Government Cloud Security: Leveraging AI to Protect Sensitive Data and Critical Infrastructure (Published)
Government agencies worldwide are increasingly migrating sensitive data and critical infrastructure to cloud environments. While this shift offers numerous benefits including enhanced operational efficiency, cost reduction, and improved service delivery, it also introduces significant security challenges that traditional measures struggle to address effectively. This article explores how artificial intelligence (AI) can revolutionize government cloud security by enabling enhanced threat detection, vulnerability management, and automated incident response capabilities. Through implementing AI-driven security solutions, government agencies can better protect sensitive information, ensure the continuity of critical services, and maintain public trust in digital government operations. The article examines current implementations of AI in government cybersecurity, explores future developments in predictive threat analysis and autonomous security operations, and addresses key implementation challenges related to compliance, explainability, and workforce development.
Keywords: Artificial Intelligence, cloud security, government cybersecurity, human-machine teaming, predictive threat analysis
Intelligent Automation Ecosystems: AI and Cloud Computing Synergies in E-Commerce Platform Engineering (Published)
This article examines the transformative impact of intelligent automation on e-commerce platform engineering, focusing specifically on the integration of artificial intelligence with cloud computing infrastructure. The article investigates how these technologies collectively enhance operational efficiency while reducing costs across customer support, order management, and marketing functions within digital enterprises. By analyzing both theoretical frameworks and practical implementations, this study identifies serverless computing as a critical enabler that significantly lowers barriers to adoption. The article demonstrates how automated analytics systems deliver personalized customer experiences while simultaneously streamlining backend operations. The article further explores emerging trajectories in machine learning applications and predictive analytics that are reshaping competitive dynamics in digital commerce. This article contributes to the understanding of how intelligent automation technologies can be strategically deployed to create sustainable competitive advantages for e-commerce businesses in an increasingly digital marketplace.
Keywords: Artificial Intelligence, Cloud Computing, E-commerce automation, Predictive Analytics, serverless architecture
Interoperability within Healthcare Systems through FHIR, Artificial Intelligence and Cloud Integration (Published)
One of the main challenges for healthcare interoperability is defining common standards for the structured content of healthcare data and the transport of this data between different systems. The purpose of this paper is to enable the exchange of these data, with a focus on the FHIR (Fast Healthcare Interoperability Resources) protocol which created the HL7 (Health Level Seven International) standard, a framework that has become widely adopted by all stakeholders around the world to determine the content of this data and the way they want to share it and to identify the potential of the integration of Artificial Intelligence with the HL7 FHIR standart. The integration of this data was one of the main questions raised in the project “Integration of AI in Advancing Interdisciplinary Research at the University of Tirana”.
Keywords: Artificial Intelligence, FHIR, HL7, data integration, interdisciplinary research, interoperability
The Integration of Artificial Intelligence and Blockchain in Logistics and Facility Management: A Case Study on Sea Cargo Handling Services (Published)
The logistics and facility management industries are currently experiencing a paradigm shift driven by the rapid advancement of Artificial Intelligence (AI) and blockchain technology. These innovative technologies are poised to revolutionize various aspects of logistics operations, particularly in the context of sea cargo handling, where the demands for efficiency, security, and transparency are increasingly critical. As global trade continues to expand and the complexity of supply chains grows, the need for more advanced and reliable systems becomes ever more pressing. This research paper delves into the transformative potential of integrating AI and blockchain technologies within logistics and facility management, with a focused lens on sea cargo handling services. Sea cargo handling, a vital component of international trade, has traditionally relied on manual processes and centralized systems that often struggle with inefficiencies, delays, and security vulnerabilities. The integration of AI and blockchain offers a robust solution to these challenges by enhancing operational efficiency through automation and predictive analytics, while simultaneously ensuring the security and transparency of transactions through the decentralized and immutable nature of blockchain. AI’s ability to process vast amounts of data in real-time allows for improved decision-making, predictive maintenance, and the optimization of resource allocation. Blockchain, on the other hand, provides a secure, tamper-proof ledger that ensures the authenticity and integrity of cargo movements, from origin to destination, reducing the risk of fraud and enhancing compliance with international regulations. This paper presents a comprehensive exploration of the integration of AI and blockchain in the logistics sector, specifically within the realm of sea cargo handling. The study begins with an extensive literature review that examines the current state of AI and blockchain technologies in logistics and facility management, highlighting both the potential benefits and the existing challenges. The review covers various applications of AI, such as machine learning algorithms for demand forecasting and route optimization, and explores how blockchain can be used to create transparent and secure supply chains. It also addresses the synergy between these technologies, proposing a combined approach that leverages the strengths of both AI and blockchain to create a more resilient and efficient logistics framework. Following the literature review, the paper outlines the methodologies employed to integrate AI and blockchain into sea cargo handling operations. This includes the development of AI models for optimizing cargo handling processes, predicting port congestion, and automating customs clearance procedures. The methodologies also cover the implementation of blockchain for tracking the provenance of goods, verifying transactions, and ensuring that all cargo movements are securely recorded and accessible to relevant stakeholders. The paper details the steps involved in deploying these technologies, from initial assessment and planning to the actual implementation and integration with existing systems.To provide a practical perspective, the paper includes a detailed case study of a major port that has successfully implemented AI and blockchain technologies in its sea cargo handling operations. This case study illustrates the tangible benefits of this integration, such as significant improvements in operational efficiency, enhanced security measures, and cost reductions. It also highlights the challenges encountered during the implementation process, such as the need for extensive training and the complexities of integrating new technologies with legacy systems. The case study serves as a valuable example for other ports and logistics companies considering similar technological upgrades, offering insights into the best practices and potential pitfalls.This research paper underscores the transformative impact of AI and blockchain on logistics and facility management, particularly in sea cargo handling services. By integrating these technologies, logistics operations can achieve higher levels of efficiency, security, and transparency, ultimately leading to more reliable and cost-effective supply chains. However, the paper also cautions that the successful implementation of these technologies requires careful planning, a clear understanding of the specific operational context, and a willingness to invest in the necessary infrastructure and training. As the logistics industry continues to evolve, the integration of AI and blockchain will likely become a standard practice, paving the way for a more advanced and secure global trade network.
Keywords: Artificial Intelligence, Blockchain, Integration, Logistics, facility management: sea cargo handling services
Artificial Intelligence (AI) Application in Process Safety Cumulative Risk Visualization for Petroleum Operations: Conceptual Framework (Published)
One of the key challenges in preventing major process safety accidents in an operating plant is the lack of an integrated system/model that brings together the risks posed by the deficiencies / deviations on the safety critical barriers, for operational decision making. Based on this context, a model/framework was developed for assessing and visualizing the accumulation of process safety risks arising from safety critical barriers impairments in petroleum facilities in Niger-Delta Nigeria. Based on the review of the model, the need for an intelligent web-based software was identified. An exploratory study was therefore undertaken through extensive literature review and focused group participants, to develop a conceptual framework for an intelligent web-based software for process safety cumulative risk visualization. The results from the study make it evident that the conceptual framework provides a novel approach in developing an intelligent web-based software using artificial intelligence (AI) techniques, for real time visualization of process safety cumulative risk picture.
Keywords: Artificial Intelligence, cumulative risk assessment, major accident prevention, petroleum operations, process safety
Prediction of Gold associated Mineral worth: An application of mathematically driven artificial neural network technique (Published)
The elemental composition of other associate minerals existing with gold is a significant asset that defines the amount of additional economic contribution that can be obtained from the gold tailings. The elemental composition is a needed factor in increasing the economic value of gold run-off and getting a clear estimation for the quantity of value-added elements in each tonne of gold sand scooped during the separation process. In this study, the artificial neural network (ANN) modeling technique was used to develop an economic worth prediction model for 10 gold-associated minerals. The developed models have a 1:7:10 architecture and were trained using the ANN Bayesian regularization training algorithm. According to the root mean square error values, the results revealed that the predicted values of the associated minerals are closer to the measured values. Also, the developed model prediction performance was found to be appropriate for the estimation of gold-associated mineral economic benefits based on the high coefficient of determination and variance account. The model performance evaluation results show that the developed ANN models are suitable for economic estimation of gold-associated mineral worth.
Keywords: Artificial Intelligence, Gold, Mining, Nigeria, machine learning algorithms, mineral economics