The Hidden Markov Model as a suitable model for time sequence modeling is used in this project for estimation of speech synthesis parameters. In our approach, HMMs generate cepstral coefficients and pitch parameter which are then feed to a speech synthesis filter named MLSA. To generate the parameters of speech synthesis using HMMs, an algorithm is used which utilizes the context dependent information of speech units provided by cepstral coefficients, and their first and second derivatives. In our project, a phone with known left and right context, named triphone, is used as speech unit. For speech unit modeling, we compare observations of each triphone in the database with its HMM model. The result of this comparison is a sequence of HMM states. The comparison is done using viterbi algorithm. Average number of presence times in each state of each triphone, constitute a model for triphone duration. During speech synthesis, in order to obtain necessary parameters for synthesizing a triphone, HMM parameters such as mean and variance vectors of each state are repeated based on duration model. Using mean and variances obtained from HMM models, cepstral coefficients and pitch frequency are calculated and then transformed to speech using MLSA filter. In order to take into account the effects of various parameters on the pronunciation of triphones, cart Decision Trees are also used. These Trees generate pitch and the duration of phonemes. In another way for automatic generation of pitch contour, we used the method proposed by Fujisaki. In this method, there is a global component for pitch contour and some local components for modeling of accents. To evaluate the performance of our speech synthesis system, MOS and DRT tests were conducted. The results of the MOS test were 3.8 for intelligibility, 3.9 for naturalness, and 3.5 for pleasantness when no Decision tree was used for duration and pitch modeling. In another MOS test, pitch and duration were modeled using Decision Trees. The results of the MOS test were 4.2, 4.4, and 4.1 for sentences existing in training database. These results were 4.3, 4.2, and 3.4 respectively for sentences out of training database. Pitch contour was also modeled using Fujisaki method. The results of the MOS test for this kind of pitch modeling were 4.6, 4.3, and 4.5 for sentences existing in training data base. These results were 4.5, 4.0, and 4.4 respectively for sentences out of training database. The DRT test result was 88% for word pairs synthesized using Decision Trees for both duration and pitch modeling. These results show the suitability of the method used in this project.  

A High-gain, fully balanced preamplifier is presented. The proposed structure advantages flipped voltage follower scheme to achieve a compact current conveyor with very low input impedance. The presented current conveyor then is used as a core element to realize a high-gain, gm-Enhanced trans-conductance amplifier. The presented amplifier is suitable for application as a preamplifier. The high gain of amplifier makes it very suitable to be configured in a feedback form to deliver a high-precision predefined or programmable amplification gain. The proposed structure draws a very low power of 150nW from a 0.6V supply voltage. The Spectre Post-layout simulations with TSMC 180nm CMOS technology have been performed. The proposed amplifier exhibits an open-loop DC gain of 141.5dB and 3-dB frequency bandwidth of 2.4kHz at 60dB closed-loop configuration. The load capacitance is set to be 5pF. The proposed structure also delivers high CMRR and PSRR values of 148.3dB and 153.7dB, respectively.

Choosing a stock portfolio is always one of the most important issues for investors. Theoretically, selecting a stock portfolio can be solved by minimizing risk assumptions with the help of mathematical relationships, but with the variety of choices in the capital market, mathematical relationships alone are not an effective solution. The variety of investment tools and the differences in the functionality of investors’ complexity have complicated the selection process. Now the expansion of financial and capital markets, the use of rule-based systems for quick Decisions, with minimal risk and away from human error, design, development, or improvement of these systems can be a competitive advantage. In the present study, neural network algorithms and genetic programming algorithms have been used to identify effective features and the Decision tree to improve id3 has been proposed as a method for predicting price and trend of stock price change to select the optimal basket. The research results show that in addition to reducing computational and memory overhead, the proposed method is able to accurately predict severe fluctuations with nonlinear patterns and compared to modern methods such as nearest neighbor search, linear regression, autoregressive integrated moving average, and time series prophet algorithm will do better.

Background: Malaria is an infectious disease infecting 200-300 million people annually. Environmental factors such as precipitation, temperature, and humidity can affect its geographical distribution and prevalence. The environmental factors are also effective in the abundance and activity of malaria vectors. The present study aimed at presenting a model to predict the type of malaria. Methods: This cross-sectional study was conducted using the data of 285 people referring to a health center in Saravan from June 2009 to December 2016. Clementine 12. 0 was used for data analysis. The modeling was done using classification and regression Decision Trees, chi-squared automatic interaction detector, C 5. 0, and neural network algorithms. Results: The accuracy of classification and regression Decision Trees, chi-squared automatic interaction detector, C5. 0, and neural network was 0. 7217, 0. 6698, 0. 6840, and 0. 6557, respectively. Classification and regression Decision Trees performed better than the other algorithms in terms of sensitivity, specificity, accuracy, precision, negative predictive value, and area under the ROC curve. The sensitivity and area under the ROC curve were 0. 5787 and 0. 66 for classification and regression Decision Trees. Conclusions: Applying data mining methods for the analysis of malaria’ s data can change the current attitude toward malaria type determination. Faster and more precise identification of malaria type helps determine the proper cure and improve the performance of health organizations.

Introduction A Decision tree is a flow chart-like graph structure of Decision nodes, branches, and leaf nodes starting from the root and ending at the leaves. The node is the independent variable on which the test is performed, and the root node is placed at the top of the tree, while the leaf is the dependent variable (answer) or category label, which is the last node of the tree. The Decision at each stage of tree construction depends on the previous branching operation, which is crucial to the predictive capability of the tree. The branching method of information gain, by using the concept of entropy, measures how much information a feature provides about a class and decides to split the tree at each node. Material and Methods The first dataset describes the diagnosis of cardiac Single Proton Emission Computed Tomography (SPECT) image contains 267 SPECT image sets (patients), and 22 Attributes such that each of the patients is classified into two categories: normal and abnormal. The second data set contains 1024 binary attributes (molecular fingerprints) used to classify 1687 chemicals into two classes (binder to androgen receptor/positive, non-binder to androgen receptor /negative). Also, a real-world dataset used that contains 90 instances and 7 attributes include gender, blood pressure, blood sugar, cholesterol, smoking, weight and occupation of the patient, which were collected to predict the determination of the treatment method (medical treatment or angiography). A new approach is proposed to produce a Decision tree based on the T-entropy criterion. The method applied on the three datasets, examined by 11 evaluation criteria and compared with the well-known methods of Gini index, Shannon, Tsallis, and Renyi entropies for splitting the Decision tree, with a proportion of 75 % for training and 25 % for testing and for 300 times of execution (each time of execution leads to the production of a Decision tree). Also, a comparison is made between T-entropy and other discussed splitting measures in terms of the area under the ROC curve (AUC). Results and Discussion The performance of splitting methods of the Gini index, Shannon, Tsallis, Renyi entropies and T-entropy are examined. The evaluation criteria of accuracy (A￿, ￿, ), sensitivity, specificity, positive predictive value (PPV)(or precision), F-S￿, ore index (F١, ), negative predictive value (NPV), false discovery rate (F￿, R), false positive rate (FPR), false negative rate (FNR) and Mean square error for the three data sets were calculated. The maximum values for the first six criteria and the minimum values for the second four criteria indicate the better performance of the Decision tree based on the introduction method. Also, the AUC value of the t-entropy method presented for all three data sets is higher than other methods, which indicates that the value of the true positive rate is higher than the false positive rate in the t-entropy method compared to other methods. Conclusion The results suggest that the proposed node-splitting method based on Tentropy has better behaviour than the other discussed methods for both low and high numbers of samples. Also, today because of the increasing growth of the big data problem and, on the other hand, the superiority of the Tentropy splitting method over the other investigated methods on different sizes of the dataset, the benefit of this method is twofold.

Optimal selection of a company for investment considering its financial ratios is a challenge that is expected to be somewhat simplified by reducing the amount of data. Accurately recognizing the relative importance of metrics in any company is not easy for many Decision-makers and investors. The purpose of this study is to provide methods for Decision-making that can be implemented without specialized financial knowledge. For this purpose, a sample of 172 companies listed on the Tehran Stock Exchange as a company-year, during the period 2008-2019 was examined. First, the financial ratios were prioritized using Decision tree regression analysis (type CART) to predict the life cycle. The results showed that the cash ratio and Debt to Equity Ratio were the most and the least important factors, respectively. Then, using fuzzy hierarchical analysis (FAHP) and TOPSIS, financial ratios were prioritized to evaluate the Financial Performance of companies that leverage ratios and profitability ratios had the highest and lowest ranks, respectively.