One of the main issues in the controlling of aircraft in difficult terrain during wartime is to ensure normal movement, but also to fulfill the requirements of evading enemy control. This paper proposes an improved ant swarm algorithm that makes it possible to pre-determine and optimize the trajectory of aircraft in such areas. When applying this method, a special parameter is included in the probability of choosing a movement trajectory – the height of the terrain above sea level, so that each ant does not enter territory controlled by the enemy. Using a 2D-H digital elevation map, the rectangular area under study is divided into 90 m × 90 m squares. To take into account the variability of the terrain, the heuristic function of the ant swarm algorithm takes into account the parameters of distance, height and smooth surface. Additionally, to reduce the number of iterations and computations, the ants are divided in half by number and released from the start and end points simultaneously. As a result, it allows you to choose the shortest and minimum trajectory among various calculated trajectories. To verify the effectiveness of the proposed scheme, a number of computational experiments were conducted. Experimental results on various simulated and real terrain maps show that this algorithm can be used to select an initial reference trajectory in difficult terrain.
{"title":"DETERMINATION OF THE OPTIMAL TRAJECTORY OF THE MOVEMENT OF AIRCRAFT IN AREAS WITH COMPLEX TERRAIN UNDER THE CONTROL OF THE ENEMY","authors":"Nadir Aghayev, Namig Kalbiyev, Sabina Aghazade","doi":"10.25045/jpit.v15.i1.04","DOIUrl":"https://doi.org/10.25045/jpit.v15.i1.04","url":null,"abstract":"One of the main issues in the controlling of aircraft in difficult terrain during wartime is to ensure normal movement, but also to fulfill the requirements of evading enemy control. This paper proposes an improved ant swarm algorithm that makes it possible to pre-determine and optimize the trajectory of aircraft in such areas. When applying this method, a special parameter is included in the probability of choosing a movement trajectory – the height of the terrain above sea level, so that each ant does not enter territory controlled by the enemy. Using a 2D-H digital elevation map, the rectangular area under study is divided into 90 m × 90 m squares. To take into account the variability of the terrain, the heuristic function of the ant swarm algorithm takes into account the parameters of distance, height and smooth surface. Additionally, to reduce the number of iterations and computations, the ants are divided in half by number and released from the start and end points simultaneously. As a result, it allows you to choose the shortest and minimum trajectory among various calculated trajectories. To verify the effectiveness of the proposed scheme, a number of computational experiments were conducted. Experimental results on various simulated and real terrain maps show that this algorithm can be used to select an initial reference trajectory in difficult terrain.","PeriodicalId":419916,"journal":{"name":"Problems of Information Technology","volume":"76 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140494390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Forecasting non-scheduled passenger air transportation demand is essential for effective operational planning and decision-making. In this abstract, we explore the use of Gaussian Support Vector Machines (SVM) methods in forecasting nonscheduled passenger air transportation processes. SVM is a type of supervised machine learning algorithm that can be applied to various domains, including nonscheduled passenger air transportation. In classification and regression tasks, SVMs are considered especially useful. SVMs can be used to forecast passenger demand for specific routes or flights. By analysing historical data, including factors such as time of day, day of the week, etc., SVMs can help airlines estimate future passenger demand. This method is crucial for optimising ticket pricing and managing seat inventory. This research proposes the implementation of different Gaussian SVM methods for the forecasting of non-scheduled passenger air transportation.
{"title":"SUPPORT VECTOR MACHINES FOR FORECASTING NON-SCHEDULED PASSENGER AIR TRANSPORTATION","authors":"Nadir Aghayev, Dashqin Nazarli","doi":"10.25045/jpit.v15.i1.01","DOIUrl":"https://doi.org/10.25045/jpit.v15.i1.01","url":null,"abstract":"Forecasting non-scheduled passenger air transportation demand is essential for effective operational planning and decision-making. In this abstract, we explore the use of Gaussian Support Vector Machines (SVM) methods in forecasting nonscheduled passenger air transportation processes. SVM is a type of supervised machine learning algorithm that can be applied to various domains, including nonscheduled passenger air transportation. In classification and regression tasks, SVMs are considered especially useful. SVMs can be used to forecast passenger demand for specific routes or flights. By analysing historical data, including factors such as time of day, day of the week, etc., SVMs can help airlines estimate future passenger demand. This method is crucial for optimising ticket pricing and managing seat inventory. This research proposes the implementation of different Gaussian SVM methods for the forecasting of non-scheduled passenger air transportation.","PeriodicalId":419916,"journal":{"name":"Problems of Information Technology","volume":"65 4-5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140494179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article examines the role of Big Data Analytics and Data Governance in the gaming industry. It shows how Big Data Analytics has changed game design and player interaction, focusing on trends and player preferences, especially in free-to-play games. The importance of Data Governance is stressed for handling data responsibly, focusing on challenges like data quality, security, and legal rules like GDPR. Examples from Ubisoft, SEGA, and Kolibri Games show these concepts in action. Metavibes is another example of research in field of Data Governance. The article also looks at how the gaming industry is dealing with data issues, including using strong policies and better security. It predicts future trends in AI and blockchain in gaming. The piece highlights the need for ethical practices, like protecting player privacy, as key for trust in the industry. The article points out the vital impact of these technologies in advancing and growing the gaming world.
本文探讨了大数据分析和数据治理在游戏行业中的作用。文章介绍了大数据分析如何改变游戏设计和玩家互动，重点关注趋势和玩家偏好，尤其是在免费游戏中。报告强调了数据治理对于负责任地处理数据的重要性，重点关注数据质量、安全性和 GDPR 等法律规则等挑战。育碧（Ubisoft）、世嘉（SEGA）和 Kolibri Games 的实例展示了这些概念的实际应用。Metavibes 是数据治理领域的另一个研究实例。文章还探讨了游戏行业如何处理数据问题，包括使用强有力的政策和更好的安全性。文章预测了人工智能和区块链在游戏领域的未来趋势。文章强调了道德实践的必要性，如保护玩家隐私，这是行业信任的关键。文章指出了这些技术对游戏世界的进步和发展的重要影响。
{"title":"DATA GOVERNANCE IN GAMING INDUSTRY","authors":"Farid G. Hagverdiyev","doi":"10.25045/jpit.v15.i1.06","DOIUrl":"https://doi.org/10.25045/jpit.v15.i1.06","url":null,"abstract":"This article examines the role of Big Data Analytics and Data Governance in the gaming industry. It shows how Big Data Analytics has changed game design and player interaction, focusing on trends and player preferences, especially in free-to-play games. The importance of Data Governance is stressed for handling data responsibly, focusing on challenges like data quality, security, and legal rules like GDPR. Examples from Ubisoft, SEGA, and Kolibri Games show these concepts in action. Metavibes is another example of research in field of Data Governance. The article also looks at how the gaming industry is dealing with data issues, including using strong policies and better security. It predicts future trends in AI and blockchain in gaming. The piece highlights the need for ethical practices, like protecting player privacy, as key for trust in the industry. The article points out the vital impact of these technologies in advancing and growing the gaming world.","PeriodicalId":419916,"journal":{"name":"Problems of Information Technology","volume":"73 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140494185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Industrial control systems (ICS) form the basis of critical infrastructures, managing complex processes in various sectors of industry, energy, etc. With the increasing frequency and complexity of cyber threats, effective management of ICS cybersecurity risks is critical. This paper is devoted to the analysis of approaches used in the field of cybersecurity risk management of automated process control systems. The study examines the cybersecurity risks of ICS and the role of international standards in managing cybersecurity risks. The results of the analysis carried out in this paper can serve as information for the development of new reliable cybersecurity risk management systems for ICS.
{"title":"CYBERSECURITY RISKS MANAGEMENT OF INDUSTRIAL CONTROL SYSTEMS: A REVIEW","authors":"R. Shikhaliyev","doi":"10.25045/jpit.v15.i1.05","DOIUrl":"https://doi.org/10.25045/jpit.v15.i1.05","url":null,"abstract":"Industrial control systems (ICS) form the basis of critical infrastructures, managing complex processes in various sectors of industry, energy, etc. With the increasing frequency and complexity of cyber threats, effective management of ICS cybersecurity risks is critical. This paper is devoted to the analysis of approaches used in the field of cybersecurity risk management of automated process control systems. The study examines the cybersecurity risks of ICS and the role of international standards in managing cybersecurity risks. The results of the analysis carried out in this paper can serve as information for the development of new reliable cybersecurity risk management systems for ICS.","PeriodicalId":419916,"journal":{"name":"Problems of Information Technology","volume":"127 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140493969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, the amount of data created worldwide has grown exponentially. The increase in computational complexity when working with "Big data" leads to the need to develop new approaches for their clustering. The problem of massive data amounts clustering can be solved using parallel processing. Dividing the data into batches helps to perform clustering in a reasonable time. In this case, the reliability of the obtained result for each block will affect the performance of the entire dataset. The main idea of the proposed approach is to apply the k-medoids and k-means algorithms to parallel Big data clustering. The advantage of this hybrid approach is that it is based on the central object in the cluster and is less sensitive to outliers than k-means clustering. Experiments are conducted on real datasets, namely YearPredictionMSD and Phone Accelerometer. The proposed approach is compared with the k-means and MiniBatch k-means algorithms. Experimental results proved that the proposed parallel implementation of k-medoids with the k-means algorithm shows greater accuracy and works faster than the k-means algorithm.
{"title":"IMPROVED PARALLEL BIG DATA CLUSTERING BASED ON K-MEDOIDS AND K-MEANS ALGORITHMS","authors":"Rasim Alguliyev, R. Aliguliyev, L. Sukhostat","doi":"10.25045/jpit.v15.i1.03","DOIUrl":"https://doi.org/10.25045/jpit.v15.i1.03","url":null,"abstract":"In recent years, the amount of data created worldwide has grown exponentially. The increase in computational complexity when working with \"Big data\" leads to the need to develop new approaches for their clustering. The problem of massive data amounts clustering can be solved using parallel processing. Dividing the data into batches helps to perform clustering in a reasonable time. In this case, the reliability of the obtained result for each block will affect the performance of the entire dataset. The main idea of the proposed approach is to apply the k-medoids and k-means algorithms to parallel Big data clustering. The advantage of this hybrid approach is that it is based on the central object in the cluster and is less sensitive to outliers than k-means clustering. Experiments are conducted on real datasets, namely YearPredictionMSD and Phone Accelerometer. The proposed approach is compared with the k-means and MiniBatch k-means algorithms. Experimental results proved that the proposed parallel implementation of k-medoids with the k-means algorithm shows greater accuracy and works faster than the k-means algorithm.","PeriodicalId":419916,"journal":{"name":"Problems of Information Technology","volume":"74 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140493557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The dynamics and complexity of processes occurring in complex software systems, as well as the emergence of new types of malicious threats, further complicate the issues of ensuring software reliability. Despite the development of hundreds of models for increasing the reliability of software systems, this issue still remains relevant. Research shows that the use of neural networks in predicting the reliability of software systems allows one to obtain more accurate results. In this paper, to predict reliability, we used a neural network model with long short-term memory, which is a type of recurrent neural networks. Seven real-world software crash datasets were used to test the model's performance. The experiments were carried out in Python. Both parametric and nonparametric models were taken for comparison. The experimental results showed the practical significance of using the proposed model in predicting the reliability of software systems.
{"title":"PREDICTING THE RELIABILITY OF SOFTWARE SYSTEMS USING RECURRENT NEURAL NETWORKS: LSTM MODEL","authors":"T. Bayramova","doi":"10.25045/jpit.v15.i1.07","DOIUrl":"https://doi.org/10.25045/jpit.v15.i1.07","url":null,"abstract":"The dynamics and complexity of processes occurring in complex software systems, as well as the emergence of new types of malicious threats, further complicate the issues of ensuring software reliability. Despite the development of hundreds of models for increasing the reliability of software systems, this issue still remains relevant. Research shows that the use of neural networks in predicting the reliability of software systems allows one to obtain more accurate results. In this paper, to predict reliability, we used a neural network model with long short-term memory, which is a type of recurrent neural networks. Seven real-world software crash datasets were used to test the model's performance. The experiments were carried out in Python. Both parametric and nonparametric models were taken for comparison. The experimental results showed the practical significance of using the proposed model in predicting the reliability of software systems.","PeriodicalId":419916,"journal":{"name":"Problems of Information Technology","volume":"42 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140494030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study explores the use of artificial intelligence (AI) in e-government applications, focusing on the various phases of e-government expansion and advancement. The frameworks include providing information, enabling interaction, and facilitating transactions. The main source of improvement is the integration of AI into government services, enabling computer systems to learn, reason, and make human-like decisions. The use of generator AI is expected to result in more intelligent, precise, and efficient approaches, but it is essential for organizations to formulate plans that align with advancements and consequences of intelligent technology. The goal is to achieve development goals that enable the government to adopt smart generators in its applications.
{"title":"THE USE OF GENERATIVE ARTIFICIAL INTELLIGENCE FOR CUSTOMER SERVICES","authors":"Mohammad Ali AL Qudah, Leyla Muradkhanli","doi":"10.25045/jpit.v15.i1.02","DOIUrl":"https://doi.org/10.25045/jpit.v15.i1.02","url":null,"abstract":"This study explores the use of artificial intelligence (AI) in e-government applications, focusing on the various phases of e-government expansion and advancement. The frameworks include providing information, enabling interaction, and facilitating transactions. The main source of improvement is the integration of AI into government services, enabling computer systems to learn, reason, and make human-like decisions. The use of generator AI is expected to result in more intelligent, precise, and efficient approaches, but it is essential for organizations to formulate plans that align with advancements and consequences of intelligent technology. The goal is to achieve development goals that enable the government to adopt smart generators in its applications.","PeriodicalId":419916,"journal":{"name":"Problems of Information Technology","volume":"86 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140494593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The standardization procedure of the fifth generation communication has already been completed and global spread has launched. To maintain the competitive advantage of wireless communication, researchers conceptualize next-generation (6th generation, 6G) wireless communication systems aimed at founding the stratification of communication needs of the 2030s. This article highlights the most promising research areas in the recent literature on the overall trends of the 6G project to support this view. It discusses the development and analysis of 6G wireless communication technology, which is projected to be implemented in the near future. Networks based on 6G wireless technology seem to be the most promising and developing field in the field of wireless technology. The article indicates the emergence and development of 6G to lead to a new wave of developments in the field of the Internet of Things (IoT). It touches upon the services applied during the implementation of the previous generation (5th generation, 5G) technologies and the emerging problems. It also reviews the benefits and challenges associated with the development of 6G wireless communication, which is designed to provide a better communication system in the future and to get many new perspectives.
{"title":"6G TECHNOLOGY: PERSPECTIVES, PROBLEMS AND SOLUTIONS","authors":"Javid Aghashov, Tabriz Aghashov","doi":"10.25045/jpit.v14.i2.06","DOIUrl":"https://doi.org/10.25045/jpit.v14.i2.06","url":null,"abstract":"The standardization procedure of the fifth generation communication has already been completed and global spread has launched. To maintain the competitive advantage of wireless communication, researchers conceptualize next-generation (6th generation, 6G) wireless communication systems aimed at founding the stratification of communication needs of the 2030s. This article highlights the most promising research areas in the recent literature on the overall trends of the 6G project to support this view. It discusses the development and analysis of 6G wireless communication technology, which is projected to be implemented in the near future. Networks based on 6G wireless technology seem to be the most promising and developing field in the field of wireless technology. The article indicates the emergence and development of 6G to lead to a new wave of developments in the field of the Internet of Things (IoT). It touches upon the services applied during the implementation of the previous generation (5th generation, 5G) technologies and the emerging problems. It also reviews the benefits and challenges associated with the development of 6G wireless communication, which is designed to provide a better communication system in the future and to get many new perspectives.","PeriodicalId":419916,"journal":{"name":"Problems of Information Technology","volume":"198 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139361028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent decades, information technology has been integrated into industrial control systems (ICS). At the same time, there was a connection of the ICS to the Internet and a transition to cloud computing. Consequently, new vulnerabilities and threats to sophisticated cyberattacks have emerged that create significant risks for the cybersecurity of ICS, and the old security model based on the isolation of ICS is no longer able to ensure their cybersecurity. This situation makes it very important to intellectualize the cybersecurity of ICS, for which machine learning (ML) methods are used. The use of ML methods will make it possible to detect cybersecurity problems of ICS at an early stage, as well as eliminate their consequences without real damage. This paper discusses the issues of ICS intrusion detection based on ML methods. The work can help in the choice of ML methods for solving anomaly detection problems of ICS.
近几十年来，信息技术已融入工业控制系统（ICS）。与此同时，ICS 与互联网连接，并向云计算过渡。因此，出现了新的漏洞和复杂的网络攻击威胁，给 ICS 的网络安全带来了巨大风险，而基于 ICS 隔离的旧安全模式已无法确保其网络安全。在这种情况下，将 ICS 的网络安全智能化就变得非常重要，为此需要使用机器学习 (ML) 方法。使用 ML 方法可以及早发现 ICS 的网络安全问题，并在不造成实际损失的情况下消除其后果。本文讨论了基于 ML 方法的 ICS 入侵检测问题。这项工作有助于选择 ML 方法来解决 ICS 的异常检测问题。
{"title":"USING MACHINE LEARNING METHODS FOR INDUSTRIAL CONTROL SYSTEMS INTRUSION DETECTION","authors":"R. Shikhaliyev","doi":"10.25045/jpit.v14.i2.05","DOIUrl":"https://doi.org/10.25045/jpit.v14.i2.05","url":null,"abstract":"In recent decades, information technology has been integrated into industrial control systems (ICS). At the same time, there was a connection of the ICS to the Internet and a transition to cloud computing. Consequently, new vulnerabilities and threats to sophisticated cyberattacks have emerged that create significant risks for the cybersecurity of ICS, and the old security model based on the isolation of ICS is no longer able to ensure their cybersecurity. This situation makes it very important to intellectualize the cybersecurity of ICS, for which machine learning (ML) methods are used. The use of ML methods will make it possible to detect cybersecurity problems of ICS at an early stage, as well as eliminate their consequences without real damage. This paper discusses the issues of ICS intrusion detection based on ML methods. The work can help in the choice of ML methods for solving anomaly detection problems of ICS.","PeriodicalId":419916,"journal":{"name":"Problems of Information Technology","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139360932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research paper explores the prediction of solar energy radiation using various machine learning methods and neural networks. The results are presented based on the analysis of four different datasets obtained from solar stations. The study begins with an overview of solar energy in the context of contemporary challenges in the fields of energy and environmental sustainability, and reviews previous research related to the application of artificial intelligence in solar energy. The main contribution of the work lies in the analysis and comparison of diverse machine learning models and neural networks for predicting solar energy radiation. The results are compared considering accuracy metrics (RMSE - Root Mean Squared Error, MAE - Mean Absolute Error, MRE - Mean Relative Error) and execution times for each model. Each model is evaluated on four datasets with different characteristics.
{"title":"COMPARATIVE ANALYSIS OF MODELS FOR SOLAR STATION OUTPUT PREDICTION","authors":"Javad Najafli","doi":"10.25045/jpit.v14.i2.04","DOIUrl":"https://doi.org/10.25045/jpit.v14.i2.04","url":null,"abstract":"This research paper explores the prediction of solar energy radiation using various machine learning methods and neural networks. The results are presented based on the analysis of four different datasets obtained from solar stations. The study begins with an overview of solar energy in the context of contemporary challenges in the fields of energy and environmental sustainability, and reviews previous research related to the application of artificial intelligence in solar energy. The main contribution of the work lies in the analysis and comparison of diverse machine learning models and neural networks for predicting solar energy radiation. The results are compared considering accuracy metrics (RMSE - Root Mean Squared Error, MAE - Mean Absolute Error, MRE - Mean Relative Error) and execution times for each model. Each model is evaluated on four datasets with different characteristics.","PeriodicalId":419916,"journal":{"name":"Problems of Information Technology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139361088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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