Pub Date : 2023-01-01DOI: 10.18178/jaai.2023.1.2.96-102
Syed Adnan Jawaid
Artificial Intelligence has transformed the cyber security industry by enabling organizations to systematize and enlarge outdated safety procedures. AI can provide more effective threat detection and response capabilities, enhance vulnerability management, and improve compliance and governance. AI technologies such as machine learning, natural language processing, behavioral analytics, and deep learning can enhance cyber security defenses and protect against a wide range of cyber threats, including malware, phishing attacks, and insider threats. Theoretical underpinnings of AI in cyber security, such as machine learning, natural language processing, behavioral analytics, and deep learning, are discussed. The advantages of using AI in cyber security are discussed including speed and accuracy, continuous learning and adaptation, and efficiency and scalability. It's important to note that AI is not a silver bullet for cyber security and should be used in conjunction with other security measures to provide a comprehensive defense strategy. AI has transformed the way cyber security operates in today's digital age. By analyzing vast amounts of data quickly and accurately it has become a valuable tool for organizations looking to protect their assets from cyber threats.
{"title":"Artificial Intelligence with Respect to Cyber Security","authors":"Syed Adnan Jawaid","doi":"10.18178/jaai.2023.1.2.96-102","DOIUrl":"https://doi.org/10.18178/jaai.2023.1.2.96-102","url":null,"abstract":"Artificial Intelligence has transformed the cyber security industry by enabling organizations to systematize and enlarge outdated safety procedures. AI can provide more effective threat detection and response capabilities, enhance vulnerability management, and improve compliance and governance. AI technologies such as machine learning, natural language processing, behavioral analytics, and deep learning can enhance cyber security defenses and protect against a wide range of cyber threats, including malware, phishing attacks, and insider threats. Theoretical underpinnings of AI in cyber security, such as machine learning, natural language processing, behavioral analytics, and deep learning, are discussed. The advantages of using AI in cyber security are discussed including speed and accuracy, continuous learning and adaptation, and efficiency and scalability. It's important to note that AI is not a silver bullet for cyber security and should be used in conjunction with other security measures to provide a comprehensive defense strategy. AI has transformed the way cyber security operates in today's digital age. By analyzing vast amounts of data quickly and accurately it has become a valuable tool for organizations looking to protect their assets from cyber threats.","PeriodicalId":163795,"journal":{"name":"Journal of Advances in Artificial Intelligence","volume":"278 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135699467","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}
Pub Date : 2023-01-01DOI: 10.18178/jaai.2023.1.2.103-116
A. F. Pinheiro, F. B. Lima-Neto
This article presents the domain engineering process carried out to obtain the requirements for the implementation of an Artificial Intelligence (AI) compliance framework aimed at the public sector. Owing to the current competitive and fast economy, which generates huge demand for increasingly efficient, reliable, and transparent intelligent systems, decision-support architectures should also be developed under strong restrictions of cost and time. Such a context requires adequate structures, processes, and technologies for coping with the complexity of building such intelligent systems. Currently, many public organizations have adopted applications for process automation, with the aim of refraining from repetitive work and producing more efficient results. However, what is not so often observed is the development of intelligent engines to support complex public decision-making. Possible explanations are the plethora of available data sources and the number of legal norms to be abided by. Moreover, it is important to highlight the need to incorporate transparency, auditability, reusability, and flexibility into such systems. Thus, they can be safely utilized in various analogous situations, reducing the need to develop new applications from scratch. An architecture suitable for supporting public decision-making with so many features and increasingly unstructured data, as well as abundant regulation, needs well-crafted formal specifications. This article aims to analyze three existing frameworks and carry out domain engineering studies in three cases to produce some guidance for future public applications and services based on AI. Next, we provide a conceptual preliminary architectural definition for the public sector. The proposed architecture targets were identified in the three cases studied, namely, frequent tasks of process mining requirements, detection of anomalies, and extraction of rules and public policies for helping public servants. All these aim at expedient AI development for public decision-making.
{"title":"Use of Domain Engineering in Hyperautomation Applied to Decision Making in Government","authors":"A. F. Pinheiro, F. B. Lima-Neto","doi":"10.18178/jaai.2023.1.2.103-116","DOIUrl":"https://doi.org/10.18178/jaai.2023.1.2.103-116","url":null,"abstract":"This article presents the domain engineering process carried out to obtain the requirements for the implementation of an Artificial Intelligence (AI) compliance framework aimed at the public sector. Owing to the current competitive and fast economy, which generates huge demand for increasingly efficient, reliable, and transparent intelligent systems, decision-support architectures should also be developed under strong restrictions of cost and time. Such a context requires adequate structures, processes, and technologies for coping with the complexity of building such intelligent systems. Currently, many public organizations have adopted applications for process automation, with the aim of refraining from repetitive work and producing more efficient results. However, what is not so often observed is the development of intelligent engines to support complex public decision-making. Possible explanations are the plethora of available data sources and the number of legal norms to be abided by. Moreover, it is important to highlight the need to incorporate transparency, auditability, reusability, and flexibility into such systems. Thus, they can be safely utilized in various analogous situations, reducing the need to develop new applications from scratch. An architecture suitable for supporting public decision-making with so many features and increasingly unstructured data, as well as abundant regulation, needs well-crafted formal specifications. This article aims to analyze three existing frameworks and carry out domain engineering studies in three cases to produce some guidance for future public applications and services based on AI. Next, we provide a conceptual preliminary architectural definition for the public sector. The proposed architecture targets were identified in the three cases studied, namely, frequent tasks of process mining requirements, detection of anomalies, and extraction of rules and public policies for helping public servants. All these aim at expedient AI development for public decision-making.","PeriodicalId":163795,"journal":{"name":"Journal of Advances in Artificial Intelligence","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135700855","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}
Pub Date : 2023-01-01DOI: 10.18178/jaai.2023.1.2.117-122
Sankalp Chenna
AI and Blockchain are two of the most promising technologies of the 21st century. They both have the potential to revolutionize various industries and bring about significant changes in the way we live and work. However, for AI and Blockchain to reach their full potential, they must be implemented in a trustworthy and secure manner. This is where the combination of AI and Blockchain comes in. By using Blockchain technology to secure and validate the data used to train AI systems, we can ensure that these systems are trustworthy and reliable. Additionally, by using AI to secure and validate Blockchain transactions, we can ensure that these transactions are safe and secure. Together, AI and Blockchain have the potential to create intelligent systems that are both trustworthy and secure. The integration of AI and Blockchain can also enable new use cases such as decentralized autonomous organizations (DAOs) which can take decisions autonomously by using AI, and Smart contracts which can automatically execute the terms of a contract when certain conditions are met. This can bring about improvements in transparency, immutability, and accountability in various industries such as finance, healthcare, and supply chain management. However, the integration of AI and Blockchain also raises important ethical and legal considerations, such as privacy, bias, and accountability. Therefore, it is crucial to continue researching and developing best practices for the implementation of AI and Blockchain in order to ensure their responsible and effective use.
{"title":"AI and Blockchain: Towards Trustworthy and Secure Intelligent Systems","authors":"Sankalp Chenna","doi":"10.18178/jaai.2023.1.2.117-122","DOIUrl":"https://doi.org/10.18178/jaai.2023.1.2.117-122","url":null,"abstract":"AI and Blockchain are two of the most promising technologies of the 21st century. They both have the potential to revolutionize various industries and bring about significant changes in the way we live and work. However, for AI and Blockchain to reach their full potential, they must be implemented in a trustworthy and secure manner. This is where the combination of AI and Blockchain comes in. By using Blockchain technology to secure and validate the data used to train AI systems, we can ensure that these systems are trustworthy and reliable. Additionally, by using AI to secure and validate Blockchain transactions, we can ensure that these transactions are safe and secure. Together, AI and Blockchain have the potential to create intelligent systems that are both trustworthy and secure. The integration of AI and Blockchain can also enable new use cases such as decentralized autonomous organizations (DAOs) which can take decisions autonomously by using AI, and Smart contracts which can automatically execute the terms of a contract when certain conditions are met. This can bring about improvements in transparency, immutability, and accountability in various industries such as finance, healthcare, and supply chain management. However, the integration of AI and Blockchain also raises important ethical and legal considerations, such as privacy, bias, and accountability. Therefore, it is crucial to continue researching and developing best practices for the implementation of AI and Blockchain in order to ensure their responsible and effective use.","PeriodicalId":163795,"journal":{"name":"Journal of Advances in Artificial Intelligence","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135700852","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}
Pub Date : 2023-01-01DOI: 10.18178/jaai.2023.1.2.123-128
Mohd Naved
In this paper, we review the use of machine learning and artificial intelligence in various sectors, including the social and business ecosystem, supply chain management, financial management, marketing management, and performance management. We show that these technologies have the potential to significantly enhance the efficiency and effectiveness of these systems by enabling the analysis and interpretation of large datasets in real-time, and by automating tasks and processes. We also discuss opportunities for further research in these areas and suggest that the use of machine learning and artificial intelligence will continue to grow in importance in the coming years. In conclusion, the use of machine learning and artificial intelligence has the potential to significantly enhance the efficiency and effectiveness of various sectors, and further research is needed to fully understand the potential of these technologies.
{"title":"A Review of the Use of Machine Learning and Artificial Intelligence in Various Sectors","authors":"Mohd Naved","doi":"10.18178/jaai.2023.1.2.123-128","DOIUrl":"https://doi.org/10.18178/jaai.2023.1.2.123-128","url":null,"abstract":"In this paper, we review the use of machine learning and artificial intelligence in various sectors, including the social and business ecosystem, supply chain management, financial management, marketing management, and performance management. We show that these technologies have the potential to significantly enhance the efficiency and effectiveness of these systems by enabling the analysis and interpretation of large datasets in real-time, and by automating tasks and processes. We also discuss opportunities for further research in these areas and suggest that the use of machine learning and artificial intelligence will continue to grow in importance in the coming years. In conclusion, the use of machine learning and artificial intelligence has the potential to significantly enhance the efficiency and effectiveness of various sectors, and further research is needed to fully understand the potential of these technologies.","PeriodicalId":163795,"journal":{"name":"Journal of Advances in Artificial Intelligence","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135700856","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}
Pub Date : 2023-01-01DOI: 10.18178/jaai.2023.1.2.67-95
Nwosu Obinnaya Chikezie Victor
Any building project's success depends on its planning. Due to the complexity of the construction business and the many aspects to consider, including money, supplies, labor, and timelines, this process may be difficult. AI can speed up and improve planning. This study examines the use of AI for building project planning. The research starts by examining building project planning and its problems. Next, the article covers machine learning, natural language processing, and computer vision AI technologies for building project planning. This study examines AI in building project planning. AI might revolutionize construction by improving project efficiency, accuracy, and performance. The study discusses predictive analytics, machine learning, and natural language processing for building project planning. The difficulties and prospects of AI in building project planning are also examined. The study finishes by examining how AI might be used to improve construction project planning and management. The article assesses AI's potential in building project planning and advises practitioners on how to use it. The study article then examines AI-planned construction projects. These examples demonstrate how AI may speed up construction, reduce waste, and streamline projects. Finally, the article discusses the merits and downsides of utilizing AI to plan building projects and suggests further research. This study report shows how important AI is for planning construction projects and how these technologies can be used to improve the performance of construction projects.
{"title":"The Application of Artificial Intelligence for Construction Project Planning","authors":"Nwosu Obinnaya Chikezie Victor","doi":"10.18178/jaai.2023.1.2.67-95","DOIUrl":"https://doi.org/10.18178/jaai.2023.1.2.67-95","url":null,"abstract":"Any building project's success depends on its planning. Due to the complexity of the construction business and the many aspects to consider, including money, supplies, labor, and timelines, this process may be difficult. AI can speed up and improve planning. This study examines the use of AI for building project planning. The research starts by examining building project planning and its problems. Next, the article covers machine learning, natural language processing, and computer vision AI technologies for building project planning. This study examines AI in building project planning. AI might revolutionize construction by improving project efficiency, accuracy, and performance. The study discusses predictive analytics, machine learning, and natural language processing for building project planning. The difficulties and prospects of AI in building project planning are also examined. The study finishes by examining how AI might be used to improve construction project planning and management. The article assesses AI's potential in building project planning and advises practitioners on how to use it. The study article then examines AI-planned construction projects. These examples demonstrate how AI may speed up construction, reduce waste, and streamline projects. Finally, the article discusses the merits and downsides of utilizing AI to plan building projects and suggests further research. This study report shows how important AI is for planning construction projects and how these technologies can be used to improve the performance of construction projects.","PeriodicalId":163795,"journal":{"name":"Journal of Advances in Artificial Intelligence","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135699471","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}
Pub Date : 1900-01-01DOI: 10.18178/jaai.2023.1.1.20-44
{"title":"Exploring the Use of AI in Sustainable Sourcing: Insights into the Impact and Potential of Artificial Intelligence","authors":"","doi":"10.18178/jaai.2023.1.1.20-44","DOIUrl":"https://doi.org/10.18178/jaai.2023.1.1.20-44","url":null,"abstract":"","PeriodicalId":163795,"journal":{"name":"Journal of Advances in Artificial Intelligence","volume":"366 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115784382","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}
Pub Date : 1900-01-01DOI: 10.18178/jaai.2023.1.1.45-48
{"title":"The Intersection of Artificial Intelligence and Business: A Study of Impact and Implications","authors":"","doi":"10.18178/jaai.2023.1.1.45-48","DOIUrl":"https://doi.org/10.18178/jaai.2023.1.1.45-48","url":null,"abstract":"","PeriodicalId":163795,"journal":{"name":"Journal of Advances in Artificial Intelligence","volume":"180 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121087161","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}
Pub Date : 1900-01-01DOI: 10.18178/jaai.2023.1.1.57-66
{"title":"AI and Its Application on Human Health","authors":"","doi":"10.18178/jaai.2023.1.1.57-66","DOIUrl":"https://doi.org/10.18178/jaai.2023.1.1.57-66","url":null,"abstract":"","PeriodicalId":163795,"journal":{"name":"Journal of Advances in Artificial Intelligence","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124621525","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}
Pub Date : 1900-01-01DOI: 10.18178/jaai.2023.1.1.1-19
{"title":"An Active Structure‐Based Artificial General Intelligence","authors":"","doi":"10.18178/jaai.2023.1.1.1-19","DOIUrl":"https://doi.org/10.18178/jaai.2023.1.1.1-19","url":null,"abstract":"","PeriodicalId":163795,"journal":{"name":"Journal of Advances in Artificial Intelligence","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127816112","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：