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Fig. 2 in Revision of the Astyanax orthodus species-group (Teleostei: Characidae) with descriptions of three new species

Fig. 2. Astyanax embera sp. nov., holotype, 83.0 mm SL, Telembí River at mouth of Yamunde River, 1 km below Barbacoas, Municipality of Barbacoas, Nariño, Pacific coast of Colombia (IUQ 3614). Scale bar = 1 cm.

Fig. 3 in Revision of the Astyanax orthodus species-group (Teleostei: Characidae) with descriptions of three new species

Fig. 3. Shape of caudal-peduncle spot in species of the Astyanax orthodus species-group. A. Astyanax orthodus Eigenmann, 1907. B. Astyanax embera sp. nov. Scale bar = 1 cm.

Fig. 10. Astyanax superbus Myers, 1942, 70.4 in Revision of the Astyanax orthodus species-group (Teleostei: Characidae) with descriptions of three new species

Fig. 10. Astyanax superbus Myers, 1942, 70.4 mm SL, Guache River, Garabote, Portuguesa state, Venezuela (MCNG 36349). Scale bar = 1 cm.

Fig. 9 in Revision of the Astyanax orthodus species-group (Teleostei: Characidae) with descriptions of three new species

Fig. 9. Squamation of pectoral-fin base, ventral view. A. Astyanax bimaculatus (Linnaeus, 1758). B. Astyanax yariguies (Torres-Mejía, Hernández & Senechal, 2012) comb. nov. In A, the pore of pectoral-fin is in epithelial tissue, covered or circumscribed by three scales indicated by the curved line, while in B, the pore of the base of pectoral-fin is part of a scale circumscribed by four scales, indicated by the curved line. Scale bars = 1 cm.

Fig. 5 in Revision of the Astyanax orthodus species-group (Teleostei: Characidae) with descriptions of three new species

Fig. 5. Astyanax boliviensis sp. nov., paratype, 79.0 mm SL, Colonia San Carlos River, Isiboro, Mamoré, Bolivia (UMSS 5282). Scale bar = 1 cm.

Fig. 8. Astyanax orthodus Eigenmann, 1907. A. Holotype, 88.3 in Revision of the Astyanax orthodus species-group (Teleostei: Characidae) with descriptions of three new species

Fig. 8. Astyanax orthodus Eigenmann, 1907. A. Holotype, 88.3 mm SL, Truando River, Atrato River Basin, Chocó, Pacific coast of Colombia (USNM 55655). B. 32.5 mm SL, Vereda Sautatá, Atrato River drainage, Chocó, Pacific coast of Colombia (IAVH 7146). Scale bars = 1 cm.

Fig. 4 in Revision of the Astyanax orthodus species-group (Teleostei: Characidae) with descriptions of three new species

Fig. 4. Distribution of species of the Astyanax orthodus species-group (symbol includes type localities).

Fig. 13 in Revision of the Astyanax orthodus species-group (Teleostei: Characidae) with descriptions of three new species

Fig. 13. Astyanax moorii (Boulenger, 1892), 64.7 mm SL, Creek near the Manso Hydroelectric Plant, Cuiabá River drainage, Fazenda Nova region, Mato Grosso State, Brazil (MNRJ 29284). Scale bar = 1 cm.

Fig. 6 in Revision of the Astyanax orthodus species-group (Teleostei: Characidae) with descriptions of three new species

Fig. 6. Astyanax gandhiae sp. nov., holotype, 89.4 mm SL, Condorcanqui, Amazonas, Upper Cenepa River drainage (MUSM 46845). Scale bar = 1 cm.

Fig. 12 in Revision of the Astyanax orthodus species-group (Teleostei: Characidae) with descriptions of three new species

Fig. 12. Astyanax yariguies (Torres-Mejía, Hernández & Senechal, 2012) comb. nov., paratype, 75.5 mm SL, La Concordia Creek, Santander, Colombia (ICNMNH 17642). Scale bar = 1 cm.

Fig. 11 in Revision of the Astyanax orthodus species-group (Teleostei: Characidae) with descriptions of three new species

Fig. 11. Astyanax villwocki Zarske & Géry, 1999, 74.7 mm SL, Caquetá River, Colombia (IUQ 3593). Scale bar = 1 cm.

Fig. 7. Astyanax bopiensis nom. nov., 143.9 in Revision of the Astyanax orthodus species-group (Teleostei: Characidae) with descriptions of three new species

Fig. 7. Astyanax bopiensis nom. nov., 143.9 mm SL, Quebrada Culli, Madre de Dios River drainage, Upper Amazon, Peru (MUSM 3758). Scale bar = 1 cm.

Fig. 1. A in Revision of the Astyanax orthodus species-group (Teleostei: Characidae) with descriptions of three new species

Fig. 1. A. Distribution of pigment in the humeral region of Astyanax species. B. A. bimaculatus (Linnaeus, 1758). C. A. caucanus (Steindachner, 1879). D. A. mexicanus (De Filippi, 1853). E. A. courensis Bertaco, Carvalho & Jerep, 2010 (modified from Bertaco et al. 2010). The anterior humeral spot consists of two overlapping layers of pigment identified by numbers 1 and 2 (see Román-Valencia et al. 2015: fig. 1): 1. Diffuse vertical bar. 2. Horizontal spot with concentrated pigments.

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Figure 6. Three interfaces available on the mobile device

The device provides two important facilities: multimedia facilities (it allows recording, processing and playing audio samples) as well as graphic facilities (it provides a friendly and accessible interface). In Figure 6 is illustrated the main page of the application that is implemented on the mobile device (a), as well as two types of exercises; (b) the child is required to identify whether a sound is present in a word (which is indicated by an image); and (c), the child is required to choose a word from a group of paronyms.

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Figure 1. DiagAgentExpertDM architecture (adapted from Ioniță, 2014) -Intelligent System for Diagnosis of a Three-Phase Separator

For the current hybrid system (DiagAgentExpertDM) , data from different sources and in various forms are preprocessed, in order to represe nt them in a unified way to be able to upload them in a learning module. Simultaneously, inconsis tency tests are made to eliminate the measurement errors caused by improper calibration o f transducers etc. In condition of using a diagnosis method based on p rocess history, the next step in fault identification referring to the gas-oil separation process is to scan the historical data. Retrieved d ata will possess a label with a certain priority which will be used in the next retrieval process. The learning module is supplied with multiple preproces sed data samples, in order to extract knowledge from them. For each data samples, a data mining alg orithm will be applied (figure 1).

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Figure 2. Correct Classified Instances for different data sets-Intelligent System for Diagnosis of a Three-Phase Separator

The data mining models may be considered a superior technique that may be successful applied in diagnosis and may be develop in the futu re on the base of more training data to increase the accuracy of results. Industrial processes are dynamic processes with ran dom behavior and whose evolution over time cannot be predicted unless it is well known th e process model and use advanced predictive techniques. Consequently, design and implement an a utomated online monitoring and diagnosis three-phase separator remains a future direction of research conducted so far. Conceptually, this system should have permanent acc ess to data collected from field transducers, to be able to identify the type of fau lt occurred, to locate the fault and provide recommendations to remedy abnormal operating condit ion. Also, updating the database defects with new types of defects occurred and the adequate solu tions adopted for eliminating errors in the operating mode is an important feature to be consid ered during the design of the online diagnosis system. This is possible if the system would have s elf-learning capabilities. To acquire this "skill", the automatic online diagnosis system may contain a diagnosis module based on artificial neural networks.

BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 19. Accuracy of classification using the three methods: KNN, SVM and our method for MCI subjects

Whatever the patient condition, Normal, MCI or AD, our method has provided us with better results. Advocate Example precision for Normal Patients was found 96% as opposed to 88% for the SVM method and 84% for KNN. For MCI patients was found 88% as opposed to 80% for the SVM method and 72% for KNN. Also for AD patients were found 92% as opposed to 88% for the SVM method and 80% for KNN. Our classification method gave us the best results, finding overall accuracy of 92% as opposed to 84% for the SVM method and 78.66% for KNN.

BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 20. The accuracy of classification using the three methods, KNN, SVM and our method, for AD subjects

BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 18. Accuracy of classification using the three methods, KNN, SVM and our method, for normal subjects

We present three figures representing the accuracy of the classification using the three methods, KNN, SVM and our method for normal, MCI and Alzheimer subjects.

BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 2. Three Corpus Calosum: Normal, MCI, AD

The three figures above present the Corpus Callosum relating to three topics: Normal Topic by MCI (Mild Cognitive Impairment), Alzheimer’s topic. Secondly, we will present our clustering method to classify the test subject between 3 classes: N (Normal), MCI (Mild Cognitive Impairment), and AD (Alzheimer's disease).