Revolutionizing Customer Experience through Innovative Digital Marketing Approaches

Baddam, Parikshith Reddy

doi:10.18034/gdeb.v11i2.716

Cited by 5 publications

(3 citation statements)

References 28 publications

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“…As AI continues to evolve, so will the challenges and opportunities it presents. ImpactLens AI is committed to staying ahead of these changes, continuously evolving to meet the needs of an ever-changing world (Baddam, 2022).…”

Section: The Future Of Ai Equity: Vision and Commitmentmentioning

confidence: 99%

Harnessing the Power of ImpactLens AI: Transforming Data into Actionable Intelligence

Saxena

2024

Asian j. appl. sci. eng.

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This article explores the transformative potential of ImpactLens AI in turning data into actionable intelligence. The study aims to elucidate the objectives, significance, major findings, and policy implications of leveraging ImpactLens AI in organizations. The objective is to harness advanced machine learning algorithms to analyze data comprehensively, provide real-time insights, and predict future trends. The significance lies in empowering organizations to make informed decisions, drive strategic growth, and stay competitive in dynamic environments. Major findings reveal ImpactLens AI's capability to uncover hidden opportunities, mitigate risks, and achieve tangible results through comprehensive data analysis and predictive analytics. The discussion emphasizes the importance of embracing data-driven decision-making for organizational success. Policy implications suggest the adoption of ImpactLens AI to optimize operations, enhance customer experiences, and drive innovation. Overall, this study highlights the pivotal role of ImpactLens AI in transforming data into actionable intelligence for strategic advantage.

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Section: The Future Of Ai Equity: Vision and Commitmentmentioning

confidence: 99%

Harnessing the Power of ImpactLens AI: Transforming Data into Actionable Intelligence

Saxena

2024

Asian j. appl. sci. eng.

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“…Computational Complexity: The computational complexity of defect diagnosis and prognostics algorithms may make implementation difficult in resource-constrained industrial situations. These methods demand computing resources and experience for model building, training, and deployment (Baddam, 2022) (Baddam, 2020).…”

Section: Limitations and Policy Implicationsmentioning

confidence: 99%

Investigation of Fault Diagnosis and Prognostics Techniques for Predictive Maintenance in Industrial Machinery

Varghese,

Ande,

Mahadasa

et al. 2023

Eng. int. (Dhaka)

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This research looks into fault detection and prognostic methods for industrial machinery predictive maintenance to maximize equipment dependability, reduce downtime, and improve operational effectiveness. The project aims to investigate integrated fault diagnosis and prognostics methodologies, analyze their applications in different industrial sectors, and determine policy implications to encourage implementation. Peer-reviewed articles, industry reports, case studies, and other current material are thoroughly reviewed as part of the technique. Major conclusions demonstrating the value of integrated fault diagnosis and prognostics in early fault identification, proactive decision-making, and optimal maintenance scheduling have been drawn from case studies in the power generating, petrochemical refining, and automotive manufacturing industries. The policy ramifications encompass the requirement for staff training, data standardization, investment in R&D, and regulatory frameworks to surmount constraints and stimulate innovation in industrial maintenance procedures. Organizations must adopt predictive maintenance technology to maintain competitiveness, cut expenses, and guarantee the dependable operation of vital mechanical assets in changing circumstances.

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“…The "Resource-Constrained Project Scheduling Problem" (RCPSP) adds resource restrictions, including labor, equipment, and budget, to the PSP using dynamic programming. Dynamic programming approaches like the Resource-Constrained Project Scheduling Problem (RCPSP) maximize resource allocation and task scheduling while considering resource limits (Baddam, 2022).…”

mentioning

confidence: 99%

Dynamic Programming Approaches for Resource Allocation in Project Scheduling: Maximizing Efficiency under Time and Budget Constraints

Goda,

Vadiyala,

Yerram

et al. 2023

ABC j. adv. res.

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This study investigates dynamic programming methodologies for resource allocation in project scheduling, aiming to optimize efficiency while adhering to time and budget limitations. Exploring the theoretical underpinnings of dynamic programming, including time and budget restrictions into resource allocation models, and conducting case studies to assess actual applications are the primary goals of this project. This study utilized a thorough review process, including a synthesis of the relevant literature, an analysis of the case study, and policy implications. Among the most important discoveries are the following: the versatility of dynamic programming techniques in addressing resource allocation challenges across industries, the significance of incorporating time and budget constraints into decision-making processes, and the necessity of addressing limitations related to computational complexity, data requirements, and risk management. In the context of policy consequences, investments in computing infrastructure, data management techniques, and risk mitigation strategies are highlighted. In general, the findings of this study highlight the potential of dynamic programming methodologies to improve the efficiency and effectiveness of resource allocation within the context of a project that is affected by time and financial restrictions.

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Revolutionizing Customer Experience through Innovative Digital Marketing Approaches

Cited by 5 publications

References 28 publications

Harnessing the Power of ImpactLens AI: Transforming Data into Actionable Intelligence

Harnessing the Power of ImpactLens AI: Transforming Data into Actionable Intelligence

Investigation of Fault Diagnosis and Prognostics Techniques for Predictive Maintenance in Industrial Machinery

Dynamic Programming Approaches for Resource Allocation in Project Scheduling: Maximizing Efficiency under Time and Budget Constraints

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