The Implicit Function as Squashing Time Model: A Novel Parallel Nonlinear EEG Analysis Technique Distinguishing Mild Cognitive Impairment and Alzheimer's Disease Subjects with High Degree of Accuracy

Objective. This paper presents the results obtained using a protocol based on special types of artificial neural networks (ANNs) assembled in a novel methodology able to compress the temporal sequence of electroencephalographic (EEG) data into spatial invariants for the automatic classification of mild cognitive impairment (MCI) and Alzheimer's disease (AD) subjects. With reference to the procedure reported in our previous study (2007), this protocol includes a new type of artificial organism, named TWIST. The working hypothesis was that compared to the results presented by the workgroup (2007); the new artificial organism TWIST could produce a better classification between AD and MCI. Material and methods. Resting eyes-closed EEG data were recorded in 180 AD patients and in 115 MCI subjects. The data inputs for the classification, instead of being the EEG data, were the weights of the connections within a nonlinear autoassociative ANN trained to generate the recorded data. The most relevant features were selected and coincidently the datasets were split in the two halves for the final binary classification (training and testing) performed by a supervised ANN. Results. The best results distinguishing between AD and MCI were equal to 94.10% and they are considerable better than the ones reported in our previous study (∼92%) (2007). Conclusion. The results confirm the working hypothesis that a correct automatic classification of MCI and AD subjects can be obtained by extracting spatial information content of the resting EEG voltage by ANNs and represent the basis for research aimed at integrating spatial and temporal information content of the EEG

MicroRNA expression is associated with auditory dysfunction in workers exposed to ototoxic solvents and noise

This study is part of a project on early hearing dysfunction induced by combined exposure to volatile organic compounds (VOCs) and noise in occupational settings. In a previous study, 56 microRNAs were found differentially expressed in exposed workers compared to controls. Here, we analyze the statistical association of microRNA expression with audiometric hearing level (HL) and distortion product otoacoustic emission (DPOAE) level in that subset of differentially expressed microRNAs. The highest negative correlations were found; for HL, with miR-195-5p and miR-122-5p, and, for DPOAEs, with miR-92b-5p and miR-206. The homozygous (mut) and heterozygous (het) variants of the gene hOGG1 were found disadvantaged with respect to the wild-type (wt), as regards the risk of hearing impairment due to exposure to VOCs. An unsupervised artificial neural network (auto contractive map) was also used to detect and show, using graph analysis, the hidden connections between the explored variables. These findings may contribute to the formulation of mechanistic hypotheses about hearing damage due to co-exposure to noise and ototoxic solvents

Shoreline extraction based on an active connection matrix (ACM) image enhancement strategy

Coastal environments are facing constant changes over time due to their dynamic nature and geological, geomorphological, hydrodynamic, biological, climatic and anthropogenic factors. For these reasons, the monitoring of these areas is crucial for the safeguarding of the cultural heritage and the populations living there. The focus of this paper is shoreline extraction by means of an experimental algorithm, called J-Net Dynamic (Semeion Research Center of Sciences of Communication, Rome, Italy). It was tested on two types of image: a very high resolution (VHR) multispectral image (WorldView-2) and a high resolution (HR) radar synthetic aperture radar (SAR) image (Sentinel-1). The extracted shorelines were compared with those manually digitized for both images independently. The results obtained with the J-Net Dynamic algorithm were also compared with common algorithms, widely used in the literature, including theWorldView water index and the Canny edge detector. The results show that the experimental algorithm is more effective than the others, as it improves shoreline extraction accuracy both in the optical and SAR images

Gli approcci agli studi di settore: dal modello struttura-comportamento-risultati alle reti neurali

Industry Analysis is one of the major topics of Industrial Organization. This article is a survey aiming to collect different historical approaches in studying industries. We start from the first well organized approach of Structure-Conduct-Performance in which a causality among these three elements was supposed, and regression analysis was the statistical instrument to study it. Later, in the ’80 the New Industrial Economics speculated that the causality might be also in the opposite way: from Performance to Conducts to Struc- tures. At the same time, some scholars were also interested in classifying clusters (or types) of structures and of conducts. Furthermore, they tried to find if some association among these clusters could exist. Multivariate Data Analysis was the statistical instrument used to find these groups and to measure the relations among groups. Summarizing, causality and association (among variables) were the two main objectives of all these studies. Nowadays, Artificial Intelligence might be a new statistical instrument to observe and study at the same time the problem of causality and the problem of association. After an example of the use of Artificial Neural Networks, we will try to outline the advantages and disadvantages of this new technique that seems anyway very promising

Encoding and Simulating the Past. A Machine Learning Approach to the Archaeological Information

The encoding of the spatial-temporal archeological, historical and anthropological records can be considered an ideal-typical representation of the human reasoning and thus also an artificial membrane interposed between the researcher and the past. These membranes are here considered artificial networks and can undergo interrogation processes through the most advanced analytical tools for learning and modeling complex configurations. The aim of this paper is to synthesize recent advances in Artificial Intelligence and Computer Science and – at the same time – to support the connectionists and symbolic computational paradigms as a new epistemic frontier in the automatic annotation of tangible and intangible heritage as well in the contemporary theories and methods of the archeological thought

Affective symptoms and postural abnormalities as predictors of headache : an application of artificial neural networks

Chronic headache is a major liability in the individuals' quality of life. Identifying in advance the main features common to patients with headache may allow planning a preventive strategy of intervention. An artificial neural network model (Auto Contractive Maps - AutoCM), aimed at analyzing the correlations between patients' characteristics, affective symptoms and posture indicators has been developed in this paper. Patients suffering from chronic headache were observed at a neurological centre in Sicily (Italy). Headache and affective states were measured using the Profile of Mood States (POMS), the Beck Depression Inventory (BDI), the Toronto Alexithymia Scale (TAS-20) and the Repression Scale. Postural evaluations were carried through a stabilometric platform. The method of analysis selected allowed to reconstruct some records that were missing, through a Recirculation AutoAssociative Neural Network, and to obtain sound results. The results showed how some items from TAS-20, Repression and POMS were closely linked. The postural abnormalities were correlated primarily with repression features. The highest scores of the POMS were correlated with the items of the BDI. The results obtained lead to interesting remarks about the common traits to patients with headache. The main conclusion lies in the potentialities offered by the new methodology applied, that may contribute, overall, to a better understanding of the complexity of chronic diseases, where many factors concur to define patients' health conditions

A Pattern Recognition Analysis of Vessel Trajectories

The automatic identification system (AIS) facilitates the monitoring of ship movements and provides essential input parameters for traffic safety. Previous studies have employed AIS data to detect behavioral anomalies and classify vessel types using supervised and unsupervised algorithms, including deep learning techniques. The approach proposed in this work focuses on the recognition of vessel types through the “Take One Class at a Time” (TOCAT) classification strategy. This approach pivots on a collection of adaptive models rather than a single intricate algorithm. Using radar data, these models are trained by taking into account aspects such as identifiers, position, velocity, and heading. However, it purposefully excludes positional data to counteract the inconsistencies stemming from route variations and irregular sampling frequencies. Using the given data, we achieved a mean accuracy of 83% on a 6-class classification task