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Computational solution of capacity planning models under uncertainty

Abstract The traditional supply chain network planning problem is stated as a multi-period resource allocation model involving 0–1 discrete strategic decision variables. The MIP structure of this problem makes it fairly intractable for practical applications, which involve multiple products, factories, warehouses and distribution centres (DCs). The same problem formulated and studied under uncertainty makes it even more intractable. In this paper we consider two related modelling approaches and solution techniques addressing this issue. The first involves scenario analysis of solutions to “wait and see” models and the second involves a two-stage integer stochastic programming (ISP) representation and solution of the same problem. We show how the results from the former can be used in the solution of the latter model. We also give some computational results based on serial and parallel implementations of the algorithms.

Sentiment analysis

The huge amount of textual data on the Web has grown in the last few years rapidly creating unique contents of massive dimension. In a decision making context, one of the most relevant tasks is polarity classification of a text source, which is usually performed through supervised learning methods. Most of the existing approaches select the best classification model leading to over-confident decisions that do not take into account the inherent uncertainty of the natural language. In this paper, we pursue the paradigm of ensemble learning to reduce the noise sensitivity related to language ambiguity and therefore to provide a more accurate prediction of polarity. The proposed ensemble method is based on Bayesian Model Averaging, where both uncertainty and reliability of each single model are taken into account. We address the classifier selection problem by proposing a greedy approach that evaluates the contribution of each model with respect to the ensemble. Experimental results on gold standard datasets show that the proposed approach outperforms both traditional classification and ensemble methods. A novel ensemble learning methodology is proposed for polarity classification task.A selection strategy is studied to reduce the search space of candidate ensembles.The proposed model has been shown to be effective and efficient in several domains.

Genetic programming for computational pharmacokinetics in drug discovery and development

The success of a drug treatment is strongly correlated with the ability of a molecule to reach its target in the patient’s organism without inducing toxic effects. Moreover the reduction of cost and time associated with drug discovery and development is becoming a crucial requirement for pharmaceutical industry. Therefore computational methods allowing reliable predictions of newly synthesized compounds properties are of outmost relevance. In this paper we discuss the role of genetic programming in predictive pharmacokinetics, considering the estimation of adsorption, distribution, metabolism, excretion and toxicity processes (ADMET) that a drug undergoes into the patient’s organism. We compare genetic programming with other well known machine learning techniques according to their ability to predict oral bioavailability (%F), median oral lethal dose (LD50) and plasma-protein binding levels (%PPB). Since these parameters respectively characterize the percentage of initial drug dose that effectively reaches the systemic blood circulation, the harmful effects and the distribution into the organism of a drug, they are essential for the selection of potentially good molecules. Our results suggest that genetic programming is a valuable technique for predicting pharmacokinetics parameters, both from the point of view of the accuracy and of the generalization ability.

Genetic programming for human oral bioavailability of drugs

Automatically assessing the value of bioavailability from the chemical structure of a molecule is a very important issue in biomedicine and pharmacology. In this paper, we present an empirical study of some well known Machine Learning techniques, including various versions of Genetic Programming, which have been trained to this aim using a dataset of molecules with known bioavailability. Genetic Programming has proven the most promising technique among the ones that have been considered both from the point of view of the accurateness of the solutions proposed, of the generalization capabilities and of the correlation between predicted data and correct ones. Our work represents a first answer to the demand for quantitative bioavailability estimation methods proposed in literature, since the previous contributions focus on the classification of molecules into classes with similar bioavailability.

An integrated communications framework for context aware continuous monitoring with body sensor networks

This paper deals with a wireless pervasive communication system to support advanced healthcare applications. The proposed system is based on an ad hoc interaction of mobile body sensor networks with independent wireless sensor networks already deployed within the environments in order to allow a continuous and context aware health monitoring for patients along their daily life scenarios with an unprecedented precision and flexibility of sensing. After an accurate protocol characterization, simulation results are provided, underlining remarkable performance with respect to existing solutions, for different mobility models and node density values.

A hierarchical document clustering environment based on the induced bisecting k-means

The steady increase of information on WWW, digital library, portal, database and local intranet, gave rise to the development of several methods to help user in Information Retrieval, information organization and browsing. Clustering algorithms are of crucial importance when there are no labels associated to textual information or documents. The aim of clustering algorithms, in the text mining domain, is to group documents concerning with the same topic into the same cluster, producing a flat or hierarchical structure of clusters. In this paper we present a Knowledge Discovery System for document processing and clustering. The clustering algorithm implemented in this system, called Induced Bisecting k-Means, outperforms the Standard Bisecting k-Means and is particularly suitable for on line applications when computational efficiency is a crucial aspect.

Expressive signals in social media languages to improve polarity detection

To capture the sentiment of messages, several expressive forms are investigated.Expressive signals enrich the feature space of baseline and ensemble classifiers.Only adjectives play a fundamental role as expressive signal.Pragmatic particles and expressive lengthening could lead to the de finition of erratic polarity classifiers. Social media represents an emerging challenging sector where the natural language expressions of people can be easily reported through blogs and short text messages. This is rapidly creating unique contents of massive dimensions that need to be efficiently and effectively analyzed to create actionable knowledge for decision making processes. A key information that can be grasped from social environments relates to the polarity of text messages. To better capture the sentiment orientation of the messages, several valuable expressive forms could be taken into account. In this paper, three expressive signals - typically used in microblogs - have been explored: (1) adjectives, (2) emoticon, emphatic and onomatopoeic expressions and (3) expressive lengthening. Once a text message has been normalized to better conform social media posts to a canonical language, the considered expressive signals have been used to enrich the feature space and train several baseline and ensemble classifiers aimed at polarity classification. The experimental results show that adjectives are more discriminative and impacting than the other considered expressive signals.

Enhance User-Level Sentiment Analysis on Microblogs with Approval Relations

Sentiment Analysis for polarity classification on microblogs is generally based on the assumption that texts are independent and identically distributed (i.i.d). Although these methods are aimed at handling the complex characteristics of natural language, usually they do not consider microblogs as networked data. Early approaches for overcoming this limitation consist in exploiting friendship relationships, since connected users may be more likely to hold similar opinions (Homophily and Social Influence). However, the assumption about the friendship relations does not reflect the real world, where two connected users could have different opinions about the same topic. In order to overcome these shortcomings, we propose a semi-supervised framework that estimates user polarities about a given topic by combining post contents and weighted approval relations, which are intended to better represent the contagion on social networks. The experimental investigation reveals that incorporating approval relations can lead to statistically significant improvements over the performance of complex supervised classifiers based only on textual features.

A probabilistic relational approach for web document clustering

The exponential growth of information available on the World Wide Web, and retrievable by search engines, has implied the necessity to develop efficient and effective methods for organizing relevant contents. In this field document clustering plays an important role and remains an interesting and challenging problem in the field of web computing. In this paper we present a document clustering method, which takes into account both contents information and hyperlink structure of web page collection, where a document is viewed as a set of semantic units. We exploit this representation to determine the strength of a relation between two linked pages and to define a relational clustering algorithm based on a probabilistic graph representation. The experimental results show that the proposed approach, called RED-clustering, outperforms two of the most well known clustering algorithm as k-Means and Expectation Maximization.

Emotional states in judicial courtrooms: An experimental investigation

Thanks to the recent progress in the judicial proceedings management, especially related to the introduction of audio/video recording facilities, the challenge of identification of emotional states can be tackled. Discovering affective states embedded into speech signals could help in semantic retrieval of multimedia clips, and therefore in a deep understanding of mechanisms behind courtroom debates and judges/jurors decision making processes. In this paper two main contributions are given: (1) the collection of real-world human emotions coming from courtroom audio recordings; (2) the investigation of a hierarchical classification system, based on a risk minimization method, able to recognize emotional states from speech signatures. The accuracy of the proposed classification approach - named Multilayer Support Vector Machines - has been evaluated by comparing its performance with traditional machine learning approaches, by using both benchmark datasets and real courtroom recordings. Results in recognition obtained by the proposed technique outperform the prediction power achieved by traditional approaches like SVM, k-Nearest Neighbors, Naive Bayes, Decision Trees and Bayesian Networks.