Enterprise services are becoming more reliant on the Domain Name System (DNS) infrastructure, and with that increased reliance comes an increased need for proactive monitoring solutions that can detect operational anomalies that can affect service availability. Traditional DNS monitoring methods are largely dependent on availability checks and manual log analysis, which can hinder timely insights into infrastructure health. This paper proposes a pattern-based architecture for real-time monitoring of the DNS infrastructure in enterprise grid networks, based on syslog analysis. The proposed framework starts by collecting the DNS operational logs using Python, parses the logs using structured log events, stores them in SQL Server, visualizes them using Grafana, and automatically alerts using the information gathered. A systematic event classification model is created to classify DNS events based on their operational significance, to monitor zone transfers, replication status, notification events, and configuration changes. The framework aims to be scalable for large enterprise deployments, improve observability of infrastructure, accelerate incident detection, and improve operational decision-making. The proposed approach guarantees efficient and cost-effective solutions to enhance DNS operational resilience through the use of structured log mining and real-time visualization. The insights have been compiled into a practical monitoring architecture that can enable the modern enterprise DDI environment and provide a starting point for future infrastructure monitoring with AI.
As enterprise information technology (IT) environments grow more complex, there is a greater need for efficient Domain Name System (DNS) domain management to facilitate digital services, cloud adoption, and cross-functional collaboration. For many large organizations, domain-specific questions continue to be handled traditionally through centralized IT Service Desks, which causes high ticket volumes, long resolution times, and poor use of technical staff time. The present invention provides a self-service DNS domain management portal having central intelligence of domain information to simplify domain discovery and metadata retrieval and to decrease operational overhead. The solution being proposed involves consolidating all the internal DNS records, organisational metadata, and information from external domain registries into a central repository, allowing authenticated users to conduct real-time searches of the domains with a secure web-based front-end. Built on ASP. The portal improves accessibility, improves governance, and reduces manual support processes and reliance on NET Core, as well as increasing governance and support using Microsoft SQL Server with Windows Authentication. The proposed architecture illustrates the benefits of centralising domain intelligence for more efficient service delivery, enterprise-wide decision-making, and optimised IT services. The study provides a scalable and practical approach to modern enterprise DNS management, which is aligned with the platform engineering, self-service IT, and intelligent network management trends.
The Urban Heat Island (UHIs) is an important environmental problem of rapidly developing cities, contributing to the exposure to heat, aggravating air quality, and enhancing the rate of health risks caused by climate changes. Since the process of urbanization all over the world has been growing evenly during the past decade, urban areas have gotten hotter in comparison to the surrounding land areas, which has resulted in the need to consume more energy, overburdening the infrastructure, and making the process of thermoregulation unpleasant. The most recent advancement of machine learning (ML) introduced the application of powerful analytical tools that can identify the presence of UHI trends, extreme heat events, and simplify the climate-resilient infrastructure policy (Zhou et al., 2019). The paper will also determine the application of ML-based models in the reduction of UHIs through the use of predictive intelligence, dynamically allocating resources, and planning cities based on data.To predict the urban temperature data, the satellite-based land surfaces, and infrastructure vulnerability indicators, this article uses empirical data (2016 2020) that is back-dated to run the empirical data. Another hybrid algorithm- Urban Heat Island Neural Network ( UHINet ) is introduced and offered to educate spatial-temporal change of the temperature and propose certain mitigating actions, such as planting sites, reflective surfaces, and the best construction of buildings. The complementary geospatial models also are incorporated like Geo-Heat Mapping System (GHMS) and the Adaptive Environmental Heat Forecasting Model (AEHF) to supplement pattern recognition and complementary interpretability. It was discovered that UHINet has the potential to perform much better than classical algorithms and enhance the accuracy of prediction by 14.6 percent and decrease the mean temperature forecasting error by 22 percent in all datasets.It is also stated in the research that ML can effectively measure effectiveness of mitigation measures with the help of statistical tools and formulas of heat-intensity. It has shown that green-infrastructure interventions delivered an average of 1.8 o C of urban cooling and 2.3 o C maximum surface temperature of local surfaces of high-albedo surface treatments (Li and Bou-Zeid, 2018). Such findings justify the radical application of machine learning in fostering the resilience of cities. With climate science, data analytics, and smart optimization models, the research will indicate a scalable solution, whereby cities can be in a position to adapt to the change in the heat stress and come up with climate-resilient infrastructure by the next few decades.
As Artificial Intelligence (AI) transforms various sectors of industry and all spheres of everyday life, there is more necessity to make intelligence relocate towards the place where data is being generated at the edge. This shift is termed Edge AI, and it allows gadgets, including smartphone, sensors, and cost-automated machines, to determine decisions on the ground, devoid of the continuously reliant connection to geographically distant distributed cloud servers. It is expected that it would augment response rates, data security as well as autonomy, but it also raises profound issues on how we would be able to construct resilient systems that are made of distributed agents with AI capabilities that could work effectively under real world conditions.In this paper, I will explore how deployment of AI at the edge may impact the topology, the resilience of AI fabrics, the complex interdependence of compute, data and learning systems that enable intelligent decisions. We revisit the rationale of Edge AI and how latency minimization and control of data sovereignty have led to it, and the issues of resource bottlenecks and latencies, synchronization and security in a distributed setting. We also make comparisons between the current solutions of data locality, collaborative intelligence, and federated learning and we also endeavor to find answers as to how an AI infrastructure can become flexible and dynamic in the processes of a decentralized world. We would like to establish a reputation of the principles of being smart, resilient, secure, and human aligned AI through this work.
The high in-migration of the urban population has augmented the pressure on the infrastructure systems to be resilient, efficient and endure, and sustainable. Reactive or time-based traditional maintenance activities, which lack real-time visibility, do not support the complexity of the present day urban facilities. The transformative infrastructure Smart infrastructure based on combining Internet of Things (IoT) and Artificial Intelligence (AI) provides a transformative solution to anticipatory maintenance. IoT sensors, interconnected to each other, and by using advanced analytics, gather real-time information on structure, energy and environmental metrics, whereas AI models use the collected data to predict possible failures in advance. This is a predictive solution which minimizes downtime of operations, maximizes the useful life of the assets that are critical, economizes cost and increases the safety of the people. Still, factors like risk of cyber-security, extensive implementation charges, and administration of data are forbidding impediments on broad use. To respond adequately to these issues, it is necessary to have effective policy frameworks, cross-sector cooperation, and digital capacity-building investments. Via the interconnection of the technology and governance and urban planning, smart infrastructure has proven to have the capacity of redesigning maintenance approaches to revolve around sustainable and resilient urban environments in the future.
The growing integration of Artificial Intelligence (AI) in software quality assurance (SQA) is transforming how organizations test, validate, and deliver reliable software systems. This paper explores the evolving paradigm of Human–AI collaboration, emphasizing the need to balance automation efficiency with human expertise. While AI-driven tools enhance accuracy, speed, and defect prediction, human insight remains crucial for contextual interpretation, ethical oversight, and adaptive decision-making. Drawing from recent studies and industry frameworks, this research identifies best practices that optimize hybrid collaboration between humans and intelligent systems. It also examines challenges such as algorithmic bias, explainability, and trust, proposing a co-evolutionary framework for future SQA processes. By harmonizing automation with human creativity and critical reasoning, the study highlights how collaborative intelligence can drive innovation, accountability, and sustained software excellence in the era of intelligent engineering.
Generative AI (AI) has quickly become a groundbreaking force with the potential to transform creativity, innovationand productivity in various domains including art, education, healthcare and business. Although its possibilities areincontrovertible, the emergence of generative AI also brings up deeply significant ethical concerns in terms of fairness,accountability, intellectual property, misinformation, and social responsibility. In this article, the ethical aspects of generativeAI are critically discussed, and it focuses on the necessity to find the balance between the ability of the AI to promoteinnovation and creativity and the need to reduce the threats. The paper is based on literature, policy frameworks, andcase studies and investigates the problem of data bias, copyright infringement, transparency, and labor displacement. Italso brings out the role of the main stakeholders,developers, policymakers, industries and users in responsible use of AI.This paper supports a multi-stakeholder strategy that allows merging ethics, regulatory frameworks and digital literacyto protect human values and allow technological advancement. Finally, this study highlights that regulators must actresponsibly to govern generative AI to safeguard intellectual property rights and social confidence but also to preserveits use as a source of human-centered innovation and creativity.
The sheer proliferation of terascale artificial intelligence (AI) workloads (in disk drive, Hadoop and NoSQL) like distributed deep learning, model inference pipelines, has put unprecedented pressure on data center interconnects. As part of capturing these demands, there is a rampant use of high-performance network technologies such as the Ultra Ethernet, and the InfiniBand in modern infrastructures with ultra-low latency and high bandwidth. But conventional telemetry systems do not have the density and real-time sensitivity to best tune network dynamics with such loads. The topic of the paper at hand is the development of advanced network telemetry and AI-based optimization in order to improve performance, identify anomalies, and mitigate congestion in high-speed interconnects. Our architecture is inspired by telemetry and is based on programmable data planes, in-band telemetry and high bandwidth monitoring engines that use to emit highly granular, low-latency data streams. The streams are passed through AI/ML models, such as unsupervised anomaly detectors, predictive congestion algorithms, to dynamically adjust routing and resource allocation. Our findings indicate that this method works well in enhancing usage of communications networks, latency and pro-active management of network health. The paper advances a scalable design of a real-time intelligent network management in next-generation AI systems, and proposes a set of factors it would be necessary to consider in future studies along the telemetry-AI-high-speed networking nexus.
The fast development of the Industry 4.0 system has turned the old fabrication system into a smart, connected ecosystem sharing the level of rapid evolution that requires new solutions to the efficiency of the business processes and even equipment reliability. Artificial Intelligence (AI) has allowed predictive maintenance (PdM) to develop into a strategic method of reducing unplanned downtimes, maximizing machine life, and minimizing maintenance expenses. This paper investigates the possibility of systems such as machine learning, deep learning, and data analytics, being integrated into the smart manufacturing environment, in order to predict equipment breakdowns before it breaks down. It is a thorough review of the state-of-the-art AI models used in PdM, a review of the effectiveness of these models using the real-time sensor data and a modular system to implement the AI models in different industrial environments. By means of comparing and contrasting classic and AI-enhanced maintenance systems, the given research underscores better performance of intelligent PdM in terms of optimal production processes and decision-making. The limitations of key issues including sparsity of data, scalability issues, and model explanation have been addressed, as well as how this research might move forward into the future through the use of edge computing, the use of digital twins, and explainable AI. The results highlight the transformational role of AI in establishing resilient, cost effective and sustainable manufacturing systems.
This paper presents the load control in the industry during peak hours by using PLC and monitors all load parameters of the motors on PC by using SCADA. The Energy management is the highest demand of the organizations to reduce their energy cost. Confirm to the regulatory requirements and improve their corporate image. The automation used in the industries has several benefits. To accomplish the automation in the industry we have knowledge of PLC and SCADA. PLC is a programmable logic controller which controls the load or machines in industry. PLC programming is done by using ladder logic. The overall automation of the industry is controlled by SCADA software. SCADA define as a centralized system that control and monitor the whole sites. SCADA is used for collecting the data from various sensors or machines and then monitor the proper functioning of the machines. Automation will make the industry safe, cheap, highly efficient and maintained free.
An innovative indoor navigation system designed to assist users in navigating complex indoor environments such as shopping malls, airports, and hospitals. The system leverages a combination of wireless technologies, including Wi-Fi and Bluetooth, alongside advanced algorithms for real-time positioning and route optimization. By utilizing existing infrastructure and mobile devices, the system offers a cost-effective solution that enhances user experience and accessibility. The proposed navigation system consists of three main components: a mapping module for indoor layout visualization, a positioning module that employs trilateration techniques to determine user location, and a routing module that generates optimal paths to desired destinations. The integration of user feedback mechanisms allows for continuous improvement and adaptation to changing environments. [1] Additionally, the system incorporates a dynamic and scalable architecture, allowing it to be easily deployed and adapted to various indoor settings with minimal infrastructure changes. It is designed to work seamlessly with mobile applications, providing users with real-time turn-by-turn directions, accessibility features, and notifications for points of interest. The system's ability to integrate with different wireless networks ensures high accuracy in location tracking, even in challenging environments where GPS signals may be weak or unavailable. This flexibility makes it an ideal solution for enhancing user navigation in diverse indoor spaces, fostering greater independence and convenience for all users, including those with disabilities.[2]
Chronic diseases, such as Alzheimer’s and cardiovascular conditions, pose significant global health challenges, necessitating early detection to improve outcomes and reduce costs. This study builds a machine learning (ML) framework using the U.S. Chronic Disease Indicators (CDI) and Alzheimer's datasets. It does this by combining a new hybrid feature selection method with advanced classification algorithms. Gradient boosting models (XGBoost, LightGBM) do better than traditional classifiers, and the framework achieves up to 93.2% accuracy and 0.96 AUC-ROC. It improves early detection by 25–30% and makes computations 30% easier. It provides us useful information about risk factors like APOE ε4 and cholesterol levels. These findings support data-driven healthcare policies and preventive strategies, laying a foundation for scalable, AI-driven chronic disease management.
This paper presents the load control in the industry during peak hours by using PLC and monitors all load parameters of the motors on PC by using SCADA. The Energy management is the highest demand of the organizations to reduce their energy cost. Confirm to the regulatory requirements and improve their corporate image. The automation used in the industries has several benefits. To accomplish the automation in the industry we have knowledge of PLC and SCADA. PLC is a programmable logic controller which controls the load or machines in industry. PLC programming is done by using ladder logic. The overall automation of the industry is controlled by SCADA software. SCADA define as a centralized system that control and monitor the whole sites. SCADA is used for collecting the data from various sensors or machines and then monitor the proper functioning of the machines. Automation will make the industry safe, cheap, highly efficient and maintained free.The implementation of efficient energy systems is considered as one of the most important requirements in modern building. The purpose of these systems is to regulate energy consumption and meanwhile to reduce the negative impact on the surrounding environment through an efficient management of available energy resources, including renewable and nonrenewable resources. The integration of mains power supply with the solar power supply, besides other energy resources is a key element in designing the required energy management system. In this paper, the usage of Programmable Logic Controllers (PLC’s) is proposed to control the energy consumed by various loads in the building based on real-time measurements of certain factors affecting the total amount of consumed energy. Hence, this paper presents a real time prototype design and implementation of an automated control system of mains electricity power distributed to various loads, using Allen Bradley MicroLogix 1100 Programmable Logic Controller (PLC). The PLC is programmed using ladder diagram for intelligent switching of both solar power supply and diesel generator power supply units. Also, it is programmed in order to prioritize the usage of the available solar energy as much as possible. The Rockwell Software Logix 500 is used for programming a PLC, running on a host computer terminal. For completeness, the control program results are compared with a hardware interfacing module.
This study addresses the growing demand for electricity in India, particularly in remote villages.With biomass and other non-commercial fuels constituting a significant portion of energyrequirements, the need for alternative energy sources is evident. The aim of this research is to explorethe use of solar power as a sustainable energy solution for remote villages in India, specifically forpowering grain milling activities. The methodology involves the design and implementation of solarpanel systems to capture solar energy and convert it into electricity for use in grain milling. The studyincludes participants from remote Indian villages who currently rely on traditional, time-consumingmilling techniques. The results indicate that solar power can significantly improve the efficiency ofgrain milling, reducing the time and effort required. The implications of this research are farreaching,as it highlights the potential for solar power to address energy needs in remote are as andimprove the livelihoods of the residents.
This research explores operational inefficiencies in the outdoor advertising industry, a sector projected to grow significantly, reaching $410.82 billion by 2024, but hindered by challenges like market fragmentation, lack of standardized pricing, and limited data-driven practices. The study introduces an innovative digital platform designed to connect property owners and advertisers, streamlining processes and fostering transparency. Key issues such as inconsistent pricing models, inadequate audience tracking, and limited realtime analytics are addressed through solutions like IoT-enabled smart displays, AI-powered targeting, and blockchain-based smart contracts. The integration of these technologies enhances the effectiveness and accountability of advertising campaigns while boosting stakeholder trust. Recommendations focus on creating a centralized marketplace with standardized procedures, deploying advanced data analytics, and implementing robust security measures. The proposed platform aims to revolutionize the outdoor advertising landscape by bridging operational gaps, increasing ROI, and ensuring long-term sustainability through technological innovation.
Cyber bullying has emerged as a great threat to the people on the internet. The platform, which was made for good use, is being used by some to harass people. This is actually a misuse of a great invention. Also, the nature of social media is such that these things spread very quickly due to the online communications. Many times, this spreads anonymously. Manual way of detection of cyber bullying will be very inefficient and a lot of time consuming. Thus, an automated cyber bullying detection using machine learning will come to help. This paper explores the effectiveness of various machine learning algorithms in classifying the tweets into different types of cyber bullying, including age based, ethnicity based, gender based, religious based and non cyber bullying. This automated detection using machine learning will offer a great approach to mitigate these bullying and its after effects by identifying and isolating the harmful texts and messages in real time.
Abstract With the way enterprise networks are changing with high cloud adoption rates, remote working populations and advanced forms of cyber threats, the traditional perimeter approach to security is no longer tenable. Zero Trust Architecture (ZTA) has become an innovative approach to cybersecurity that aims to overcome the weaknesses of the legacy systems due to the implementation of the set of principles of never trust, always verify. This paper will examine the theoretical backgrounds, essential elements, practical applications of ZTA in current business spheres. It offers a critical analysis of existing constructs, including NIST SP 800-207 and the Forrester ZTX framework as well as case studies in the industry, featuring Google BeyondCorp, the Zero Trust implementation at Microsoft, and the Zero Trust requirements at U.S federal government agencies. The comparative analysis used in the study provides both positive points, e.g. the improvement of access control, regulatory compliance, and threat mitigation, and negative ones, e.g. the need to integrate with the legacy infrastructure, the performance overhead, and organizational readiness. Lastly, the article suggests future directions and emerging trends such as the importance of artificial intelligence, blockchain-based identity, and deployment of Zero Trust in Internet of Things (IoT) as well as hybrid cloud ecosystem. Combining the learning of the scholarly world and the practices of organizations, this paper will provide an enterprise with a clear guide on how to implement Zero Trust without limits, vulnerability, and cognizance.
The growing complexity and velocity of cyber threats in high-security environments such as defense, critical infrastructure, and intelligence networks necessitates a paradigm shift in threat detection capabilities. Traditional cybersecurity systems, including those enhanced by classical machine learning algorithms, often struggle to process and classify massive volumes of heterogeneous and encrypted data in real time. This shortcoming is particularly evident in the context of advanced persistent threats (APTs), polymorphic malware, and insider attacks, which require rapid adaptation and heightened sensitivity to anomalous behavior.Quantum Machine Learning (QML), an emerging interdisciplinary field at the intersection of quantum computing and artificial intelligence, presents a promising avenue for augmenting threat detection mechanisms. Leveraging quantum phenomena such as superposition and entanglement, QML models offer potential advantages in processing speed, pattern recognition, and feature space transformation that can outperform their classical counterparts in high-dimensional data analysis. This paper explores the application of QML to threat detection in high-security networks, proposing a hybrid quantum-classical framework that integrates quantum-enhanced classifiers such as quantum support vector machines and variational quantum circuits into existing detection pipelines.The study outlines a technical overview of quantum computing principles relevant to cybersecurity, critically evaluates existing detection architectures, and presents simulation-based case studies to assess performance metrics, including detection accuracy and false positive rates. It further examines the limitations of current quantum hardware, algorithmic constraints, and emerging ethical and operational considerations. The findings suggest that while QML is still constrained by hardware maturity and integration complexity, it holds transformative potential for proactive, intelligent, and adaptive cyber defense systems in high-stakes environments. This research contributes to ongoing efforts to future-proof cybersecurity infrastructure against both classical and post-quantum threat landscapes.
Per- and polyfluoroalkyl substances PFAS have emerged as persistent contaminants of concern in urban stormwater systems due to their chemical stability, mobility, and resistance to conventional treatment processes. Stormwater infiltration basins, while effective for hydrologic control, may facilitate PFAS migration into subsurface environments if not properly engineered. This study investigates the effectiveness of reactive soil mixes incorporating biochar, zeolite, and iron oxide as amendment media for enhanced PFAS adsorption in stormwater infiltration basins. Laboratory-scale adsorption assessments were combined with field-scale demonstration and pre- and post-infiltration monitoring to evaluate retention performance and dominant adsorption mechanisms. Results indicate that blended reactive media significantly improve PFAS attenuation compared to native soils, with adsorption governed by a combination of electrostatic interactions, hydrophobic partitioning, and surface complexation. Field observations confirm sustained PFAS reduction under operational stormwater loading, supporting the integration of reactive soil amendments as a practical strategy for mitigating PFAS transport in infiltration-based stormwater management systems.
Cryogenic distillation remains a cornerstone technology for the high-purity separation of industrial gases such as oxygen, nitrogen, and argon. Despite its widespread application across critical sectors including pharmaceuticals, semiconductors, aerospace, and energy numerous technical and regulatory challenges continue to constrain operational efficiency and product compliance. This study critically examines the multifaceted issues facing cryogenic distillation systems, including thermodynamic limitations, material degradation at ultra-low temperatures, complex process control requirements, and evolving regulatory expectations. Drawing from recent advances in process engineering, the paper explores innovative solutions such as digital twin modeling, AI-driven control systems, and sustainable design integration. Through case studies and comparative industry analyses, the research highlights best practices for aligning distillation processes with stringent compliance benchmarks while optimizing energy and cost performance. The findings underscore the need for a cross-disciplinary approach that combines engineering innovation with regulatory foresight, paving the way for a new generation of high-fidelity cryogenic separation systems.
