einordnung von Sequenzen und das Lernen taxonomischer Bäume

In this work we consider the use of the genome signature for two important bioinformatics problems; the taxonomic assignment of metagenome sequences and tree inference from whole genomes. We look at those problems from a sequence comparison point of view and propose machine learning based methods as solutions. For the first problem, we propose a novel method based on structural support vector machines that can directly predict paths in a tree implied by evolutionary relationships between taxa. The method is based on an ensemble strategy to predict highly specific assignments for varying length sequences arising from metagenome projects. Through controlled experimental analyses on simulated and real data sets we show the benefits of our method under realistic conditions. For the task of genome tree inference we propose a metric learning method. Based on the assumption that for different groups of prokaryotes, as defined by their phylogeny, genomic or ecological properties, different oligonucleotide weights can be more informative, our method learns group-specific distance metrics. We show that, indeed, it is possible to learn specific distance metrics that provide improved genome trees for the groups. In the outlook, we expect that for the addressed problems the work of this thesis will complement and in some cases even outperform alignment-based sequence comparison at a considerably reduced computational cost, allowing it to keep up with advancements in sequencing technologies.In dieser Arbeit wird die Verwendung der Genomsignatur für zwei wichtige bioinformatische Probleme untersucht. Diese sind zum einen die taxonomische Einordnung von Sequenzen aus Metagenomexperimenten und zum anderen das Lernen eines taxonomischen Baums aus verschiedenen ganzen Genomen. Diese beiden Probleme werden aus dem Blickwinkel der Sequenzanalyse betrachtet und Verfahren des maschinellen Lernens werden als Lösungsansätze vorgeschlagen. Für die Lösung des ersten Problems schlagen wir eine neue Methode vor, die auf strukturellen Support Vektor Maschinen beruht und direkt Pfade in einem Baum vorhersagen kann, der auf den evolutionären Ähnlichkeiten der Taxa beruht. Die Methode basiert auf einer Ensemble Strategie, um sehr genaue Zuweisungen für Sequenzen verschiedener Länge, die in Metagenomprojekten gemessen wurden, vorherzusagen. Wir zeigen die Vorteile unserer Methode auf simulierten sowie auf experimentellen Daten. Für das zweite Problem, bei dem ein taxonomischer Baum, basierend auf der genetischen Sequenz gelernt werden soll, schlagen wir eine Methode vor, die eine Metrik lernt. Die Annahme, auf der diese Methode beruht, ist, dass für verschiedene Gruppen von Prokaryoten unterschiedliche Gewichtungen der Oligonukleotidvorkommen notwendig sind, weswegen eine gruppenspezifische Metrik gelernt wird. Die Gruppen können dabei aufgrund ihrer phylogenetischen Beziehungen oder ökologischer sowie genomischer Merkmale bestimmt sein. Wir zeigen in unserer Analyse, dass es hierdurch möglich ist, spezifische Metriken zu lernen, die zu besseren Bäumen für diese Gruppen führen. Wir erwarten, dass unsere hier vorgestellten Arbeiten für die bearbeiteten Probleme Alignment-basierte Ansätze ergänzen und teilweise sogar überbieten können, wobei unsere Lösungen deutlich weniger Rechenzeit benötigen und damit mit dem rasanten Wachstum im Sequenzierbereich schritthalten können

Genome signature based sequence comparison for taxonomic assignment and tree inference

In this work we consider the use of the genome signature for two important bioinformatics problems; the taxonomic assignment of metagenome sequences and tree inference from whole genomes. We look at those problems from a sequence comparison point of view and propose machine learning based methods as solutions. For the first problem, we propose a novel method based on structural support vector machines that can directly predict paths in a tree implied by evolutionary relationships between taxa. The method is based on an ensemble strategy to predict highly specific assignments for varying length sequences arising from metagenome projects. Through controlled experimental analyses on simulated and real data sets we show the benefits of our method under realistic conditions. For the task of genome tree inference we propose a metric learning method. Based on the assumption that for different groups of prokaryotes, as defined by their phylogeny, genomic or ecological properties, different oligonucleotide weights can be more informative, our method learns group-specific distance metrics. We show that, indeed, it is possible to learn specific distance metrics that provide improved genome trees for the groups. In the outlook, we expect that for the addressed problems the work of this thesis will complement and in some cases even outperform alignment-based sequence comparison at a considerably reduced computational cost, allowing it to keep up with advancements in sequencing technologies.In dieser Arbeit wird die Verwendung der Genomsignatur für zwei wichtige bioinformatische Probleme untersucht. Diese sind zum einen die taxonomische Einordnung von Sequenzen aus Metagenomexperimenten und zum anderen das Lernen eines taxonomischen Baums aus verschiedenen ganzen Genomen. Diese beiden Probleme werden aus dem Blickwinkel der Sequenzanalyse betrachtet und Verfahren des maschinellen Lernens werden als Lösungsansätze vorgeschlagen. Für die Lösung des ersten Problems schlagen wir eine neue Methode vor, die auf strukturellen Support Vektor Maschinen beruht und direkt Pfade in einem Baum vorhersagen kann, der auf den evolutionären Ähnlichkeiten der Taxa beruht. Die Methode basiert auf einer Ensemble Strategie, um sehr genaue Zuweisungen für Sequenzen verschiedener Länge, die in Metagenomprojekten gemessen wurden, vorherzusagen. Wir zeigen die Vorteile unserer Methode auf simulierten sowie auf experimentellen Daten. Für das zweite Problem, bei dem ein taxonomischer Baum, basierend auf der genetischen Sequenz gelernt werden soll, schlagen wir eine Methode vor, die eine Metrik lernt. Die Annahme, auf der diese Methode beruht, ist, dass für verschiedene Gruppen von Prokaryoten unterschiedliche Gewichtungen der Oligonukleotidvorkommen notwendig sind, weswegen eine gruppenspezifische Metrik gelernt wird. Die Gruppen können dabei aufgrund ihrer phylogenetischen Beziehungen oder ökologischer sowie genomischer Merkmale bestimmt sein. Wir zeigen in unserer Analyse, dass es hierdurch möglich ist, spezifische Metriken zu lernen, die zu besseren Bäumen für diese Gruppen führen. Wir erwarten, dass unsere hier vorgestellten Arbeiten für die bearbeiteten Probleme Alignment-basierte Ansätze ergänzen und teilweise sogar überbieten können, wobei unsere Lösungen deutlich weniger Rechenzeit benötigen und damit mit dem rasanten Wachstum im Sequenzierbereich schritthalten können

A simple plug-in bagging ensemble based on threshold-moving for classifying binary and multiclass imbalanced data

Class imbalance presents a major hurdle in the application of classification methods. A commonly taken approach is to learn ensembles of classifiers using rebalanced data. Examples include bootstrap averaging (bagging) combined with either undersampling or oversampling of the minority class examples. However, rebalancing methods entail asymmetric changes to the examples of different classes, which in turn can introduce their own biases. Furthermore, these methods often require specifying the performance measure of interest a priori, i.e., before learning. An alternative is to employ the threshold moving technique, which applies a threshold to the continuous output of a model, offering the possibility to adapt to a performance measure a posteriori, i.e., a plug-in method. Surprisingly, little attention has been paid to this combination of a bagging ensemble and threshold-moving. In this paper, we study this combination and demonstrate its competitiveness. Contrary to the other resampling methods, we preserve the natural class distribution of the data resulting in well-calibrated posterior probabilities. Additionally, we extend the proposed method to handle multiclass data. We validated our method on binary and multiclass benchmark data sets by using both, decision trees and neural networks as base classifiers. We perform analyses that provide insights into the proposed method. Keywords: Imbalanced data; Binary classification; Multiclass classification; Bagging ensembles; Resampling; Posterior calibrationBurroughs Wellcome Fund (Grant 103811AI

Verifiability as a Complement to AI Explainability: A Conceptual Proposal

Recent advances in the field of artificial intelligence (AI) are providing automated and in many cases improved decision-making. However, even very reliable AI systems can go terribly wrong without human users understanding the reason for it. Against this background, there are now widespread calls for models of “explainable AI”. In this paper we point out some inherent problems of this concept and argue that explainability alone is probably not the solution. We therefore propose another approach as a complement, which we call “verifiability”. In essence, it is about designing AI so that it makes available multiple verifiable predictions (given a ground truth) in addition to the one desired prediction that cannot be verified because the ground truth is missing. Such verifiable AI could help to further minimize serious mistakes despite a lack of explainability, help increase their trustworthiness and in turn improve societal acceptance of AI

The PhyloPythiaS Web Server for Taxonomic Assignment of Metagenome Sequences

Metagenome sequencing is becoming common and there is an increasing need for easily accessible tools for data analysis. An essential step is the taxonomic classification of sequence fragments. We describe a web server for the taxonomic assignment of metagenome sequences with PhyloPythiaS. PhyloPythiaS is a fast and accurate sequence composition-based classifier that utilizes the hierarchical relationships between clades. Taxonomic assignments with the web server can be made with a generic model, or with sample-specific models that users can specify and create. Several interactive visualization modes and multiple download formats allow quick and convenient analysis and downstream processing of taxonomic assignments. Here, we demonstrate usage of our web server by taxonomic assignment of metagenome samples from an acidophilic biofilm community of an acid mine and of a microbial community from cow rumen

Neurobiological Divergence of the Positive and Negative Schizophrenia Subtypes Identified on a New Factor Structure of Psychopathology Using Non-negative Factorization:An International Machine Learning Study

ObjectiveDisentangling psychopathological heterogeneity in schizophrenia is challenging and previous results remain inconclusive. We employed advanced machine-learning to identify a stable and generalizable factorization of the “Positive and Negative Syndrome Scale (PANSS)”, and used it to identify psychopathological subtypes as well as their neurobiological differentiations.MethodsPANSS data from the Pharmacotherapy Monitoring and Outcome Survey cohort (1545 patients, 586 followed up after 1.35±0.70 years) were used for learning the factor-structure by an orthonormal projective non-negative factorization. An international sample, pooled from nine medical centers across Europe, USA, and Asia (490 patients), was used for validation. Patients were clustered into psychopathological subtypes based on the identified factor-structure, and the neurobiological divergence between the subtypes was assessed by classification analysis on functional MRI connectivity patterns.ResultsA four-factor structure representing negative, positive, affective, and cognitive symptoms was identified as the most stable and generalizable representation of psychopathology. It showed higher internal consistency than the original PANSS subscales and previously proposed factor-models. Based on this representation, the positive-negative dichotomy was confirmed as the (only) robust psychopathological subtypes, and these subtypes were longitudinally stable in about 80% of the repeatedly assessed patients. Finally, the individual subtype could be predicted with good accuracy from functional connectivity profiles of the ventro-medial frontal cortex, temporoparietal junction, and precuneus.ConclusionsMachine-learning applied to multi-site data with cross-validation yielded a factorization generalizable across populations and medical systems. Together with subtyping and the demonstrated ability to predict subtype membership from neuroimaging data, this work further disentangles the heterogeneity in schizophrenia