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Ductal adenocarcinoma of the pancreas is a cancer with a high mortality rate. Among the main reasons for this baleful prognosis is that, in most patients, this neoplasm is diagnosed at a too advanced stage. Clinical oncology research is now particularly focused on decoding the cancer molecular onset by understanding the complex biological architecture of tumor cell proliferation. In this direction, machine learning has proved to be a valid solution in many sectors of the biomedical field, thanks to its ability to mine useful knowledge by biological and genetic data. Since the major risk factor is represented by genetic predisposition, the aim of this study is to find a mathematical model describing the complex relationship existing between genetic mutations of the involved genes and the onset of the disease. To this end, an approach based on evolutionary algorithms is proposed. In particular, genetic programming is used, which allows solving a symbolic regression problem through the use of genetic algorithms. The identification of these correlations is a typical objective of the diagnostic approach and is one of the most critical and complex activities in the presence of large amounts of data that are difficult to correlate through traditional statistical techniques. The mathematical model obtained highlights the importance of the complex relationship existing between the different gene's mutations present in the tumor tissue of the group of patients considered. [ABSTRACT FROM AUTHOR]

Resource Planning Optimization (RPO) is a common task that many companies need to face to get several benefits, like budget improvements and run-time analyses. However, even if it is often solved by using several software products and tools, the great success and validity of the Artificial Intelligence-based approaches, in many research fields, represent a huge opportunity to explore alternative solutions for solving optimization problems. To this purpose, the following paper aims to investigate the use of multiple Artificial Neural Networks (ANNs) for solving a RPO problem related to the scheduling of different Combined Heat & Power (CHP) generators. The experimental results, carried out by using data extracted by considering a real Microgrid system, have confirmed the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Bayesian networks in their Factor Graph Reduced Normal Form are a powerful paradigm for implementing inference graphs. Unfortunately, the computational and memory costs of these networks may be considerable even for relatively small networks, and this is one of the main reasons why these structures have often been underused in practice. In this work, through a detailed algorithmic and structural analysis, various solutions for cost reduction are proposed. Moreover, an online version of the classic batch learning algorithm is also analysed, showing very similar results in an unsupervised context but with much better performance; which may be essential if multi-level structures are to be built. The solutions proposed, together with the possible online learning algorithm, are included in a C++ library that is quite efficient, especially if compared to the direct use of the well-known sum-product and Maximum Likelihood algorithms. The results obtained are discussed with particular reference to a Latent Variable Model structure. [ABSTRACT FROM AUTHOR]

Non-predictive or inaccurate weather forecasting can severely impact the community of users such as farmers. Numerical weather prediction models run in major weather forecasting centers with several supercomputers to solve simultaneous complex nonlinear mathematical equations. Such models provide the medium-range weather forecasts, i.e., every 6 h up to 18 h with grid length of 10–20 km. However, farmers often depend on more detailed short-to medium-range forecasts with higher-resolution regional forecasting models. Therefore, this research aims to address this by developing and evaluating a lightweight and novel weather forecasting system, which consists of one or more local weather stations and state-of-the-art machine learning techniques for weather forecasting using time-series data from these weather stations. To this end, the system explores the state-of-the-art temporal convolutional network (TCN) and long short-term memory (LSTM) networks. Our experimental results show that the proposed model using TCN produces better forecasting compared to the LSTM and other classic machine learning approaches. The proposed model can be used as an efficient localized weather forecasting tool for the community of users, and it could be run on a stand-alone personal computer. [ABSTRACT FROM AUTHOR]

The maximum network lifetime problem is a well-known and challenging optimization problem which has been addressed successfully with several approaches in the last years. It essentially consists in finding an optimal schedule for sensors activities in a wireless sensor network (WSN) aiming at maximizing the total amount of time during which the WSN is able to perform its monitoring task. In this paper, we consider a new scenario in which, in order to monitor some locations in a geographical area, the sensors need to be active for a fixed amount of time, defined as operating time slot. For this new scenario, we derive an upper bound on the maximum lifetime and propose a genetic algorithm for finding a near-optimal node activity schedule. The performance evaluation results obtained on numerous benchmark instances show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Malignant pleural effusion is diagnostically challenging in presence of negative cytology. The assessment of tumor markers in serum has become a standard tool in cancer diagnosis, while pleural fluid sampling has not met universal consensus. The evaluation of a panel of markers both in serum and pleural fluid may be crucial to improve the diagnostic accuracy. Using a machine learning-based approach, we provide a mathematical formula capable to express the complex relation existing among the expressed markers in serum and pleural effusion and the presence of lung cancer. The formula indicates CEA and CYFRA21-1 in pleural fluid as the best diagnostic markers, with 97% accuracy, 98% sensitivity, 95% specificity, 96% area under curve, 98% positive predictive value, and 92% MCC (Matthews correlation coefficient). [ABSTRACT FROM AUTHOR]

Pervasive computing is one of the latest and more advanced paradigms currently available in the computers arena. Its ability to provide the distribution of computational services within environments where people live, work or socialize leads to make issues such as privacy, trust and identity more challenging compared to traditional computing environments. In this work, we review these general issues and propose a pervasive computing architecture based on a simple but effective trust model that is better able to cope with them. The proposed architecture combines some artificial intelligence techniques to achieve close resemblance with human-like decision making. Accordingly, Apriori algorithm is first used in order to extract the behavioral patterns adopted from the users during their network interactions. Naïve Bayes classifier is then used for final decision making expressed in term of probability of user trustworthiness. To validate our approach, we applied it to some typical ubiquitous computing scenarios. The obtained results demonstrated the usefulness of such approach and the competitiveness against other existing ones. [ABSTRACT FROM AUTHOR]