As the world moves swiftly towards more environmentally friendly industrial practices, the manufacturing
sector, which has traditionally been a major source of carbon emissions and resource use, is under a lot of pressure to
adapt. The solution is that combining artificial intelligence (AI) with digital twin technology offers a new technique to
keep track of sustainability in real time that might change the game. Digital twins are replicas of things, processes, or
even full systems that exist in the actual world.
2.
Fusion of Edge AI and Federated Learning in Smart Cities Karthikayan M, Eswar S
Carbon Fiber Reinforced Polymer (CFRP) composite strips have gained popularity as a viable strengthening technique for reinforced concrete structures. The main task of this experiment is to investigate flexural strengthening performance of Reinforced Concrete(RC) beams wrapped by CFRP composites.
3.
Data Mesh vs. Data Lakehouse: A Comparative Analysis of
Enterprise-Scale Data Architectures Tharsila A, Suba G
The rapid expansion of data-driven decision-making within modern enterprises has intensified the
demand for scalable, flexible, and high-value data architectures capable of supporting both advanced analytics and
real-time operational needs. Two architectural paradigms—Data Lakehouse and Data Mesh—have emerged as
leading approaches to address long-standing challenges associated with traditional data lakes, data warehouses,
and centralized data management frameworks.
4.
Event-Driven Data Architecture for Real-Time Analytics and
Decision Systems Farin, Safeer
The rapid expansion of data-driven decision-making within modern enterprises has intensified the
demand for scalable, flexible, and high-value data architectures capable of supporting both advanced analytics and
real-time operational needs. Two architectural paradigms—Data Lakehouse and Data Mesh—have emerged as
leading approaches to address long-standing challenges associated with traditional data lakes, data warehouses,
and centralized data management frameworks.
5.
Role of Enterprise Resource Planning Software (ERP) In Driving
Circular Economy Practices in the United States Dharani K, Gopika P
This study delved into the role of Enterprise Resource Planning Software (ERP) in facilitating circular economy
within the United States. It identifies key stakeholders and driving forces necessary to extend producer responsibility,
aligning with national circular economy strategies. An evaluation system is established to correlate producers' eco-design
strategies with downstream recycling performance, drawing data from sustainable development reports and recycling
platforms.
6.
Enhancing Customer Segmentation with Azure Cognitive
Services Ravi, Kishore
In the field of marketing, customer segmentation is crucial for targeted work and improved customer
engagement. Traditional customer segmentation mainly focuses on age, gender, location etc., which mainly misses the
emotional context of customer feedback. To improve the efficiency of this segmentation, we are using Azure Cognitive
Services’ sentiment analysis. This paper dwells into implementation details, outcomes and analysis. We found that
sentiment analysis can substantially enhance precision and accuracy of segmentation, leading to more effective marketing
approach.
7.
AI-Augmented Decision-Making in Complex Human Systems:
From Healthcare to Governance Sarathi, Ragu
Artificial Intelligence (AI) has emerged as a transformative force in augmenting human decision-making
across complex systems, ranging from personalized healthcare to governance structures that impact entire
populations. Unlike traditional data-processing tools, AI offers predictive intelligence, real-time analysis, and adaptive
learning that can complement human expertise in uncertain and high-stakes environments. In healthcare, AI augments
diagnostic precision, optimizes treatment strategies, and supports personalized medicine, thereby improving patient
outcomes while reducing resource strain.
8.
AI-Designed Materials and Nanotechnology for Next-Gen Engineering Applications Gokul G, Ishwarya
The integration of Artificial Intelligence (AI) with materials science and nanotechnology is rapidly transforming engineering applications. AI algorithms, including machine learning and deep learning models, are increasingly being utilized to design novel materials with tailored properties, optimize nanostructures, and predict performance under extreme conditions. This convergence enables accelerated material discovery, improved fabrication processes, and enhanced functional performance, addressing challenges in aerospace, electronics, energy, and biomedical engineering.
9.
Automation of PMO Processes through AI and Workflow Intelligence Devadharshini G, Kishalini C
The Project Management Office (PMO) serves as a cornerstone for organizational project governance,
standardization, and performance management. Traditional PMO processes, while effective in ensuring adherence to
project methodologies, often rely heavily on manual interventions for reporting, resource allocation, risk assessment, and
compliance monitoring. These manual processes are time-consuming, prone to human error, and often result in delays in
decision-making, which can impact overall project outcomes.
10.
Ethical Considerations of AI Adoption in Project Management Harihara sudhan, Sanjaykumar
Artificial Intelligence (AI) is rapidly reshaping the field of project management by offering advanced tools for
predictive analytics, automated scheduling, resource optimization, and risk management. These innovations promise
significant improvements in project efficiency, accuracy, and overall performance. However, alongside these benefits, AI
adoption introduces complex ethical challenges that can impact decision-making, stakeholder trust, and organizational
integrity. Key ethical concerns include algorithmic bias, transparency deficits, accountability ambiguity, and privacy risks.
This study investigates the ethical considerations associated with AI adoption in project management, aiming to provide a
comprehensive understanding of both opportunities and challenges.
11.
Theoretical Foundations of Artificial Intelligence and Machine
Learning Abirami, Swasti Karna
Another area is Artificial Intelligence (AI) and Machine Learning (ML), which is revolutionizing how machines
interact with humans, other complex systems as well as data. Abstract The theoretical basis of AI and ML is the intellectual
structure, which theoretically enables intelligent systems to reason, learn, make decisions, sense through perception and adapt
all on their own. Of course these foundations are in mathematics, statistics and logic, optimization theory, neuroscience and
cognitive science, computational theory.
12.
Mathematical Modeling in Distributed Computing Systems Kalavathi, Padmavati
Distributed computing systems have become the backbone of modern computational infrastructures, enabling
large-scale data processing, cloud computing, edge computing, Internet of Things (IoT) ecosystems, scientific simulations,
and artificial intelligence applications. The increasing complexity of distributed architectures has introduced significant
challenges in system coordination, resource allocation, fault tolerance, scalability, synchronization, and performance
optimization. Mathematical modeling plays a critical role in understanding, designing, analyzing, and improving
distributed computing systems by providing formal analytical frameworks that describe system behavior under varying
operational conditions. Through the application of mathematical theories, algorithms, stochastic processes, optimization
methods, graph theory, queuing models, and probabilistic analysis, researchers and engineers can predict system
performance, reduce computational overhead, and ensure reliability in distributed environments.
13.
Formal Verification Methods for Secure Software Architectures Rahul, Haripriya
The increasing dependence of modern society on digital systems has significantly elevated the importance of
software security and reliability. Contemporary software architectures are deeply integrated into critical infrastructures
such as healthcare systems, banking platforms, cloud computing environments, defense systems, transportation networks,
industrial automation, and Internet of Things (IoT) ecosystems. As these systems continue to evolve in complexity and
scale, the occurrence of vulnerabilities, cyberattacks, and software failures has become a major concern for researchers,
developers, and organizations worldwide. Traditional software testing approaches, while useful, often fail to provide
exhaustive guarantees regarding correctness, safety, and security properties. This limitation has led to growing interest in
formal verification methods, which use mathematical and logical techniques to prove the correctness and security of
software systems with a high degree of assurance.
14.
Powered Visual Intelligence for Cloud Infrastructure
Monitoring: Image-Based Diagnostics in Data Center
Environments
Modern data centers and cloud environments demand highly reliable, scalable, and autonomous monitoring
systems to ensure continuous service availability, especially as infrastructure grows increasingly complex and
distributed across edge, hybrid, and hyperscale deployments. Traditional monitoring approaches rely heavily on
telemetry data such as logs, metrics, and traces, which, while effective for software observability, often fail to capture
physical infrastructure anomalies such as hardware degradation, thermal hotspots, cable disconnections, airflow
obstructions, and visual indicators of failure that precede system outages.
15.
Agentic AI-Driven Quality Engineering for Continuous
Compliance and Adaptive Test Automation Srikanth Chakravarthy Vankayala
The increasing complexity of modern enterprise software systems, regulatory requirements, and continuous
delivery environments has created significant challenges in maintaining software quality, compliance governance, and
scalable test automation. Traditional quality assurance methodologies often rely on static testing strategies, manual
compliance validation, and rule-based automation frameworks that struggle to adapt to rapidly evolving application
ecosystems. This research paper presents an Agentic AI-driven quality engineering framework designed to enable
continuous compliance management and adaptive test automation through autonomous and policy-aware intelligent
agents.
AI-Powered Digital Twins for Real-Time Sustainability Tracking in Manufacturing Bala M, Kamalakannan M
Download
As the world moves swiftly towards more environmentally friendly industrial practices, the manufacturing sector, which has traditionally been a major source of carbon emissions and resource use, is under a lot of pressure to adapt. The solution is that combining artificial intelligence (AI) with digital twin technology offers a new technique to keep track of sustainability in real time that might change the game. Digital twins are replicas of things, processes, or even full systems that exist in the actual world.
Fusion of Edge AI and Federated Learning in Smart Cities
Karthikayan M, Eswar S
Download
Carbon Fiber Reinforced Polymer (CFRP) composite strips have gained popularity as a viable strengthening technique for reinforced concrete structures. The main task of this experiment is to investigate flexural strengthening performance of Reinforced Concrete(RC) beams wrapped by CFRP composites.
Data Mesh vs. Data Lakehouse: A Comparative Analysis of Enterprise-Scale Data Architectures
Tharsila A, Suba G
Download
The rapid expansion of data-driven decision-making within modern enterprises has intensified the demand for scalable, flexible, and high-value data architectures capable of supporting both advanced analytics and real-time operational needs. Two architectural paradigms—Data Lakehouse and Data Mesh—have emerged as leading approaches to address long-standing challenges associated with traditional data lakes, data warehouses, and centralized data management frameworks.
Event-Driven Data Architecture for Real-Time Analytics and Decision Systems
Farin, Safeer
Download
The rapid expansion of data-driven decision-making within modern enterprises has intensified the demand for scalable, flexible, and high-value data architectures capable of supporting both advanced analytics and real-time operational needs. Two architectural paradigms—Data Lakehouse and Data Mesh—have emerged as leading approaches to address long-standing challenges associated with traditional data lakes, data warehouses, and centralized data management frameworks.
Role of Enterprise Resource Planning Software (ERP) In Driving Circular Economy Practices in the United States
Dharani K, Gopika P
Download
This study delved into the role of Enterprise Resource Planning Software (ERP) in facilitating circular economy within the United States. It identifies key stakeholders and driving forces necessary to extend producer responsibility, aligning with national circular economy strategies. An evaluation system is established to correlate producers' eco-design strategies with downstream recycling performance, drawing data from sustainable development reports and recycling platforms.
Enhancing Customer Segmentation with Azure Cognitive Services
Ravi, Kishore
Download
In the field of marketing, customer segmentation is crucial for targeted work and improved customer engagement. Traditional customer segmentation mainly focuses on age, gender, location etc., which mainly misses the emotional context of customer feedback. To improve the efficiency of this segmentation, we are using Azure Cognitive Services’ sentiment analysis. This paper dwells into implementation details, outcomes and analysis. We found that sentiment analysis can substantially enhance precision and accuracy of segmentation, leading to more effective marketing approach.
AI-Augmented Decision-Making in Complex Human Systems: From Healthcare to Governance
Sarathi, Ragu
Download
Artificial Intelligence (AI) has emerged as a transformative force in augmenting human decision-making across complex systems, ranging from personalized healthcare to governance structures that impact entire populations. Unlike traditional data-processing tools, AI offers predictive intelligence, real-time analysis, and adaptive learning that can complement human expertise in uncertain and high-stakes environments. In healthcare, AI augments diagnostic precision, optimizes treatment strategies, and supports personalized medicine, thereby improving patient outcomes while reducing resource strain.
AI-Designed Materials and Nanotechnology for Next-Gen Engineering Applications
Gokul G, Ishwarya
Download
The integration of Artificial Intelligence (AI) with materials science and nanotechnology is rapidly transforming engineering applications. AI algorithms, including machine learning and deep learning models, are increasingly being utilized to design novel materials with tailored properties, optimize nanostructures, and predict performance under extreme conditions. This convergence enables accelerated material discovery, improved fabrication processes, and enhanced functional performance, addressing challenges in aerospace, electronics, energy, and biomedical engineering.
Automation of PMO Processes through AI and Workflow Intelligence
Devadharshini G, Kishalini C
Download
The Project Management Office (PMO) serves as a cornerstone for organizational project governance, standardization, and performance management. Traditional PMO processes, while effective in ensuring adherence to project methodologies, often rely heavily on manual interventions for reporting, resource allocation, risk assessment, and compliance monitoring. These manual processes are time-consuming, prone to human error, and often result in delays in decision-making, which can impact overall project outcomes.
Ethical Considerations of AI Adoption in Project Management
Harihara sudhan, Sanjaykumar
Download
Artificial Intelligence (AI) is rapidly reshaping the field of project management by offering advanced tools for predictive analytics, automated scheduling, resource optimization, and risk management. These innovations promise significant improvements in project efficiency, accuracy, and overall performance. However, alongside these benefits, AI adoption introduces complex ethical challenges that can impact decision-making, stakeholder trust, and organizational integrity. Key ethical concerns include algorithmic bias, transparency deficits, accountability ambiguity, and privacy risks. This study investigates the ethical considerations associated with AI adoption in project management, aiming to provide a comprehensive understanding of both opportunities and challenges.
Theoretical Foundations of Artificial Intelligence and Machine Learning
Abirami, Swasti Karna
Download
Another area is Artificial Intelligence (AI) and Machine Learning (ML), which is revolutionizing how machines interact with humans, other complex systems as well as data. Abstract The theoretical basis of AI and ML is the intellectual structure, which theoretically enables intelligent systems to reason, learn, make decisions, sense through perception and adapt all on their own. Of course these foundations are in mathematics, statistics and logic, optimization theory, neuroscience and cognitive science, computational theory.
Mathematical Modeling in Distributed Computing Systems
Kalavathi, Padmavati
Download
Distributed computing systems have become the backbone of modern computational infrastructures, enabling large-scale data processing, cloud computing, edge computing, Internet of Things (IoT) ecosystems, scientific simulations, and artificial intelligence applications. The increasing complexity of distributed architectures has introduced significant challenges in system coordination, resource allocation, fault tolerance, scalability, synchronization, and performance optimization. Mathematical modeling plays a critical role in understanding, designing, analyzing, and improving distributed computing systems by providing formal analytical frameworks that describe system behavior under varying operational conditions. Through the application of mathematical theories, algorithms, stochastic processes, optimization methods, graph theory, queuing models, and probabilistic analysis, researchers and engineers can predict system performance, reduce computational overhead, and ensure reliability in distributed environments.
Formal Verification Methods for Secure Software Architectures
Rahul, Haripriya
Download
The increasing dependence of modern society on digital systems has significantly elevated the importance of software security and reliability. Contemporary software architectures are deeply integrated into critical infrastructures such as healthcare systems, banking platforms, cloud computing environments, defense systems, transportation networks, industrial automation, and Internet of Things (IoT) ecosystems. As these systems continue to evolve in complexity and scale, the occurrence of vulnerabilities, cyberattacks, and software failures has become a major concern for researchers, developers, and organizations worldwide. Traditional software testing approaches, while useful, often fail to provide exhaustive guarantees regarding correctness, safety, and security properties. This limitation has led to growing interest in formal verification methods, which use mathematical and logical techniques to prove the correctness and security of software systems with a high degree of assurance.
Powered Visual Intelligence for Cloud Infrastructure Monitoring: Image-Based Diagnostics in Data Center Environments
Download
Modern data centers and cloud environments demand highly reliable, scalable, and autonomous monitoring systems to ensure continuous service availability, especially as infrastructure grows increasingly complex and distributed across edge, hybrid, and hyperscale deployments. Traditional monitoring approaches rely heavily on telemetry data such as logs, metrics, and traces, which, while effective for software observability, often fail to capture physical infrastructure anomalies such as hardware degradation, thermal hotspots, cable disconnections, airflow obstructions, and visual indicators of failure that precede system outages.
Agentic AI-Driven Quality Engineering for Continuous Compliance and Adaptive Test Automation
Srikanth Chakravarthy Vankayala
Download
The increasing complexity of modern enterprise software systems, regulatory requirements, and continuous delivery environments has created significant challenges in maintaining software quality, compliance governance, and scalable test automation. Traditional quality assurance methodologies often rely on static testing strategies, manual compliance validation, and rule-based automation frameworks that struggle to adapt to rapidly evolving application ecosystems. This research paper presents an Agentic AI-driven quality engineering framework designed to enable continuous compliance management and adaptive test automation through autonomous and policy-aware intelligent agents.