The review mentions the possibilities of the Composable CRM Architecture to transform organisations, how Microsoft Dynamics 365 and the Power Platform can be employed to modularise and optimise the relationship systems of organisations. The composable model powered by the low-code platform, reusability of workflows, and a centralized data will be agile, scalable, and will offer a better customer experience because the legacy CRM systems have grown too cumbersome to sustain the demands of the current trends.
Medical equipment is becoming more complex and requires not only technological sophistication but also careful alignment with human cognitive and physical capabilities. Although both risk analysis and usability engineering are important in terms of safety and efficacy of the device, the two fields have been historically considered separately.
Cross-domain reliability engineering has become an important field of study with regard to providing reliable operation in consumer, gaming and enterprise software ecosystems.
Industrial products that have a high value and are moving fast are difficult to handle because they are highly priced, come and go, and the supply chain procedures are complex. The review is a comprehensive description of numerous optimization approaches that can be employed to attain improved inventory performance, cost reduction, and agility.
The whole human race is scurrying towards introducing the Artificial Intelligence in their work streams and the software development companies are on the frontline. However, in practice, the majority of software development organizations are finding it challenging to get beyond small AI experiments(1), and they are finding themselves using AI as a code assistant or business advisor. The cause is not on the technology itself but organizational issues like skills deficiency, ineffective processes, poor governance and resistance to change.
Artificial intelligence is no longer an isolated automation tool, but part of enterprise platform infrastructure. This trend has stimulated the emergence of academic interest in AI-enhanced teams, i.e. work arrangements in which human expertise is combined with machine intelligence through both conversational systems and more autonomous software agents.
Value engineering has become a core strategic tool for cost optimization in heavy-duty trucks and buses, as commercial vehicle development is shaped by demanding payload requirements, durability expectations, safety regulations, fuel-economy targets, and increasing pressure related to emissions compliance and electrification.
The advancement in artificial intelligence continues to transform knowledge-based applications, and Retrieval-Augmented Generation (RAG) has become a prominent framework for this activity. By adding wide-scale information retrieval, RAG enhances huge language models, allowing them to produce responses based on applicable and current knowledge instead of simply using the pre-trained memory.
Graphical user interfaces often use visual analytics techniques to track compliance by converting massive amounts of control data into risk indicators comprehensible to humans. The prominent interface patterns are risk concentration as a heat map to allow a rapid comparison of risk across categories or across units and case workflow panels, which are linked with the investigation action. In visual analytics, dashboard design, risk matrices, network security visualization, healthcare workflow interfaces and process mining, this review assesses the academic literature that is applicable to this system in such areas. It discusses the usefulness of compliance-risk interfaces that have color-coded summaries, drill-down, prioritization of alerts, workflow, and human factors.
Generative artificial intelligence (AI) is evolving at an alarming rate, and it has opened up some transformative opportunities to automate complex and large-scale workflows.. Specifically, the advent of agentic orchestration, in which several AI agents collaborate, reason, and engage with external tools, has re-established the design and execution of bulk processes. This review discussed the principles, designs, and practice of agentic orchestration in generative AI, particularly in its use to improve scalability, efficiency and reliability in workflows of scale. The article has discussed the main advances in large language models (LLMs), reasoning systems, integration of tools and the collaboration of multiple agents, and how these elements interact with each other to create intelligent workflow automation.
Electrocardiogram (ECG) classification is important for initial detection and diagnosis of cardiovascular diseases, but it remains challenging without human interpretation, as the signals vary and labeled datasets are limited. The latest developments in deep learning have shown excellent performance, but established models still tend to require large amounts of domain-specific training data.The review examines the new paradigm of leveraging transfer learning from vision-based models, such as convolutional neural networks and transformer models, for ECG classification tasks.
The rising pace of digital transformation, companies are moving towards the use of cloud, AI, and IoT in order to enhance efficiency and decision-making. However, the greater the technology, the greater the cyber risk. Conventional security testing cannot meet it because it is slow, manual, and time-consuming. This paper will examine how intelligent security testing contributes to enhancing cyber defense in digital transformation ecosystems. It provides in-depth insight into digital ecosystems, their components, classification, and major enabling technologies, i.e., cloud computing, IoT, and big data.
The growing adoption of Artificial Intelligence (AI) in credit scoring has significantly enhanced predictive accuracy, but it has also raised concerns regarding transparency, fairness, and trust. The “black box” nature of many Machine Learning (ML) models used in financial decision-making can hinder understanding and accountability, particularly in high-stakes scenarios such as loan approvals. To address these challenges, it is essential to develop methods that improve the explainability and scalability of AI-driven credit scoring systems.
AI-assisted regression testing is beginning to transform enterprise insurance systems, especially property and casualty platforms in which policy-lifecycle behaviour spans quoting, binding, endorsements, renewals, reinstatements, cancellations, billing interactions, and claims-related updates across multiple product lines.In this environment, product variation, rule volatility, regulatory constraints, and workflow interdependence increase release risk in ways that cannot be adequately managed through static test inventories alone.
This paper presents a design-and-evaluation study of an AI-Driven Continuous Auditing (AID-CA) framework for real-time risk assessment and control testing. Building on a focused narrative synthesis of prior continuous auditing and AI-in-audit research, we design a multi-layer assurance architecture and empirically evaluate a hybrid deep-learning/ensemble model.
The application of surfactants is diverse and extends into biomedical applications, industries, and food science owing to their capacity to lower surface and interfacial tensions. The increasing need for sustainability and superior performance attributes has led to growing interest in the design of natural or biosurfactants that are sourced from animal and microorganism sources.
The recent progress in the large-scale machine learning (ML) systems has increased the need for highly efficient and scalable computational pipelines which are highly efficient and scalable to run across heterogeneous hardware platforms. Compiler-assisted optimization is a feature that has become a key factor of enhancing performance, portability and energy efficiency in these systems.
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.
Composable CRM Architecture: Leveraging Dynamics 365 and Power Platform for Modular Enterprise Design
Pradeep Raja
Download
The review mentions the possibilities of the Composable CRM Architecture to transform organisations, how Microsoft Dynamics 365 and the Power Platform can be employed to modularise and optimise the relationship systems of organisations. The composable model powered by the low-code platform, reusability of workflows, and a centralized data will be agile, scalable, and will offer a better customer experience because the legacy CRM systems have grown too cumbersome to sustain the demands of the current trends.
Bridging Usability Engineering and Risk Analysis in Medical Device DesignYashwanth Teja Donga
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Medical equipment is becoming more complex and requires not only technological sophistication but also careful alignment with human cognitive and physical capabilities. Although both risk analysis and usability engineering are important in terms of safety and efficacy of the device, the two fields have been historically considered separately.
Cross-Domain Reliability Engineering for Consumer, Gaming, and Enterprise Software EcosystemsRahul Ravindran
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Cross-domain reliability engineering has become an important field of study with regard to providing reliable operation in consumer, gaming and enterprise software ecosystems.
Inventory Optimization Strategies in High-Value, Fast-Moving Industrial Goods. Gowtham Ramakrishnan, Achyutha Mohan.
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Industrial products that have a high value and are moving fast are difficult to handle because they are highly priced, come and go, and the supply chain procedures are complex. The review is a comprehensive description of numerous optimization approaches that can be employed to attain improved inventory performance, cost reduction, and agility.
Preparing the Enterprise for AI-Driven Software Development: A Readiness Framework for Organizational Transformation.
Debashis Patra, Ambar Nath Saha
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The whole human race is scurrying towards introducing the Artificial Intelligence in their work streams and the software development companies are on the frontline. However, in practice, the majority of software development organizations are finding it challenging to get beyond small AI experiments(1), and they are finding themselves using AI as a code assistant or business advisor. The cause is not on the technology itself but organizational issues like skills deficiency, ineffective processes, poor governance and resistance to change.
AI Augmented Teams: Redefining the Future of Work with Salesforce Copilots and Agentforce Grid
Sufia Parveen
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Artificial intelligence is no longer an isolated automation tool, but part of enterprise platform infrastructure. This trend has stimulated the emergence of academic interest in AI-enhanced teams, i.e. work arrangements in which human expertise is combined with machine intelligence through both conversational systems and more autonomous software agents.
Value Engineering Strategies for Cost Optimization in Heavy Duty Trucks and Buses
Aneesh Upasanamandiram Baladevan
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Value engineering has become a core strategic tool for cost optimization in heavy-duty trucks and buses, as commercial vehicle development is shaped by demanding payload requirements, durability expectations, safety regulations, fuel-economy targets, and increasing pressure related to emissions compliance and electrification.
Advances and Challenges in Retrieval-Augmented Generation Models for Knowledge-Driven NLP Tasks
Sumeet Mathur
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The advancement in artificial intelligence continues to transform knowledge-based applications, and Retrieval-Augmented Generation (RAG) has become a prominent framework for this activity. By adding wide-scale information retrieval, RAG enhances huge language models, allowing them to produce responses based on applicable and current knowledge instead of simply using the pre-trained memory.
Graphical User Interface for Visualizing Compliance Risk Using Heat Maps and Integrated Case Workflow Panel
Sanjay Chandrakant Vichare
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Graphical user interfaces often use visual analytics techniques to track compliance by converting massive amounts of control data into risk indicators comprehensible to humans. The prominent interface patterns are risk concentration as a heat map to allow a rapid comparison of risk across categories or across units and case workflow panels, which are linked with the investigation action. In visual analytics, dashboard design, risk matrices, network security visualization, healthcare workflow interfaces and process mining, this review assesses the academic literature that is applicable to this system in such areas. It discusses the usefulness of compliance-risk interfaces that have color-coded summaries, drill-down, prioritization of alerts, workflow, and human factors.
Agentic Orchestration of Generative AI in bulk Workflows
Sarath Vankamardhi Nirmala Varadhi
Download
Generative artificial intelligence (AI) is evolving at an alarming rate, and it has opened up some transformative opportunities to automate complex and large-scale workflows.. Specifically, the advent of agentic orchestration, in which several AI agents collaborate, reason, and engage with external tools, has re-established the design and execution of bulk processes. This review discussed the principles, designs, and practice of agentic orchestration in generative AI, particularly in its use to improve scalability, efficiency and reliability in workflows of scale. The article has discussed the main advances in large language models (LLMs), reasoning systems, integration of tools and the collaboration of multiple agents, and how these elements interact with each other to create intelligent workflow automation.
Automated Ecg Classification Using Transfer Learning From Vision Models To Medical Signal Domains
FNU Sudhakar Abhijeet
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Electrocardiogram (ECG) classification is important for initial detection and diagnosis of cardiovascular diseases, but it remains challenging without human interpretation, as the signals vary and labeled datasets are limited. The latest developments in deep learning have shown excellent performance, but established models still tend to require large amounts of domain-specific training data.The review examines the new paradigm of leveraging transfer learning from vision-based models, such as convolutional neural networks and transformer models, for ECG classification tasks.
Intelligent Security Testing Enhancing Cyber Defense in Digital Transformation Ecosystems
Bhalchandra Bapat
Download
The rising pace of digital transformation, companies are moving towards the use of cloud, AI, and IoT in order to enhance efficiency and decision-making. However, the greater the technology, the greater the cyber risk. Conventional security testing cannot meet it because it is slow, manual, and time-consuming. This paper will examine how intelligent security testing contributes to enhancing cyber defense in digital transformation ecosystems. It provides in-depth insight into digital ecosystems, their components, classification, and major enabling technologies, i.e., cloud computing, IoT, and big data.
Enhancing Scalability and Transparency in AI-Driven Credit Scoring: Optimizing Explainability
for Large-Scale Financial Systems
Daniel Thomas
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The growing adoption of Artificial Intelligence (AI) in credit scoring has significantly enhanced predictive accuracy, but it has also raised concerns regarding transparency, fairness, and trust. The “black box” nature of many Machine Learning (ML) models used in financial decision-making can hinder understanding and accountability, particularly in high-stakes scenarios such as loan approvals. To address these challenges, it is essential to develop methods that improve the explainability and scalability of AI-driven credit scoring systems.
AI -Driven Regression Testing for Policy Lifecycle Scenarios in Multi-Line P&C Insurance
Kiran Babu Boddapati
Download
AI-assisted regression testing is beginning to transform enterprise insurance systems, especially property and casualty platforms in which policy-lifecycle behaviour spans quoting, binding, endorsements, renewals, reinstatements, cancellations, billing interactions, and claims-related updates across multiple product lines.In this environment, product variation, rule volatility, regulatory constraints, and workflow interdependence increase release risk in ways that cannot be adequately managed through static test inventories alone.
AI-Driven Continuous Auditing: Enhancing Risk Assessment and Control Testing
Sachin Kumar Gupta
Download
This paper presents a design-and-evaluation study of an AI-Driven Continuous Auditing (AID-CA) framework for real-time risk assessment and control testing. Building on a focused narrative synthesis of prior continuous auditing and AI-in-audit research, we design a multi-layer assurance architecture and empirically evaluate a hybrid deep-learning/ensemble model.
Surfactant Extraction from Slaughterhouse Offals: Recovery, Characterization, and Biomedical Applications
Dr. Anandhu A Kumar, Dr V V Kulkarni, Joshika Panda, Kriti, Lavisha Gupta
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The application of surfactants is diverse and extends into biomedical applications, industries, and food science owing to their capacity to lower surface and interfacial tensions. The increasing need for sustainability and superior performance attributes has led to growing interest in the design of natural or biosurfactants that are sourced from animal and microorganism sources.
Compiler-Assisted Performance Optimization of Large-Scale Machine Learning Pipelines Using MLIR-Based
Ankush Jitendrakumar Tyagi
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The recent progress in the large-scale machine learning (ML) systems has increased the need for highly efficient and scalable computational pipelines which are highly efficient and scalable to run across heterogeneous hardware platforms. Compiler-assisted optimization is a feature that has become a key factor of enhancing performance, portability and energy efficiency in these systems.
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.