Applied Science and Engineering Journal for Advanced Research

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

Venkatesh Kodela — 2025-07-29

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 — 2025-07-29

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 — 2025-07-29

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 — 2025-07-29

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.

Design and Implementation of an Automated Patch Management and Compliance Framework for Institutional IT Systems

Kishore Thota — 2025-07-29

Delays in patching and uneven adherence to legal requirements make institutional IT systems more susceptible to security risks. The design and implementation of an automated patch management and compliance framework suited to institutional environments was the main emphasis of this project. The framework was implemented and tested in a simulated IT infrastructure with a variety of operating systems and device roles using a design science research methodology. According to the findings, there were notable gains in patch deployment success rates (96% vs. 78%), remediation time (3.2 vs. 14.5 hours), compliance (98% vs. 72%), and system downtime. The automated system was a strong and scalable paradigm for institutional IT governance since it also improved administrative efficiency and offered real-time compliance reports. These results imply that patch management automation improves cybersecurity and simplifies IT operations in both academic and business contexts.

Effect of Ammonia on the Formation of THMS in Drinking Water Chlorination - A Case Study

Md Serajuddin — 2025-07-29

In a water supply system total Trihalomethanes (THMs) content in drinking water may vary considerably depending on water quality and treatment conditions. Most urban water treatment plants generally use chlorine as disinfectant. The effect of various parameters on the formation of THMs has been widely studied around the world over the past few decades. Almost universally, it has been found that increasing any of these parameters tends to promote the formation of THMs—except for ammonia, which has a negative effect on the process. Surprisingly, this exception has not received the attention it deserves in THM research globally. Given the high concentration of ammonia in Dhaka's drinking water sources—particularly during the dry months—this study aimed to evaluate how ammonia affects the formation of THMs in water samples from the largest water treatment plant in Dhaka, Bangladesh. The water samples were tested for a wide range of parameters including pH, ammonia, UV₂₅₄, TOC, DOC and bromide following the standard methods of testing. THMs was measured by THM plus Method (Method:10132) using UV-VIS Spectrophotometer DR 6000(HACH, USA). A detailed quantitative study was conducted to examine how ammonia affects the formation of trihalomethanes (THMs) when water is chlorinated under varying conditions. Experiments were carried out using treated water from the supply system, which had a dissolved organic carbon (DOC) content of 6.0 mg/L. Chlorination was performed with a free chlorine residual of 0.89 mg/L and a total chlorine residual of 1.29 mg/L. Different doses of ammonia—0.0, 0.5, 1.0, 5.0, and 10.0 mg/L—were applied. The results showed that the presence of ammonia at various concentrations significantly reduced THM formation at the given chlorine levels, however it did not completely eliminate it. THMs formation decreased continuously with increasing ammonia concentration, and the decline is sharp during relatively low concentration of ammonia up to 3 mg N/L then remained near to flat slope after ammonia exceeded 3 mg N/ L. It is noticed that the formation of THMs significantly reduced with increasing ammonia concentration from 0 to 10 mg N/L in chlorinated drinking water. The suppression of THMs was prominent with increasing ammonia concentration from almost zero to 5 mg N/L. However, the formation of THMs remain low and constant after ammonia addition over 5 mg N /L. A general correlation for predicting THM formation based on ammonia concentration is presented, and its predictions align well with the observed results, although it is specific to this study. Further research using a more diverse dataset is recommended. In water supply systems like Dhaka, where significant amounts of ammonia and other organic pollutants are present in river water—along with the perceived risk of THM formation—comprehensive studies should be undertaken to determine how to manage or utilize ammonia effectively during water treatment. One potential approach could be the controlled use of ammonia to form chloramine, which can act as a disinfectant in the treatment process.

Advanced Process Parameter Optimization for Compressive Strength of FDM-Printed PETG Using GA-ANFIS

Sunil — 2025-07-29

This study investigates the optimization of process parameters to enhance the compressive strength of polyethylene terephthalate glycol (PETG) parts manufactured using Fused Deposition Modeling (FDM). Compression test specimens were fabricated following ASTM D695 standards, with nozzle temperature, infill density, layer height, and printing speed selected as the key input variables. A three-level face-centered central composite design (FCCD) was employed to systematically evaluate their individual and interactive effects on ultimate compressive strength (UCS). Experimental testing revealed that higher infill density and reduced layer height significantly improved compressive performance, with UCS reaching 106.25 MPa under baseline conditions. To further optimize results, a hybrid Genetic Algorithm–Adaptive Neuro-Fuzzy Inference System (GA-ANFIS) framework was implemented, enabling accurate prediction and intelligent optimization of compressive strength. The optimized parameters—224.25 °C nozzle temperature, 88% infill density, 0.15 mm layer height, and 55 mm/s print speed—yielded a maximum UCS of 148.53 MPa, representing a 39.78% improvement over baseline results. The findings demonstrate that intelligent hybrid optimization provides a robust approach for tailoring FDM process parameters, thereby enhancing the structural reliability of PETG components for engineering applications.

Predictive SLA Management: Leveraging Machine Learning to Improve Upstream Feed Reliability

Pathik Bavadiya — 2025-07-29

In order to improve the reliability of upstream feeds, this study investigates the use of machine learning approaches for the management of anticipatory Service Level Agreements (SLAs). Random Forest, Support Vector Machine, and Gradient Boosting Machine (GBM) were the three models that were constructed and assessed with the help of historical service level agreement (SLA) and operational data. The GBM model displayed exceptional performance, with an accuracy of 94.1%, which enabled it to accurately predict service level agreement (SLA) breaches and carry out proactive interventions. After using predictive service level agreement management, there was a considerable decrease in the number of feed disruptions (26.7%), the average duration of interruptions (34.2%), and the total amount of downtime (51.8%). In addition, the operations team provided qualitative input that emphasized improvements in maintenance planning, a reduction in the number of emergency interventions, and an increase in the level of satisfaction with feed reliability. These findings provide further evidence that incorporating machine learning-driven predictive analytics into service level agreement management (SLA) management improves operational efficiency, decreases downtime, and strengthens decision-making capability in upstream feed operations.