Journal of Applied Artificial Intelligence

Artificial intelligence for enhancing resilience

Nitin Rane, Saurabh Choudhary , Jayesh Rane — Mon, 09 Sep 2024 00:00:00 +0000

In an increasingly complex and unpredictable world, resilience-the ability to withstand and recover from adverse conditions is essential across various sectors. This research paper investigates the transformative potential of artificial intelligence (AI) in enhancing resilience across multiple domains. We explore how AI technology can be utilized to develop resilient infrastructure, providing advanced predictive maintenance and real-time monitoring capabilities that ensure robustness and longevity. The study examines the role of AI in improving disaster response, offering rapid data analysis and decision-making support to enhance emergency management outcomes. In climate change, AI-driven strategies are assessed for their effectiveness in fostering climate resilience, including predictive modeling of extreme weather events and optimizing resource allocation. The paper also discusses AI applications in healthcare resilience, such as enhancing diagnostics, patient care, and operational efficiency during crises. Business continuity and crisis management are examined, highlighting AI's capability to anticipate disruptions and maintain operational stability. The paper emphasizes the importance of strengthening cybersecurity resilience using AI to detect and mitigate threats proactively. AI's role in enhancing community and social resilience is analysed, particularly in supporting vulnerable populations and fostering social cohesion. Additionally, we explored AI-powered solutions for urban resilience, focusing on smart cities and sustainable development. The study also covers AI's contributions to environmental and ecological resilience, resilient supply chain management, and resilience in the hospitality and tourism industry. Finally, we investigated AI's potential in fostering psychological resilience, providing personalized mental health support and stress management tools. Through these diverse applications, the paper underscores AI's critical role in building a resilient future.

A Comparative Analysis of FaceNet, VGGFace, and GhostFaceNets Face Recognition Algorithms For Potential Criminal Suspect Identification

Muhammad Indra Ardiawan, Gede Putra Kusuma Negarara — Mon, 09 Sep 2024 00:00:00 +0000

The escalating concerns surrounding criminal activities underscore the imperative for bolstered security measures to safeguard public welfare. Despite concerted efforts, the identification of suspects remains fraught with limitations, hindering the attainment of comprehensive individual profiles. Leveraging advancements in facial detection and identification technologies, this study assesses the efficacy of three prominent deep learning models—FaceNet, VGGFace, and GhostFaceNets—in the domain of facial recognition for suspect identification. Drawing upon data collected in 2023, the investigation scrutinizes FaceNet's intricate methodologies, including triplet loss optimization and Euclidean space mappings, yielding exceptional accuracy rates of 97.05% during validation and 97.4% during testing. Conversely, VGGFace, while displaying commendable accuracies, registers marginally lower accuracy metrics, standing at 97.05% and 96.1% during validation and testing, respectively. GhostFaceNets, integrating novel architectural components, exhibit diminished accuracy rates, signaling avenues for refinement. These empirical insights underscore FaceNet's prowess in furnishing robust and reliable facial recognition outcomes, while delineating the imperative for iterative enhancements in GhostFaceNets to foster their pragmatic applicability in security domains.

Acceptance of artificial intelligence: key factors, challenges, and implementation strategies

Nitin Rane, Saurabh P. Choudhary, Jayesh Rane — Mon, 09 Sep 2024 00:00:00 +0000

This research paper investigates the key factors influencing AI acceptance, focusing on elements such as technological readiness, perceived usefulness, and ease of use, along with the organizational and societal impacts. It identifies the significant obstacles to AI adoption, including ethical concerns, data privacy issues, and the potential for job displacement. The study also explores the importance of trust and transparency in promoting AI acceptance, highlighting the necessity for explainable AI (XAI) to build user confidence. Strategies for enhancing AI acceptance are examined, emphasizing the need for robust regulatory frameworks, ongoing education, and skill development to mitigate resistance and boost user engagement. The research stresses the importance of a user-centric approach in AI system design and implementation, taking into account end-user needs and concerns. Additionally, it underscores the value of collaboration between industry, academia, and policymakers in fostering an environment conducive to AI innovation and acceptance. By offering a thorough analysis of the factors affecting AI acceptance and the associated challenges, this paper provides valuable insights and actionable strategies for stakeholders aiming to navigate the complex landscape of AI integration effectively.

Secret key generation based Short Tandem Repeat DNA

Sadoon Hussein, Ahmed Sami Nori — Mon, 09 Sep 2024 00:00:00 +0000

In a daily basis, scientists seek to develop more advanced security solutions to be applied into IoT environment using in-hand technologies. Indeed, many approaches have been proposed to respond to this scarcity, and many other exertions have been spent. One promising consequence is DNA computing, which has significantly promising capabilities over traditional electronic computers. As biological characteristics have always been a source of inspiration for scientists in developing complex computing systems and technologies.
DNA is a molecule in organism`s cells. All organisms' DNA are similar in about 99.9%. In this paper, a new data encryption technique has been inspired by DNA characteristics in which the encryption key transmission is based on STR DNA fingerprint. The proposed technique will be applied in a healthcare environment. The STR-DNA based key generation and transmission technique will be used to encrypt the message before being transmitted over the IoT environment. The experimental results showed that the proposed technique has similar characteristics as the RSA algorithm added the feature of applying the mechanism on biological environments.