Ontology Based Strategies for Supporting Communication within Social Networks

In this paper, ontology based dialogue strategies are presented in connection with the concept of communicative images. Communicative images are graphical objects integrated with a dialogue interface and linked to an associated knowledge database which stores the semantics of the objects depicted. The relevant pieces of information can be linked to the external knowledge distributed in a social network. Exploiting a formal ontology approach facilitates the process of deriving information from relevant texts that can be found in the social network and it simultaneously forms a suitable framework for supporting dialogue communication in natural language. This approach is discussed and illustrated with various examples in this paper

A novel method for early diagnosis of Alzheimer's disease based on pseudo Zernike moment from structural MRI

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common type of dementia among older people. The number of patients with AD will grow rapidly each year and AD is the fifth leading cause of death for those aged 65 and older. In recent years, one of the main challenges for medical investigators has been the early diagnosis of patients with AD because an early diagnosis can provide greater opportunities for patients to be eligible for more clinical trials and they will have enough time to plan for future, medical and financial decisions. An established risk factor for AD is mild cognitive impairment (MCI) which is described as a transitional state between normal aging and AD patients. Hence an accurate and reliable diagnosis of MCI can be very effective and helpful for early diagnosis of AD. Therefore in this paper we present a novel and efficient method based on pseudo Zernike moments (PZMs) for the diagnosis of MCI individuals from AD and healthy control (HC) groups using structural MRI. The proposed method uses PZMs to extract discriminative information from the MR images of the AD, MCI, and HC groups. Two types of artificial neural networks, which are based on pattern recognition and learning vector quantization (LVQ) networks, were used to classify the information extracted from the MRIs. We worked with 500 MRIs from the database of the Alzheimer's Disease Neuroimaging Initiative (ADNI 1 1.5T). The 1 slice of 500 MRIs used in this study included 180 AD patients, 172 MCI patients, and 148 HC individuals. We selected 50 percent of the MRIs randomly for use in training the classifiers, 25 percent for validation and we used 25 percent for the testing phase. The technique proposed here yielded the best overall classification results between AD and MCI (accuracy 94.88%, sensitivity 94.18%, and specificity 95.55%), and for pairs of the MCI and HC (accuracy 95.59%, sensitivity 95.89% and specificity 95.34%). These results were achieved using maximum order 30 of PZM and the pattern recognition network with the scaled conjugate gradient (SCG) back-propagation training algorithm as a classifier

Optimized Seizure Detection Algorithm: A Fast Approach for Onset of Epileptic in EEG Signals Using GT Discriminant Analysis and K-NN Classifier

Background: Epilepsy is a severe disorder of the central nervous system that predisposes the person to recurrent seizures. Fifty million people worldwide suffer from epilepsy; after Alzheimer’s and stroke, it is the third widespread nervous disorder. Objective: In this paper, an algorithm to detect the onset of epileptic seizures based on the analysis of brain electrical signals (EEG) has been proposed. 844 hours of EEG were recorded form 23 pediatric patients consecutively with 163 occurrences of seizures. Signals had been collected from Children’s Hospital Boston with a sampling frequency of 256 Hz through 18 channels in order to assess epilepsy surgery. By selecting effective features from seizure and non-seizure signals of each individual and putting them into two categories, the proposed algorithm detects the onset of seizures quickly and with high sensitivity. Method: In this algorithm, L-sec epochs of signals are displayed in form of a thirdorder tensor in spatial, spectral and temporal spaces by applying wavelet transform. Then, after applying general tensor discriminant analysis (GTDA) on tensors and calculating mapping matrix, feature vectors are extracted. GTDA increases the sensitivity of the algorithm by storing data without deleting them. Finally, K-Nearest neighbors (KNN) is used to classify the selected features. Results: The results of simulating algorithm on algorithm standard dataset shows that the algorithm is capable of detecting 98 percent of seizures with an average delay of 4.7 seconds and the average error rate detection of three errors in 24 hours. Conclusion: Today, the lack of an automated system to detect or predict the seizure onset is strongly felt

Histological image analysis by invariant descriptors

In this work we propose a comparative study between different descriptors in analysing histological images. In particular, our study is focused on measuring the accuracy of moments (Hu, Legendre, Zernike), Local Binary Patterns and co-occurrence matrices in classifying histological images. The experimentation has been conducted on well known public datasets: HistologyDS, Pap-smear, Lymphoma, Liver Aging Female, Liver Aging Male, Liver Gender AL and Liver Gender CR. The comparison results show that when combined with co-occurrence matrices and extracted from the RGB images, the orthogonal moments improve the classification performance considerably, imposing themselves as very powerful descriptors for histological image analysis