Fuzzy Expert Ants to speed up big TSP Problems using ACS

Ant colony algorithms are a group of heuristic optimization algorithms that have been inspired by behavior of real ants foraging for food. In these algorithms some simple agents (i.e. ants), search the solution space for finding the suitable solution. Ant colony algorithms have many applications to computer science problems especially in optimization, such as machine drill optimization, and routing. This group of algorithms have some sensitive parameters controlling the behavior of agents, like relative pheromone importance on trail and pheromone decay coefficient. Convergence and efficiency of algorithms is highly related to these parameters. Optimal value of these parameters for a specific problem is determined through trial and error and does not obey any rule. Some approaches proposed to adapt parameter of these algorithms for better answer. The most important feature of the current adaptation algorithms are complication and time overhead. In this paper we have presented a simple and efficient approach based on fuzzy logic for optimizing ACS algorithm and by using different experiments efficiency of this proposed approach has been evaluated and we have shown that the presented concept is one of the most important reasons in success for parameter adapting algorithms

Recognizing emotional state of user based on learning method and conceptual memories

With the increased use of computers, electronic devices and human interaction with computer in the broad spectrum of human life, the role of controlling emotions and increasing positive emotional states becomes more prominent. If a user's negative emotions increase, his/her efficiency will decrease greatly as well. Research has shown that colors are to be considered as one of the most influential basic functions in sight, identification, interpretation, perception and senses. It can be said that colors have impact on individuals' emotional states and can change them. In this paper, by learning the reactions of users with different personality types against each color, communication between the user's emotional states and personality and colors were modeled for the variable "emotional control". For the sake of learning, we used a memory-based system with the user’s interface color changing in accordance with the positive and negative experiences of users with different personalities. The end result of comparison of the testing methods demonstrated the superiority of memory-based learning in all three parameters of emotional control, enhancement of positive emotional states and reduction of negative emotional states. Moreover, the accuracy of memory- based learning method was almost 70 percent

Automatic Optic Disc Center and Boundary Detection in Color Fundus Images

Accurately detection of retinal landmarks, like optic disc, is an important step in the computer aided diagnosis frameworks. This paper presents an efficient method for automatic detection of the optic disc’s center and estimating its boundary. The center and initial diameter of optic disc are estimated by employing an ANN classifier. The ANN classifier employs visual features of vessels and their background tissue to classify extracted main vessels of retina into two groups: the vessels inside the optic disc and the vessels outside the optic disc. To this end, average intensity values and standard deviation of RGB channels, average width and orientation of the vessels and density of the detected vessels their junction points in a window around each central pixel of main vessels are employed. The center of detected vessels, which are belonging to the inside of the optic disc region, is adopted as the optic disc center and the average length of them in vertical and horizontal directions is selected as initial diameter of the optic disc circle. Then exact boundary of the optic disc is extracted using radial analysis of the initial circle. The performance of the proposed method is measured on the publicly available DRIONS, DRIVE and DIARETDB1 databases and compared with several state-of-the-art methods. The proposed method shows much higher mean overlap (70.6%) in the same range of detection accuracy (97.7%) and center distance (12 pixels). The average sensitivity and predictive values of the proposed optic disc detection method are 80.3% and 84.6% respectively

A New 12-Lead ECG Signals Fusion Method Using Evolutionary CNN Trees for Arrhythmia Detection

The 12 leads of electrocardiogram (ECG) signals show the heart activities from different angles of coronal and axial planes; hence, the signals of these 12 leads have functional dependence on each other. This paper proposes a novel method for fusing the data of 12-lead ECG signals to diagnose heart problems. In the first phase of the proposed method, the time-frequency transform is employed to fuse the functional data of leads and extract the frequency data of ECG signals in 12 leads. After that, their dependence is evaluated through the correlation analysis. In the second phase, a structural learning method is adopted to extract the structural data from these 12 leads. Moreover, deep convolutional neural network (CNN) models are coded in this phase through genetic programming. These trees are responsible for learning deep structural features from functional data extracted from 12 leads. These trees are upgraded through the execution of the genetic programming (GP) algorithm to extract the optimal features. These two phases are used together to fuse the leads of ECG signals to diagnose various heart problems. According to the test results on ChapmanECG, including the signals of 10,646 patients, the proposed method enjoys the mean accuracy of 97.60% in the diagnosis of various types of arrhythmias in the Chapman dataset. It also outperformed the state-of-the-art methods

Developing Graph Convolutional Networks and Mutual Information for Arrhythmic Diagnosis Based on Multichannel ECG Signals

Cardiovascular diseases, like arrhythmia, as the leading causes of death in the world, can be automatically diagnosed using an electrocardiogram (ECG). The ECG-based diagnostic has notably resulted in reducing human errors. The main aim of this study is to increase the accuracy of arrhythmia diagnosis and classify various types of arrhythmias in individuals (suffering from cardiovascular diseases) using a novel graph convolutional network (GCN) benefitting from mutual information (MI) indices extracted from the ECG leads. In this research, for the first time, the relationships of 12 ECG leads measured using MI as an adjacency matrix were illustrated by the developed GCN and included in the ECG-based diagnostic method. Cross-validation methods were applied to select both training and testing groups. The proposed methodology was validated in practice by applying it to the large ECG database, recently published by Chapman University. The GCN-MI structure with 15 layers was selected as the best model for the selected database, which illustrates a very high accuracy in classifying different types of rhythms. The classification indicators of sensitivity, precision, specificity, and accuracy for classifying heart rhythm type, using GCN-MI, were computed as 98.45%, 97.89%, 99.85%, and 99.71%, respectively. The results of the present study and its comparison with other studies showed that considering the MI index to measure the relationship between cardiac leads has led to the improvement of GCN performance for detecting and classifying the type of arrhythmias, in comparison to the existing methods. For example, the above classification indicators for the GCN with the identity adjacency matrix (or GCN-Id) were reported to be 68.24%, 72.83%, 95.24%, and 92.68%, respectively

Identification and characterization of the channel-forming protein in the cell wall of Corynebacterium amycolatum

Soltan Mohammadi N, Mafakheri S, Abdali N, Bárcena-Uribarri I, Tauch A, Benz R. Identification and characterization of the channel-forming protein in the cell wall of Corynebacterium amycolatum. Biochimica et biophysica acta. 2013;1828(11):2574-2582.The mycolic-acid layer of certain gram-positive bacteria, the mycolata, represents an additional permeability barrier for the permeation of small water-soluble solutes. Consequently, it was shown in recent years that the mycolic acid layer of individual bacteria of the group mycolata contains pores, called porins, for the passage of hydrophilic solutes. Corynebacterium amycolatum, a pathogenic Corynebacterium species, belongs to the Corynebacteriaceae family but it lacks corynomycolic acids in its cell wall. Despite the absence of corynomycolic acids the cell wall of C. amycolatum contains a cation-selective cell wall channel, which may be responsible for the limited permeability of the cell wall of C. amycolatum. Based on partial sequencing of the protein responsible for channel formation derived from C. amycolatum ATCC 49368 we were able to identify the gene coram0001_1986 within the known genome sequence of C. amycolatum SK46 that codes for the cell wall channel. The corresponding gene of C. amycolatum ATCC 49368 was cloned into the plasmid pXHis for its expression in Corynebacterium glutamicum ∆porA∆porH. Biophysical characterization of the purified protein (PorAcoram) suggested that coram0001_1986 is indeed the gene coding for the pore-forming protein PorAcoram in C. amycolatum ATCC 49368. The protein belongs to the DUF (Domains of Unknown Function) 3068 superfamily of proteins, mainly found in bacteria from the family Corynebacteriaceae. The nearest relative to PorAcoram within this family is an ORF which codes for PorAcres, which was also recognized in reconstitution experiments as a channel-forming protein in Corynebacterium resistens

Surgical correction of upper lip deficit in patients with cleft lip using dermis fat graft

Background: This study aimed to assess dermis fat graft (DFG) as a choice to correct the tissue deficit in the free border of the upper lip in cleft lip repair surgery. Materials and Methods: Thirty-five individuals who referred to Alzahra Hospital at 2013–2014, with lip deformity following the primary repair surgery of cleft lip underwent surgery by DFG technique. Outcomes were assessed 4 months after the surgery based on comparison of preoperative and postoperative photographs. Results: The results in 18 (51.42%) patients were excellent, 10 (28.57%) good, and 7 (20%) intermediate according to the satisfaction of patients and investigators in terms of filling of lip deficit and motion of the upper lip. Moreover, complications and pain were minimal after 4-month follow-up. Conclusion: This method introduces an admissible method with 80% good to excellent results based on satisfactory of patients and surgeon