7 research outputs found
Evaluating the CDMA System Using Hidden Markov and Semi Hidden Markov Models
CDMA is an important and basic part of todayās communications technologies. This technology can be analyzed efficiently by reducing the time, computation burden, and cost by characterizing the physical layer with a Markov Model. Waveform level simulation is generally used for simulating different parts of a digital communication system. In this paper, we introduce two different mathematical methods to model digital communication channels. Hidden Markov and Semi Hidden Markov modelsā applications have been investigated for evaluating the DS-CDMA link performance with different parameters. Hidden Markov Models have been a powerful mathematical tool that can be applied as models of discrete-time series in many fields successfully. A semi-hidden Markov model as a stochastic process is a modification of hidden Markov models with states that are no longer unobservable and less hidden. A principal characteristic of this mathematical model is statistical inertia, which admits the generation, and analysis of observation symbol contains frequent runs. The SHMMs cause a substantial reduction in the model parameter set. Therefore in most cases, these models are computationally more efficient models compared to HMMs. After 30 iterations for different Number of Interferers, all parameters have been estimated as the likelihood become constant by the Baum Welch algorithm. It has been demonstrated that by employing these two models for different Numbers of Interferers and Number of symbols, Error sequences can be generated, which are statistically the same as the sequences derived from the CDMA simulation. An excellent match confirms both modelsā reliability to those of the underlying CDMA-based physical layer
Predict The Spread of COVID-19 in Iran with A SEIR Model
The current coronavirus disease 2019 (COVID-19) outbreak has recently been declared a pandemic and spread over 200 countries and territories. Forecasting the long-term trend of the COVID-19 epidemic can help health authorities determine the transmission characteristics of the virus and take appropriate prevention and control strategies beforehand. Previous studies that solely applied traditional epidemic models or machine learning models were subject to underfitting or overfitting problems. This paper designed a predictive model based on the mathematical model Susceptible-Exposed-Infective-Recovered (SEIR). SEIR is represented by a set of differential-algebraic equations incorporated with machine learning techniques to fit the data reported to estimate the spread of the COVID-19 epidemic in long-term in the Islamic Republic of Iran up to the end of July 0f 2020. This paper reduced R0 after a certain amount of days to account for containment measures and used delays to allow for lagging official data. Two evaluation criteria, R2 and RMSE, had used in this research which estimates the model on officially reported confirmed cases from different regions in Iran. The results proved the modelās effectiveness in simulating and predicting the trend of the COVID-19 outbreak. Results showed the integrated approach of epidemic and machine learning models could accurately forecast the long-term trend of the COVID-19 outbreak
The Efficacy of Choosing Strategy with General Regression Neural Network on Evolutionary Markov Games
Nowadays, Evolutionary Game Theory which studies the learning model of players,has attracted more attention than before. These Games can simulate the real situationand dynamic during processing time. This paper creates the Evolutionary MarkovGames, which maps playersā strategy-choosing to a Markov Decision Processes(MDPs) with payoffs. Boltzmann distribution is used for transition probability andthe General Regression Neural Network (GRNN) simulating the strategy-choosing inEvolutionary Markov Games. Prisonerās dilemma is a problem that uses the methodand output results showing the overlapping the human strategy-choosing line andGRNN strategy-choosing line after 48 iterations, and they choose the same strate-gies. Also, the error rate of the GRNN training by Tit for Tat (TFT) strategy is lowerthan similar work and shows a better re
Testing the Semi Markov Model Using Monte Carlo Simulation Method for Predicting the Network Traffic
Semi-Markov processes can be considered as a generalization of both Markov and renewal processes. One of the principal characteristics of these processes is that in opposition to Markov models, they represent systems whose evolution is dependent not only on their last visited state but on the elapsed time since this state. Semi-Markov processes are replacing the exponential distribution of time intervals with an optional distribution. In this paper, we give a statistical approach to test the semi-Markov hypothesis. Moreover, we describe a Monte Carlo algorithm able to simulate the trajectories of the semi-Markov chain. This simulation method is used to test the semi-Markov model by comparing and analyzing the results with empirical data. We introduce the database of Network traffic which is employed for applying the Monte Carlo algorithm. The statistical characteristics of real and synthetic data from the models are compared. The comparison between the semi-Markov and the Markov models is done by computing the Autocorrelation functions and the probability density functions of the Network traffic real and simulated data as well. All the comparisons admit that the Markovian hypothesis is rejected in favor of the more general semi Markov one. Finally, the interval transition probabilities which show the future predictions of the Network traffic are given
Deep multi-task learning structure for segmentation and classification of supratentorial brain tumors in MR images
Identification of brain tumors border and determination of their possible pathology in MR images is an important step in pre-operation analyzing of this serious medical condition. Manual segmentation and classification of brain tumors could be challenge full in neurosurgical practice because of vast differences between brain tumors characteristic such as shape, border irregularity, consistency and etc. as well as interobserver variations. To solve this problem, some automatic methods have been proposed for brain tumors segmentation or classification during recent years, but an intelligence-based method for simultaneous identification of tumor type and tumor border in MR images has not proposed till now. Here, we have planned a unique automatic model includes a common encoder for feature representation, one decoder for segmentation and a multi-layer perceptron for classification of three common primary brain tumors (meningiomas, gliomas and pituitary adenomas) in brain MR images. The proposed model was examined on a brain tumor images dataset and the output were evaluated in both multi-task and single-task learning model. The multi-task learning model gains significant improvement in simultaneous classification and segmentation of brain tumors with promising accuracy of 97% for each task. So, this model could serve as a primary screening tool for early diagnosis of common primary brain tumors in general practice with a high success rate
An efficient deep multiātask learning structure for covidā19 disease
Abstract COVIDā19 has had a profound global impact, necessitating the development of infection detection systems based on machine learning. This paper presents a Multiātask architecture that addresses the classification and segmentation tasks for COVIDā19 detection. The model comprises an encoder for feature representation, a decoder for segmentation, and a multiālayer perceptron for classification. Evaluations conducted on two datasets demonstrate the model's performance in both classification and segmentation. To enhance efficiency and diagnosis accuracy, CTāscan images undergo preāprocessing using image processing algorithms like histogram equalization, median filtering, and mathematical morphology operations. The combination of the median filter preāprocessing and the proposed model yields impressive results in the classification task, achieving high accuracy, sensitivity, and specificity, with values of 0.97, 0.97, and 0.96, respectively, for dataset 1, and 0.96 in mentioned metrics for dataset 2. For segmentation, the proposed model, particularly with the average morphology preāprocessing, exhibits excellent performance with high accuracy, low mean squared error, high peak signalātoānoise ratio, high structural similarity index, and a mean dice coefficient of 88.86Ā Ā±Ā 0.05 for dataset 1, and 87.97Ā Ā±Ā 0.02 for dataset 2. Furthermore, the preātrained models consistently demonstrate the superiority of the median filter and proposed model in the classification task on the same datasets. In conclusion, the proposed multiātask model, incorporating image processing techniques, achieves remarkable results in both classification and segmentation. The utilization of preāprocessing algorithms and the multiātask framework significantly contribute to superior performance metrics. This study encourages further exploration of combining diverse image processing algorithms to advance infection diagnosis and treatment
Diagnosis of Heart Disease Using Feature Selection Methods Based On Recurrent Fuzzy Neural Networks}, \cjournal{IPTEK The Journal of Technology and Science
The World Health Organization (WHO) estimated one-third of all global deaths reason by cardiovascular diseases. Nowadays, artificial intelligence attracts many considerations in diagnosing heart disease. This study used trained recurrent fuzzy neural networks (RFNN) for diagnosing heart disease. This study also used five kinds of feature selection and extraction models for comparing the action of a model, such as data envelopment analysis (DEA), Linear Discriminative Analysis (LDA), Principle Component Analysis (PCA), Correlation Feature Selection (CFS), and Relief. By using these methods, this paper diagnosed whether the patient has a heart disease problem or not. The results showed that Correlation feature selection has the best operation in feature selection in RFNN by accuracy of 98.4%