Objectives: Diagnosis of the psychiatric diseases is a bit challenging at the first interview due to this fact that qualitative criteria are not as accurate as quantitative ones. Here, the objective is to classify schizophrenic patients from the healthy subject using a quantitative index elicited from their electroencephalogram (EEG) signals.Methods: Ten right handed male patients with schizophrenia who had just auditory hallucination and did not have any other psychotic features and ten age-matched right handed normal male control participants participated in this study. The patients used haloperidol to minimize the drug-related affection on their EEG signals. Electrophysiological data were recorded using a Neuroscan 24 Channel Synamps system, with a signal gain equal to 75K (150 xs at the headbox). According to the observable anatomical differences in the brain of schizophrenic patients from controls, several discriminative features including AR coefficients, band power, fractal dimension, and approximation ENTROPY (ApEn) were chosen to extract quantitative values from the EEG signals.Results: The extracted features were applied to support vector machine (SVM) classifier that produced 88.40% accuracy for distinguishing the two groups. Incidentally, ApEn produces more discriminative information compare to the other features.Conclusion: This research presents a reliable quantitative approach to distinguish the control subjects from the schizophrenic patients. Moreover, other representative features are implemented but ApEn produces higher performance due to complex and irregular nature of EEG signals.

Background: The highly increase of Alzheimer's disease among human lead to increasing the demand of finding a reliable way to diagnose its symptoms at the early stages. Recent researches in this area demonstrate that the signal complexity analysis of the electroencephalogram can be useful in prognosis the development of this illness form mild cognitive impairment to Alzheimer's disease. The focus of this study was on APPROXIMATE ENTROPY and proposing an effective approach for using this criterion to diagnose the mild cognitive impairment.Methods: In this research, the electroencephalograms of 16 normal subjects and 11 patients were used. The signals were captured based on 10-20 international system for 30 minutes. In the preprocessing phase, the artefacts were eliminated both by visually inspection by a specialist physician and using band pass filter. In the processing phase, different scenarios were considered and applied to define the different parameters of APPROXIMATE ENTROPY. Finally, the results were analyzed using t-test to optimize the define protocol of the ENTROPY and find the appropriate channels for diagnosing the disease.Findings: A protocol for extracting the complexity based on APPROXIMATE ENTROPY was determined, in which the difference of the ENTROPY of normal subjects and patients were more remarkable. By using this protocol, the number of appropriate channels for diagnosing the disease increased (P<0.05). These results also showed decreasing the gray matter volume in the patients with mild cognitive impairment.Conclusion: Using the ENTROPY measurements for different channels of patients with mild cognitive impairment, demonstrate that the amount of complexity of signals decreased.

The traditional method for studying non-stationary signals is spectrogram based on the short-time Fourier transform (STFT). The well known limitation of the STFT is the inherent trade-off between time and frequency resolution. The Wigner-Ville (WV) distribution has the best time-frequency resolution, but its draw back is generating cross-terms. The matching pursuit (MP) distribution based on using the Gaussian atom is always positive, does not include crossterm, and has convenient resolution. In this paper, we have shown in addition to the known properties, the MP distribution can also remove the additive noise inherently. On the other words, we are able to remove the noise just by limiting the algorithm iterations and without paying any additional cost. Although the MP distribution based on using the Gaussian atoms is always positive and it has convenient resolution, according to the MP the time marginal and the frequency marginal will not be obtained accurately. In this paper, it has been shown that by implementing the minimum cross ENTROPY (MCE) technique according to the MP distribution as a priory positive distribution, the new extracted distribution has the most similarity to the MP distribution and it also satisfies the correct time and frequency marginal.

Typographical data entry errors and incomplete documents, produce imperfect records in real world databases. These errors generate distinct records which belong to the same entity. The aim of APPROXIMATE Record Matching is to find multiple records which belong to an entity. In this paper, an algorithm for APPROXIMATE Record Matching is proposed that can be adapted automatically with input error patterns. In field matching phase, edit distance method is used. Naturally, it had been customized for Persian language problems such as similarity of Persian characters, usual typographical errors in Persian, etc. In record matching phase, the importance of each field can be determined by specifying a coefficient related to each field. Coefficient of each field must be dynamically changed, because of changes of typographical error patterns. For this reason, Genetic Algorithm (GA) is used for supervised learning of coefficient values. The simulation results show the high abilities of this algorithm compared with other methods (such as Decision Trees).

In this paper, we prove that every orthogonally higher ring derivation is a higher ring derivation. Also we find the general solution of the pexider orthogonally higher ring derivations {fn(x + y) = gn(x) + hn(y), = 0, {fn(xy) =∑, i+j=n gi (x)hj (y). Then we prove that for any APPROXIMATE pexider orthogonally higher ring derivation under some control functions φ, (x, y) and ψ, (x, y), there exists a unique higher ring derivation D = {dn}∞,n=0, near {fn}∞,n=0, {gn}∞,n=0 and {hn}∞,n=0 estimated by φ,and ψ, .

Results concerning local boundedness of operators have a long history. In 1994, Vesel, y connected the concept of APPROXIMATE monotonicity of an operator with local boundedness of that. It is our desire in this note to characterize an APPROXIMATE monotone operator. Actually, we show that a well-known property of monotone operators, namely representing by convex functions, remains valid for the larger subclass of operators. In this general framework we establish the similar results by Fitzpatrick. Also, celebrated results of Mart, inez-Legaz and Th, era inspired us to prove that the set of maximal "-monotone operators between a normed linear space X and its continuous dual X,can be identi, ed as some subset of convex functions on X ,X, .