Applied Science and Engineering Journal for Advanced Research

The Role of AI in Automating Cyber Incident Response: Challenges and Opportunity

Venkatesh Kodela — Tue, 29 Jul 2025 00:00:00 +0530

Cyber threats are becoming more common and more complex, therefore we need faster and smarter ways to respond to them. This study looked into the function of Artificial Intelligence (AI) in automating the response to cyber incidents, focusing on how well it works, what problems it might face, and what opportunities it might create. A mixed-methods approach was used, which included testing how well AI-based tools worked in fake cyber-attack situations and talking to cybersecurity experts. The results showed that AI tools cut down on detection and response times by a lot while still being quite accurate at finding and stopping threats. However, concerns regarding trust, explainability, and integration with legacy systems emerged as key barriers to adoption. The results imply that AI has the ability to change cybersecurity for the better, but it won't be successful unless systems that are clear and easy to understand are made that can work with human experience. These insights are very helpful for companies who want to use AI to improve their ability to respond to incidents.

Optimizing Makespan and Buffer Allocation for Enhanced Job Shop Scheduling Efficiency: A Hybrid Metaheuristic Approach

K Sathyasundari, P Gowthaman — Tue, 29 Jul 2025 00:00:00 +0530

In today’s highly competitive manufacturing landscape, optimizing job shop scheduling has become vital for maximizing operational efficiency and minimizing production delays. This study explores the dual challenge of makespan minimization and efficient buffer management within job shop environments. By integrating a hybrid metaheuristic approach combining Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), we propose an intelligent scheduling framework that not only reduces the overall makespan but also enhances the utilization of intermediate buffers across machines. The experimental results, validated through benchmark datasets and real-time shop floor simulations, demonstrate significant improvements in throughput, machine utilization, and flow consistency. Our approach outperforms traditional heuristics by dynamically adjusting buffer capacities and job sequencing based on system feedback, paving the way for more resilient and adaptive manufacturing operations.

Scenic Impressions and Lasting Experiences: A Study on the Impact of Destination Image on Tourist Satisfaction in the Nilgiris

K Muthukumar, S Saravanakumar — Tue, 29 Jul 2025 00:00:00 +0530

The image of a destination plays a pivotal role in shaping tourist satisfaction and influencing their choice of travel. This study explores the dynamic relationship between the perceived image of the Nilgiris – a scenic and culturally rich hill district in Tamil Nadu – and the level of satisfaction experienced by its visitors. With growing competition among tourist destinations, understanding what truly matters to travelers is crucial for sustainable tourism development.To uncover these insights, data was collected from 600 tourists visiting various parts of the Nilgiris, including Ooty, Coonoor, and Kotagiri. The study employed the Garret Ranking Technique, a robust statistical tool that enabled the prioritization of various factors influencing destination image such as natural beauty, hospitality, cleanliness, accessibility, local culture, food, and safety. Respondents were asked to rank these attributes based on their travel experience. The Garret score conversion allowed the identification of key dimensions most valued by tourists, thereby revealing the strongest contributors to their overall satisfaction. Findings highlight that natural scenery and pleasant climate ranked highest among tourist preferences, followed closely by local hospitality and cultural richness. On the other hand, aspects like infrastructure and traffic management were ranked lower, indicating areas needing improvement. The study underscores the importance of enhancing destination image holistically, as even one weak link can affect tourist perception and repeat visits.This research offers practical insights for tourism planners, local authorities, and hospitality stakeholders in the Nilgiris to strategically strengthen and promote destination elements that elevate tourist satisfaction and foster long-term loyalty.

Optimization of FDM Process Parameters for Minimizing Specific Wear Rate Using a GA- ANFIS Hybrid Model

Nitesh Pingal, Munish Gupta, Prem Sagar — Tue, 29 Jul 2025 00:00:00 +0530

This study investigates the optimization of tribological performance in Fused Deposition Modeling (FDM) fabricated components by focusing on the specific wear rate (SWR) of Polylactic Acid (PLA) specimens. A total of 30 samples were fabricated using a MakerBot Method X 3D printer following ASTM G99 standards, considering four key process parameters: nozzle temperature, infill density, layer height, and printing speed. Wear behavior was evaluated using a Pin-on-Disc apparatus under dry sliding conditions. To predict and minimize SWR, a hybrid GA-ANFIS (Genetic Algorithm–Adaptive Neuro-Fuzzy Inference System) model was employed. The ANFIS framework effectively captured nonlinear relationships among input variables, while GA optimized membership functions to improve prediction accuracy. Experimental results demonstrated that nozzle temperature and layer height had the most significant influence on SWR. The optimized parameter combination achieved a minimum SWR of 8.26 × 10⁻⁴ mm³/N·m, representing a 25.12% reduction compared to non-optimized settings. The proposed hybrid approach proved to be a robust tool for process parameter optimization, enabling enhanced wear resistance and mechanical integrity in FDM-printed parts.