پیش بینی

In today's world, breast cancer is one of the leading causes of death and one of the most dreaded diseases known, which is one of the potential causes of death in women. Although it is considered one of the most treatable diseases, it is important that with its timely diagnosis, the death rate can be reduced in the long term. As a solution for the prediction and treatment of this disease, the automatic disease diagnosis system in diagnosis and analysis is a great help to the medical field, which provides a quick response, reliability, effectiveness, and also reduces the risk of death. Therefore, in this article, the long short-term memory (LSTM) algorithm, which is a learning algorithm, was used, because it leads to more stable and more accurate models. The simulation results and comparison with other articles showed that the use of this algorithm has improved the accuracy and sensitivity in breast cancer diagnosis and we were able to determine whether the suspicious area is rough or not with the help of the LSTM algorithm. The evaluations show that the sensitivity of the proposed method is very favorable, especially in dealing with small nodules. The estimated positive error rate for the proposed method is 16.67%, which is less than other works.

Coronavirus disease 2019 or Covid-19, which is also called acute respiratory disease NCAV-2019 or commonly called corona, is a respiratory disease caused by acute respiratory syndrome coronavirus-2. Forecasting the number of new cases and deaths during todays can be a useful step in predicting the costs and facilities needed in the future. This study aims to model and predict new cases and deaths efficiently in the future. Nine popular forecasting techniques are tested on the data of Covid-19 in Yazd city as a case study. Using the evaluation criteria of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and the mean absolute percentage of error (MAPE) of the models are compared. According to the selected evaluation criteria, the results of the comprehensive analysis emphasize that the most efficient models are the ARIMA model for predicting the cumulative cases of hospitalization of Covid-19 and the Theta model for the cumulative cases of death. Also, the autoregressive neural network model has the worst performance among other models for both hospitalization and death cases.

The purpose of this paper's prediction of survival or death within 30 days is based on a cerebral hemorrhage. Timely and correct diagnosis and treatment of cerebral hemorrhage are essential. If the patient's death is predicted during these thirty days, the treating physician should use intensive care and more treatment for the patient. Cerebral hemorrhages require immediate treatment and rapid and accurate diagnosis. In this article, using the volume of cerebral hemorrhage and the patient's age and using the neural network of support vector machine (SVM), it is predicted what percentage of people with cerebral hemorrhage survive and what percentage die. Parameters of cerebral hemorrhage volume and, age of patients, neural network input are considered. The network's output is the survival or death of patients with cerebral hemorrhage over the next thirty days. The data we used included the bleeding volume and age of 66 patients with lobar hemorrhage, 76 patients with deep bleeding, nine patients with Pontine hemorrhage and 11 patients with cerebellar hemorrhage. All bleeding models are considered as input to the support vector machine neural network. The overall accuracy of the designed support vector machine neural network is 93%. Regardless of the type of cerebral hemorrhage, the survival or death of people with cerebral hemorrhage within 30 days is predicted.

Breast cancer is one of the deadly diseases among women and every year millions of people around the world die due to this disease.If breast cancer is detected in the early stages,the chances of survival will increase.One of the methods of breast cancer diagnosis is the use of knowledge discovery methods such as machine learning. Machine learning methods can discover the pattern of breast cancer by analyzing the information of patients and their records.The important advantage of using machine learning methods to diagnose breast cancer is to reduce diagnosis costs and help more accurate diagnosis by specialist doctors. One of the methods of breast cancer diagnosis is the use of a support vector machine. Support vector machine is a method for classifying samples with the aim of reducing operational risk in classification.One of the important challenges of the support vector machine is the output error of the model due to the lack of optimal selection of the learning parameters.In the proposed method to reduce the classification error of malignant and benign people, Harris Hawks's optimization algorithm has been used.The role of Harris Hawks's algorithm in the proposed method is to optimize the parameters of the support vector machine to reduce the diagnosis error of malignant patients.The evaluations have been done in the MATLAB programming environment and on the Wisconsin dataset.The evaluations show that the proposed method in breast cancer diagnosis has an accuracy of 99.31% and is more accurate than methods such as Wall's optimization algorithm in breast cancer diagnosis.

Electric power and power distribution are prominent infrastructures for economic development in any developing country like Iran. Also, the power distribution network is a very important supply chain that combines a variety of processes. Smart electrical energy distribution networks are one of the latest technologies in the world. The main goal of these networks is to provide reliable electricity, increase the reliability factor and network stability, and respond to the growing needs of customers with minimal damage to the environment, profit, and high efficiency. In the last three decades, the rapid evolution and prevalent adoption of information systems, distribution analysis tools, computational models, and more recently, the emergence of smart grid technologies have given utilities access to the data and tools required for improving these analyses and the possibility of increasing the efficiency of power distribution systems (by, for example, reducing losses and optimizing voltage profiles). Forecasting the future state of the network with the least error brings us closer to the smart network. Because electricity is a mortal product, a comprehensive approach to unplanned power extinction (outage) time is very valuable in preventing any power distribution losses. Various accidents disrupt (cause breakdowns in) the power distribution network, which can be repaired and restored without a hotline. One of the main reasons for customers' power outages is the blackouts in the distribution field, which are affected by technical and non-technical events in the electricity distribution networks. Forecasting these events and managing them can be effective in reducing unplanned power extinction (outage) time. The purpose of this article is to present a model for predicting the duration of unplanned power extinction (outage) and unsold energy based on the data recorded from 121 systems (controllers), the urban network of the three power distribution companies in Yazd province. The final result shows that the ARIMAX model(s) shows less error than the ARIMA model(s) and presents better prediction. Therefore, using exogenous variables in predictions and not being satisfied with the fluctuations of a variable can improve predictions. The model proposed for predicting unsold energy is ARIMAX(1,0,1)(0,0,0) considering the number of incidents and the time of unplanned outages as exogenous variables. The model also shows that in July 2022, the unplanned power extinction (outage) time of this network will be approximately ten hours and also the unsold power will be approximately 6 MWH. On the other hand, the community is without electricity and dissatisfaction has arisen, which lies in economic and social losses. Therefore, with this warning, managers should re-examine the factors of disruption and lack of electricity supply and think of measures to reduce these blackouts when planning for this month of the year

One of the applications of sensor networks is to track moving target. In designing the algorithm for target tracking two issues are of importance: reduction of energy consumption and improvement of the tracking quality. One of the solutions for reduction of energy consumption is to form a tracking cluster. Two major challenges in formation of the tracking cluster are when and how it should be formed. To decrease the number of messages which are exchanged to form the tracking cluster an auction mechanism is adopted. The sensor’s bid in an auction is dynamically and independently determined with the aim of establishing an appropriate tradeoff between network lifetime and the accuracy of tracking. Furthermore, since the tracking cluster should be formed and activated before the target arrives to the concerned region (especially in high speed of target), avoidance from delay in formation of the tracking cluster is another challenge. Not addressing the mentioned challenge results in increased target missing rate and consequently energy loss. To overcome this challenge, it is proposed to predict the target’s position in the next two steps by using neural network and then, simultaneously form the tracking clusters in the next one and two steps. The results obtained from simulation indicate that the proposed algorithm outperforms AASA (Auction-based Adaptive Sensor Activation).

The high power passing through transmission systems and the high costs due to the fault occurrence in these lines have encouraged researchers to pay special attention to protection issues in this area. The limitations and deficiencies of traditional protection methods and their strong dependencies on the system operating conditions doubles the importance of early fault detection and its prediction utilizing new techniques. Timely detection and warning issuance toward the possibility of fault occurrence can be accomplished by analyzing the data and information obtained from the system and examining the relationships between different parameters. In this paper, machine learning methods are used, which have the ability to predict the occurrence of faults with appropriate accuracy independent of the operating area of the system. To evaluate the performance of the models, a large amount of data has been generated in various operating conditions and applied as input to the algorithms under study. Also, the effects of different weather conditions as one of the important factors have been considered. For the sake of greater generality, accuracy check, and comparability of the results, three methods including KNN, SVM, and decision tree in two modes (unbalanced and balanced data in the existing classes) have been used, and the outcomes have been presented. The simulations and modeling presented in this paper have been implemented using Python and MATLAB.

in this paper a new algorithm has been proposed to predict unplanned islanding. To predict unplanned islanding, rotor angle and speed time series have been used. Times of fault occurrence and clearance are estimated using monitoring system response based on the variation of phasor measure data. The estimated times of fault occurrence and clearance are used to determine suitable calculation window for uncontrolled islanding prediction. The proposed algorithm is applied to IEEE 39 bus test system. The result shows that the objective function can be used to estimate fault occurring and clearance time to predict unplanned islanding.

Epilepsy is a neural disorder with unknown nature and epileptic patients suffer from the consequences of unexpected seizures. In this paper, we proposed a new method to predict epileptic seizures using heart rate variability (HRV) signal analysis. During preictal period of epilepsy, increasing in nervous activities of neurons affects the autonomic nervous system that disturbs heart rates. Therefore, epileptic seizures can be predicted through HRV monitoring. In our method, we extracted 12 features of HRV signal from different domains: time, frequency, time-frequency and non-linear domain. We used Multivariate Statistical Process Control (MSPC) algorithm for anomaly detection which is able to detect anomalies that cannot be detected by monitoring each variable independently. This algorithm has been applied to the clinical data collected from 17 patients. The obtained results demonstrated that the proposed method can predict seizure onset with an accuracy of 88.2%. The proposed HRV-based seizure prediction algorithm is more promising than the conventional EEG-based methods from the viewpoint of practical use.

Alzheimer’s disease (AD), a progressive, irreversible neurodegenerative disorder, occurs most frequently in older adults and gradually destroys regions of the brain that are responsible for memory, thinking, learning, and behavior. In this paper, AD prediction is investigated based on rs-fMRI and sMRI analysis. Three feature selection algorithms based on soft computing method has been proposed to classify MCI-C from MCI-NC through training SVM. This is the first study used to integrate rs-fMRI and sMRI for AD prediction. The results refer to the significant brain areas (functional and structural) impaired in AD. Furthermore, NBS method on brain functional parcellations has been utilized for separating MCI-C from MCI-NC and detecting the discriminative ability networks for AD prediction.

Today, energy and its consumption are the main strategic plan of organizations and also the development of urban transport systems by considering a variety of economic, scientific, industrial, climate and growing urbanization is essential. Analysis of past trends in energy is the key to predict future trends, with regard to the rate of development of metro, for planning and future-oriented macro economic policies.  in this research has been used to predict the energy consumption of Tehran Metro Line 1 from the GMDH Neural Network Model Which is capable of detecting and screening low-input input variables In the course of training the network and removing them during the exam period. and also Was compared To understand the accuracy of the prediction with the ARIMA model. in this research, was detected twelve variables affecting Tehran's metro energy consumptionand is considered as input variables of the model. The results indicate that The GMDH neural network model has a much lower error rate than the ARIMA model and has a higher predictive accuracy.

It is difficult to apply the real world’s conceptions due to their uncertainty. Generally, time series are known to be non-linear or non-stationary. Regarding these two features, a system should be sensitive enough to apply the unity of time series and repeat this sensitiveness in the prediction. A predict system can exactly scrutinize the hidden features of time series and also can have high predicting runs. Lots of statistical tools such as regression analysis, gradient average, exponential gradient average and auto regression gradient average are used in traditional predictions. One of the biggest challenges of these approaches is the necessity of greater observations and the avoidance of linguistic variables or subjective experts’ ideas. Also these methods are limited to linear being assumptions. In order to dominate the limitations of traditional methods, many researchers have utilized soft computations like fuzzy logic, fuzzy neural networks, evolutionary algorithms and etc.
In this paper, we proposed a new fuzzy prediction novel based on the high order fuzzy time series. Our proposed model is based on the higher order fuzzy time series prediction computational approach. In this method a group of features are evaluated, by adding the value of the preceding element of predicting element to the result of the series’ differences. At that, particle swarm optimization is used to optimize Calculation algorithm features, which renders a better performance in order to solve the problems of higher order fuzzy time series. Then by choosing the best features, a result can be inferred as the predicting value.
The performance of the approach is presented in which after the fuzzification of time series and creating the logical fuzzy relations, by using the lower limit of the predicting element’s range and its consecutive range, and the resulted difference of sequential elements, some specific computations are done and a set of features are gained. Then, using the particle swarm optimization function, the best parameter is selected. The fitness function in the proposed method has two parts: a general section (the average of all orders) and a partial (Every columns orders). In general section, the overall average of error is shown. In Every columns orders section each column individually considered. For the second to tenth order (9 PSO separate) the answer is checked. The method is as follow; we used two parameters b and d for the feature calculation algorithm. The amount of d was manually and randomly between 3 – 1000, but PSO find the amount of b.
Properties obtained by this method, have less outliers data and waste, which it causes predicted closer, with less error.
Finally, defuzzification is performed. The yielded score is the predicted integer value of considered element.
In order to decide the precision of the prediction’s rate, we compare the proposed model to other methods using the mean square error and the average error. In order to show the efficiency of the proposed approach, we have implemented this method on the Alabama University’s enrollment database. It can be observed that the suggested method provides better results compared to the other methods and also renders a lower error.

For over three decades, hydrologists were recommended multivariate models to describe and modeling complex hydrology data. While recently the multivariate models in hydrology is discussed. In multivariate models, the modeling and predicting various parameters can improve by involving other factors. In this study, using both univariate and multivariate periodic ARMA models for modeling monthly discharge in Nazloochaei River in West Azerbaijan Province during the period of 1962-2011 were compared .The results of evaluation and verification models showed that the multivariate periodic ARMA models by involving the climatic parameters such as temperature and precipitation of the basin, the more accurately than univariate periodic ARMA models. Also the result showed that the selected models, maximum and minimum points of discharge to the appropriate model.

The need for prediction and patterns of gas consumption especially in the cold seasons is essential for consumption management and policy planning decision making. In residential and commercial uses which account for the bulk of gas consumption in the country the effects of meteorological variables have the highest impact on consumption.  In the present research four variables include daily average temperature , average daily relative humidity , sunny hours per day , and average wind speed  were used to predict consumption in the short term. The results for the three cities  include Ilam, Ivan and mehran have been obtained in the form of quadratic polynomial equations and according to the above variables. The results for three models based on the normalized root mean square error have are 0.21, 0.112 and 0.123. Also the rerult of coefficent of determination for three cities are 0.8356, 0.87060.7936, respectively with are favorable.

Designing a system with an emphasis on minimal human intervention helps users to explore information quickly. Adverting to methods of analyzing large data is compulsory as well. Hence, utilizing power of the data mining process to identify patterns and models become more essential from aspect of relationship between the various elements in the database and discover hidden knowledge. Therefore, Rough set theory can be used as a tool to explore data dependencies and reducing features outlined in a data set. The main purpose of the rough theory is to obtain approximate concepts of acquired data. This theory is a powerful mathematical tool for arguing in ambiguous and indeterminate terms that provides methods for remove and reduce unrelated or excessive knowledge information on the data sets. This process of data reduction is based on the main task of the system, and without losing the basic data of the data sets. Rough set theory can play a very effective role to support decision-making systems, but in some cases, with increasing data volumes, there are inconsistent or collisional results which using swarm intelligence-based methods can choose the best of the contradictory, effectless or dummy data. This will bring interesting, unexpected and valuable structures from within a wide range of data. Since the ant colony optimization compares all the exploratory paths generated by each ant and the best route is selected from the existing paths, so considering the improvement of the selecting the main features and improving the theory of the Rough set, paths are not eliminated from the possible paths. In this research, the combination of the ant colony optimization and rough set theory have been used to find the subset of the main features and to delete the inappropriate information with the loss of the minimum information. This research will improve the features reduction technique employment Rough set theory and ant colony optimization. The gist of this research is removing useless information with minimal information loss. The results on petroleum prices data evaluation demonstrate that the hybrid method is more efficient than recent methods.

In this paper, a predictive approach is applied to design a nonlinear multivariable controller to generate trajectory tracking control commands of non-holonomic robots. In this method, the nonlinear responses of the mobile robot are predicted using Taylor series. The optimal control laws are analytically developed by minimizing the difference between the predicted and the desired responses of the system outputs. The obtained control laws lead to the feedback linearization. Here, the nonlinear kinematics model of mobile robot is used for design of the controller and simulations. The performed analyses along with the simulation results indicate that the designed controller can successfully track the reference trajectories with high performance in different maneuver conditions.

It is widely accepted that natural gas is a clean energy source that can be used to meet energy demand for heating and industrial purposes among the fossil fuels and its usage remarkably increases in order to maintain a clean environment in many countries in the world. In this paper, factors affecting gas consumption were firstly identified and then GMDH-Type Neural Networks has been used for modeling and prediction of gas consumption using input-output data set. To validate the proposed model, a case study was carried out based on the data consisted of 84 sets for 7 years obtained from regional gas distribution company of Rasht city. For modeling, the experimental data were divided into train and test sections (70% for training and 30% for testing). The predicted values were compared with those of experimental values . The GMDH-Type Neural Network model values showed a very good regression with the experimental results and the Coefficient of determination was obtained 0.8943.

Wind speed prediction can be regarded as significant factor in control of wind turbines, schedule of the connection/disconnection of turbines and stability guarantee of power grids which is commonly carried out in various approaches. In this paper, a chaos based approach by analyzing only the previous measured data is proposed. For this purpose, in addition of evaluating the chaotic nature of wind speed data, the chaos theory with Neural Network techniques in forecasting session are combined in order that we can propose a method for wind speed prediction. For this regard, at first the correlation dimension and largest lyapunov exponent of wind speed time series are computed to prove that wind data generator process is chaotic. Then phase space of data generator dynamic is reconstructed. In this regard, we use the False Nearest Neighbors (FNN) algorithm to determine the embedding dimension and Average Mutual Information (AMI) approach to measure time delay for phase space reconstruction. Afterwards, Multi Layers Perceptron (MLP) neural networks and Radial Basis Function (RBF) neural networks are proposed to predict the wind speed which its structure is designed based on time delay and embedding dimension data. At the end, proposed methods apply on real data and results are expressed.