Arman Process Journal
Year:2023 | Volume:4 | Issue:1|Papers Count:6

In today's competitive world, information has emerged as one of the important production factors. As a result, the effort to extract information from data has attracted the attention of many people involved in the information industry and related fields. The large volume of data is constantly growing in all fields and the vast difference in data production process has increased the complexity of information management and extraction. Recently, several strategies and techniques have been used to collect, store, organize and efficiently manage existing data and achieve meaningful results, and data mining is one of the recent developments in the direction of data management technologies. The term data mining refers to the semi-automatic process of analyzing large databases and data warehouses in order to find useful and applicabale patterns. In this research, we are going to examine the operationalization process of data mining and do a practical analysis of this issue. In addition, we will research about the important applications and tools of this field.

Currently, the significant growth of the number of users of online services and distributed systems generate a large amount of database information at the level of data warehouses and big data. Data warehouses are large collections of business data that help organizations and businesses make more accurate and intelligent decisions. Big data is also a very large collection of data collected from multiple sources. These data can be the results of evaluating the performance of an organization or the interactions of its audience in social networks. Due to the increasing importance of big data functions and data warehouses, the issue of maintaining data security is the biggest threat these systems face. Basically, dealing with the category of information security and security of internet networks, data warehouses and services based on big data, requires special attention of the organization to the position of information security and all-round security of these systems, and this category should be considered at the macro level and from the perspective of benefits and benefits. it looked The existence of security weaknesses in these systems, the lack of proper training and justification of users regardless of their point of view regarding the position and importance of security, the absence of necessary instructions to prevent security defects, the absence of specific and codified policies in order to properly and timely deal with Security flaws will lead to issues that harm all users and the operational capabilities of these systems and actually expose the information infrastructure of organizations to serious damage and threats. In this article, we are going to research the security aspects of data warehouses and big data and examine effective and logical solutions in this area.

One of the most important problems in the world is the increase in costs in the field of health. One of the important research in recent years to reduce these costs is the prediction of diseases. Liver diseases are one of the most serious diseases in the world, because the liver plays a vital role in the human body, and any liver disorder causes serious and irreparable problems in the body. Most liver diseases do not show specific symptoms until advanced stages. Not having symptoms in the early stages may lead to the wrong diagnosis of the disease by many doctors, and this wrong diagnosis can lead to the wrong treatment and prescription of inappropriate medicine, and as a result, the creation of acute and long-term symptoms of this disease or other problems. Therefore, earlier and more accurate diagnosis of liver problems with the help of detailed analysis of the effective features of an automatic medical diagnosis system, for correct treatment and prevention of serious damage to this vital organ, seems necessary. For this purpose, it is necessary to use data mining techniques, machine learning and innovative algorithms to provide an intelligent model for early diagnosis of this disease. Therefore, the aim of this research is to comprehensively investigate liver diseases, methods of diagnosis and prediction of these diseases by machine learning techniques and meta-heuristic algorithms in terms of goals, limitations and capabilities in the field of medicine. The results show that random forest algorithm, fuzzy neural networks and support vector machine find approximate solutions faster than meta-initiative algorithms, and also usually get better results compared to deterministic algorithms. Also, among the datasets of liver diseases, the ILPD dataset is the most widely used in the diagnosis and prediction of liver diseases due to its easier access and the MATLAB tool due to its simplicity and comprehensibility.

Recently, due to the increasing use of smart communication devices, every day more companies and people are moving towards the production of mobile applications, so that their products can be used more. The Android operating system is one of the new technologies that is updated in a short time, it is upgraded and more features are quickly added to make it more efficient. The Android operating system is the first comprehensive and complete mobile operating system that has been published as an open source. Although the development of mobile applications based on the Android operating system, in addition to undeniable advantages, also brings challenges that were among the motivations and goals of this research. Therefore, according to the popularity and importance of different aspects of this operating system and inspired by the work done in this field, in this study we have tried to review the capabilities, functions, versions, challenges, perspectives and solutions to solve this problem and challenges in the Android operating system.

Nowadays, wireless networks can be used in interaction with many novel applications such as, traffic monitoring, battlefield survey, monitoring based on sensors and distribution networks. Wireless sensor networks are one of the important applications of cyber-physical systems in which information is collected from the surrounding environment by using a large number of sensor nodes in a wide environment. A wireless network can be a community of sensor networks with computing and information devices. These systems can intelligently monitor the state of the surrounding environment and react with an intelligent response to the events of the surrounding environment. Due to the technological limitations and environmental influences, the sensor data collected by these systems are inherently noisy and may have may issues and false alarms, and it is highly desirable to sift meaningful information from a large set of noisy data. Target tracking in these systems is a critical problem that has occupied many researches and in recent years, with the realization of the possibility of building wireless sensor networks, the use of these networks in different environments for tracking moving targets has also been proposed, and one of the most important issues in this Networks are considered. In this research, we investigate the approaches of tracking targets on wireless sensor network. We present the issues and problems of tracking, then we continue to review the common tracking methods in these networks and related qualitative parameters.

Today, distributed network management frameworks are used to provide, operate, maintain and secure network infrastructures. Basically, the correct management of distribution networks requires a wide range of activities, methods, routines and the use of executive, operational and maintenance tools of computer systems. When the issue of security attacks lurking in the network is raised, the importance of comprehensive management and monitoring of the data exchanged in the network will be twofold. In order for the managers of such networks to be able to regularly monitor the system logs and its operational framework and to identify hardware, software and security attacks, an integrated management framework is needed to undertake the comprehensive and systematic management. An approach that analyzes security events, collects and categorizes system logs and enables de-centralized network management. The comprehensive and integrated management approach of distribution networks uses a set of communication hardware and user interface software and accessories as an integrated system to control network tools and equipments. The perspective of integrated management of distribution networks makes network resources available to users in an efficient and fast method. Therefore, network optimization is guaranteed using error analysis and performance management. By using comprehensive management frameworks, the best suitable tools for managing, monitoring and controlling the network can be used to respond to the challenges in the network if needed. In this article, we intend to research and examine the approach of achieving integrated management approach for distribution networks and its dimensions.