https://milestoneresearch.in/JOURNALS/index.php/IJHCI <p>International Journal of Human Computations and Intelligence (IJHCI) <strong>[ISSN:</strong> 2583-5696] is an <strong>Open Access</strong>, computer science archival journal on engineering and technology. IJHCI invites researchers to submit novel and unpublished research and surveys. The journal includes computer science domains such as artificial intelligence (AI), machine learning (ML), intelligent communication, data processing, human computer interaction (HCI) systems and much more. IJHCI is indexed and abstracted in Google Scholar, Research Gate, ProQuest, COPE.</p> Milestone Research Foundation en-US International Journal of Human Computations & Intelligence 2583-5696 https://milestoneresearch.in/JOURNALS/index.php/IJHCI/article/view/162 <div><span lang="EN-IN">Honeypots are decoys in cybersecurity, where a system is set up to attract and monitor cyber intruders. These systems appear vulnerable but are isolated and monitored, emulating the entire real world, for example, databases or IOT devices. To gain insight into their tactics, attackers interact with these decoys. Security teams can fortify their defences by learning about these emerging threats. Honeypots are classified on the basis of interaction offered. A low-interaction honeypot will only record the most basic attacks. High-interaction honeypots, in contrast, allow attackers to be interacted with on a higher level, yielding more insight as to how they operate. By adopting this approach early, organizations can better understand how they might be targeted by potential attackers. Besides enabling the early detection of threats, they publish decoys that honeypots distract attackers away from actual systems. But they fail to catch all attacks, particularly those that do not engage the decoy. Honeypots must be kept current to remain effective against rapidly evolving threats.</span></div> Vishal Kumar Sawan Bhardwaj Pradeep Chouksey Praveen Sadotra Mayank Chopra Copyright (c) 2025 Vishal Kumar, Sawan Bhardwaj, Pradeep Chouksey, Praveen Sadotra, Mayank Chopra https://creativecommons.org/licenses/by-nc-nd/4.0 2025-02-05 2025-02-05 3 6 370 377 10.5281/zenodo.14811718 https://milestoneresearch.in/JOURNALS/index.php/IJHCI/article/view/163 <p>LaneMorph is a machine learning-powered system designed to optimize urban traffic management using IoT and real-time video processing. By dynamically adjusting road dividers based on traffic density, the system enhances lane utilization, reduces congestion, and prioritizes emergency vehicles. This paper details the architecture, implementation, and potential impact of LaneMorph in smart city infrastructure. Additionally, the system integrates various sensor technologies, predictive algorithms, and automation mechanisms to improve traffic flow efficiency and ensure road safety.</p> Lavanya N L Anvith Krishna N Arun Kumar V Savanvur Shrivatsa R S Udaya Kumar Shetty Copyright (c) 2025 Lavanya N L, Anvith Krishna N, Arun Kumar V Savanvur, Shrivatsa R S, Udaya Kumar Shetty https://creativecommons.org/licenses/by-nc-nd/4.0 2025-02-05 2025-02-05 3 6 378 385 10.5281/zenodo.14811747