Unsupervised cyber bullying detection in social networks

Modern young people (“digital natives”) have grown in an era dominated by new technologies where communications are pushed to quite a real-time level, and pose no limits in establishing relationships with other people or communities. However, the speed of evolution does not allow young people to split consciously acceptable behaviors from potentially harmful ones and a new phenomenon known as cyber bullying is emerging with increasing evidence, attracting the attention of educators, and media. Cyber bullying is defined as “willful and repeated harm inflicted through the use of electronic devices” [1]. In this paper we propose a possible solution for automatic detection of bully traces over a social network, using techniques derived from NLP (Natural Language Processing) and machine learning. Specifically, we shall design a model inspired by Growing Hier- archical SOMs, able to cluster efficiently documents containing bully traces, built upon semantic and syntactic features of textual sentences. We fine-tuned our model to work with the social network Twitter, but we also tested the model against other social networks such as YouTube and Formspring. Finally, we report our results, showing that the proposed unsupervised approach could be effectively used with good performances in some scenarios

Video-based access control by automatic license plate recognition

We report an access control system based on automatic license plate recognition, consisting of three main modules for acquisition, extraction, and recognition. The basic idea is to couple the online learning of a neural background model with a stopped foreground subtraction mechanism to efficiently provide a subset of relevant video frames where to look for. Another key point is the use of matching the entire license plate ROI with those stored in a database of authorized license plates, based on suitable features and validation tests. Experimental results confirm that the proposed system attains overall performance comparable with that of the state-of-the-art ALPR methods

Parallel Implementation of a Machine Learning Algorithm on GPU

The capability for understanding data passes through the ability of producing an effective and fast classification of the information in a time frame that allows to keep and preserve the value of the information itself and its potential. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. A powerful tool is provided by self-organizing maps (SOM). The goal of learning in the self-organizing map is to cause different parts of the network to respond similarly to certain input patterns. Because of its time complexity, often using this method is a critical challenge. In this paper we propose a parallel implementation for the SOM algorithm, using parallel processor architecture, as modern graphics processing units by CUDA. Experimental results show improvements in terms of execution time, with a promising speed up, compared to the CPU version and the widely used package SOM_PAK

Competitive advantage in healthcare based on augmentation of clinical images with artificial intelligence: case study of the 'Sambias' project

In the era of artificial intelligence, and particularly machine learning and deep learning models, the availability of large datasets is crucial to develop innovative and effective services, especially in the healthcare field. In this context, one essential requirement is access to verified information for contextualising/enriching the data. The SAMBIAS project analysed in this study involves the implementation of a software platform for data sharing in clinical scenarios, with the main objective of providing specific medical datasets to improve the competitiveness of the healthcare organisation from a general point of view. The platform, which is accessible via the web, provides on-demand, augmented sets of clinical situations, based on the enormous amounts of data that are collected by the health information systems of healthcare organisations. The case under investigation here is the Casa di Cura Tortorella s.p.a., Salerno, Italy. The implications of this platform are discussed in terms of more efficient performance

A new biomarker panel of ultraconserved long non-coding RNAs for bladder cancer prognosis by a machine learning based methodology

Background: Recent studies have indicated that a special class of long non-coding RNAs (lncRNAs), namely Transcribed-Ultraconservative Regions are transcribed from specific DNA regions (T-UCRs), 100% conserved in human, mouse, and rat genomes. This is noticeable, as lncRNAs are usually poorly conserved. Despite their peculiarities, T-UCRs remain very understudied in many diseases, including cancer and, yet, it is known that dysregulation of T-UCRs is associated with cancer as well as with human neurological, cardiovascular, and developmental pathologies. We have recently reported the T-UCR uc.8+ as a potential prognostic biomarker in bladder cancer.Results: The aim of this work is to develop a methodology, based on machine learning techniques, for the selection of a predictive signature panel for bladder cancer onset. To this end, we analyzed the expression profiles of T-UCRs from surgically removed normal and bladder cancer tissues, by using custom expression microarray. Bladder tissue samples from 24 bladder cancer patients (12 Low Grade and 12 High Grade), with complete clinical data, and 17 control samples from normal bladder epithelium were analysed. After the selection of preferentially expressed and statistically significant T-UCRs, we adopted an ensemble of statistical and machine learning based approaches (i.e., logistic regression, Random Forest, XGBoost and LASSO) for ranking the most important diagnostic molecules. We identified a signature panel of 13 selected T-UCRs with altered expression profiles in cancer, able to efficiently discriminate between normal and bladder cancer patient samples. Also, using this signature panel, we classified bladder cancer patients in four groups, each characterized by a different survival extent. As expected, the group including only Low Grade bladder cancer patients had greater overall survival than patients with the majority of High Grade bladder cancer. However, a specific signature of deregulated T-UCRs identifies sub-types of bladder cancer patients with different prognosis regardless of the bladder cancer Grade.Conclusions: Here we present the results for the classification of bladder cancer (Low and High Grade) patient samples and normal bladder epithelium controls by using a machine learning application. The T-UCR's panel can be used for learning an eXplainable Artificial Intelligent model and develop a robust decision support system for bladder cancer early diagnosis providing urinary T-UCRs data of new patients. The use of this system instead of the current methodology will result in a non-invasive approach, reducing uncomfortable procedures (such as cystoscopy) for the patients. Overall, these results raise the possibility of new automatic systems, which could help the RNA-based prognosis and/or the cancer therapy in bladder cancer patients, and demonstrate the successful application of Artificial Intelligence to the definition of an independent prognostic biomarker panel