Majlesi Journal of Telecommunication Devices
Volume:12 Issue: 2, Jun 2023

The main problem related to heart disease is the lack of timely diagnosis or the general weakness in the diagnosis of this disease, which is also due to the lack of selection of the appropriate model by the doctor or the lack of proper use of standard models. One of the essential applications of data mining techniques is related to medicine and disease diagnosis. One of the data mining techniques is information clustering. This paper will try to provide a model for the diagnosis of heart disease and its improvement in terms of accuracy on the standard UCI heart database. In this research, with a comprehensive and complete review of the C-Meaning fuzzy clustering method and neural networks in the field of heart disease prediction, an attempt is made to improve these solutions and provide new solutions in this field. The main goal is to combine these two data mining algorithms, both of which alone showed the highest accuracy and the fastest speed in past research. The current authors are trying to find a model that has higher accuracy and speed than the previous methods and makes fewer mistakes and has significantly higher efficiency than other models. The numerical tests implemented on the proposed model show the superiority of the new model compared to the conventional methods in the literature.

Using chaff to deflect missile guidance radar or missile seeker is a common and effective defense method in military vessels. To deal with this defensive method, focus on specific characteristics of the target and chaff signals. These features should be able to perform properly in different operating conditions of the radar or different environmental conditions that change the behavior of the radar’s return signals. But there is no feature that can distinguish the target from the target with appropriate accuracy in all conditions. In this article, a structure is presented for detecting chaff and target in a radar and has been able to improve the accuracy of target detection in presence of chaff. Also, to improve the performance of the radar with a cognitive approach, its transmitted waveform is optimally selected and changed at each stage. For this purpose, a feedback neural network with LSTM layers has been used. The general structure of the proposed method uses pre-processing on the received radar signals and extracts symmetry characteristics, Doppler spread and AGCD from it to contain the information for separating the target and chaff. Then, to remove the effect of noise on the features. Finally, these features are used to correctly distinguish the target from the chaff in a feed-forward neural network with fully connected layers. At the end, the effectiveness of this method is compared to the previous methods. It can be seen that the performance of the proposed system has made a significant improvement in accuracy of detection.

One of the areas in which businesses use artificial intelligence techniques is the analysis and prediction of customer behavior. It is important for a business to predict the future behavior of its customers. In this paper, a customer behavior model using wild horse optimization algorithm is proposed. In the first step, K-Means algorithm is used to classify based on the features extracted from the time series, and then in the second step, wild horse optimization algorithm is used to estimate customer behavior. Three dataset including, the grocery store dataset, the household appliances dataset, and the supermarket dataset are used in the simulation. The best clusters count for the grocery store dataset, the household appliances dataset, and the supermarket dataset are obtained 5, 4, and 4, respectively. The simulation results indicate that this proposed method is obtained the lowest prediction error in three simulated datasets and is superior to other counterparts.

Cloud computing is a distributed environment for providing services over the Internet. Load balancing of computing resources has emerged as a crucial element in this industry as a result of the expanding use of cloud computing and the expectations of customers to receive more services and better outcomes. The workload and system behavior of cloud computing are quite dynamic. And this can cause the resources in the data center to be overloaded. Ultimately, a load imbalance in some data center resources could result in increased energy use, decreased performance, and resource waste. Response time, expense, throughput, performance, and resource usage are among the quality of service indicators that load balancing can enhance. In this article, we analyze and evaluate scheduling and resource allocation methods with a view to load balancing, review the most recent approaches, and give a classification of these algorithms. Also, several significant problems and difficulties with cloud load balancing will be discussed in an upcoming study to create new algorithms.

The ever-increasing development of telecommunications has made secure transmission one of the most important issues today. Using steganography keeps information away from unauthorized people, and the information is hidden inside the original file without harming the marks. Since there is a high hiding capacity in the image, the use of image steganography is much more common than other steganography methods. In this article, the wavelet transform steganography technique is used, and the comparison results of the images before and after applying steganography, as well as the histogram output, show that this method benefits from high resistance, as well as the SNR of this method compared to other methods. The reviewed results show the superiority of this steganography method.

Recently, with the advent of cloud computing technology and the growth of cell phone applications, the technology has been introduced as a solution for use in smart phones. Combining cloud computing and smart phones copes with such obstacles as performance including battery life time, storage capacity and bandwidth, environment as scalability and availability, and security like reliability and accessibility. Today, patient information collection processes require extensive analysis on the collected input data. In addition, this process has lots of errors, it is affordable and increases the time needed for access to the data. These conditions constrain effective monitoring and diagnostic capabilities by the hospitals. Unlike previous methods, data can be collected by devices such as Bluetooth and stored on a server in hospital. In this paper, a comprehensive research involves collecting data from patients and sending this data to a smart phone. Therefore, two algorithms were proposed for a list of physicians with the highest priority. Priority parameters are received by the hospitals server. These priorities include the initial weight, the weight indicating how busy the physician is, and the relationship between physician and patient's disease. Android application was simulated by Eclipse software. The application includes a diagnostic system that receives patient data and compared it with the reference table. If the patient's condition is not good, a short message is sent using two mentioned algorithms.