Papers by Thejaswi Adimulam
International Journal of Innovative Research in Science, Engineering and Technology, 2024
Deep Reinforcement Learning (DRL) has emerged as a powerful paradigm for solving complex decision... more Deep Reinforcement Learning (DRL) has emerged as a powerful paradigm for solving complex decision-making tasks across various domains. This comprehensive review explores the current state-of-the-art in DRL, its applications in complex decision-making scenarios, and the challenges and opportunities that lie ahead. We provide an in-depth analysis of key DRL algorithms, their theoretical foundations, and practical implementations. The paper also examines the integration of DRL with other AI techniques such as federated learning, explainable AI, and automated machine learning. Furthermore, we investigate the application of DRL in critical areas including cloud computing, database optimization, and autonomous systems. Through this extensive review, we identify promising research directions and potential breakthroughs that could shape the future of AI-driven decision-making systems.
International Research Journal of Engineering and Technology, 2021
In today's fast-changing digital landscape, organizations are now embracing generative AI to chan... more In today's fast-changing digital landscape, organizations are now embracing generative AI to change human capital management (HCM) processes. This paper talks about integrating generative AI into SAP HCM on HANA and how this will help to improve employee experiences through personalized HR services. Companies use data-driven AI capabilities to automate HR routine tasks, speed decision-making, and personalize employee services such as recruitment, onboarding, and careers. Yet, data privacy problems, integration complexities, and employee resistance prevent the unobstructed use of AI technologies. The rest of this paper evaluates these challenges and proposes best practices to overcome them. This research is structured in a way that entails a comprehensive review of AI and HCM literature, methodology consisting of case studies, and interviews with HR professionals from multinational companies, followed by a discussion on key findings on the effectiveness of AI-driven HR strategies. Significant improvements in employee satisfaction, operational efficiency, and strategic decision-making are shown in the results. However, the research also indicates that several pressing challenges must be addressed strategically. Finally, future research directions and practical implications are discussed for HR leaders trying to enact AI solutions while upholding ethical and data governance standards.
International Journal of Enhanced Research in Management & Computer Applications, 2024
This research studies the integration of generative models to improve dialogue management in conv... more This research studies the integration of generative models to improve dialogue management in conversational AI systems, focusing on chatbots. As these systems become more pervasive across industries, providing coherent and contextually relevant interactions to enhance user satisfaction and engagement becomes increasingly important. This study extensively explores how advanced generative models, including GPT, can greatly improve dialogue flow, personalization, and adaptability, changing the user experience. We analyze current methodologies and practices and identify several technical and ethical challenges that undermine the effectiveness of conversational AI. Among these, scalability, bias, and data privacy challenges must be solved to build trustable and effective chatbots. We respond by proposing solutions that exploit the capabilities of generative models to address these concerns. This research provides valuable insights into how to optimize chatbot performance through comprehensive case studies and real-world applications. Specifically, we show how generative models can be applied to improve the user experience of conversational agents in terms of engagement and personalization of the interaction, which in turn influences the agent's overall functionality. We also discuss how future advances in AI-driven communication and interaction can be explored, the need for interdisciplinarity to address the changing needs of users, and the ethical implications of AI technologies. By exploring these dimensions, this study helps advance a more complete picture of how generative models can influence the future of conversational AI.
International Journal of All Research Education and Scientific Methods, 2024
Due to the recent advancement in generative AI, especially large language and diffusion-based ima... more Due to the recent advancement in generative AI, especially large language and diffusion-based image-generating models, there has been immense pressure on large and efficient storage systems. These models, which consist of tens or hundreds of billions of parameters, present significant problems related to data handling, especially from a storage perspective, especially in cloud scenarios where data storage and the ability to scale are paramount. This research article aims to identify new storage architectures and approaches that improve generative AI models' storage and retrieval methods. Emphasis is placed on achieving the maximum efficiency of these systems in terms of certain requirements and constraints within cloud environments. The approaches should enhance scalability, increase speed, and decrease expenses, enabling storage, management, and access to these complex models. These solutions in this study help tackle storage obstacles for generative AI, making such technologies easier to incorporate into a range of applications and services and contributing to the extensive adoption and usage of such impactful models.
International Journal of Enhanced Research in Science, Technology & Engineering, 2022
Natural Language Processing (NLP) has seen remarkable advancements in recent years, largely due t... more Natural Language Processing (NLP) has seen remarkable advancements in recent years, largely due to the
development of sophisticated deep learning models. However, these models often require vast amounts of labeled
data to achieve high performance, which poses a significant challenge in low-resource scenarios. This paper explores
the application of transfer learning techniques in NLP to address the challenges associated with low-resource
languages and domains. We provide a comprehensive review of current transfer learning approaches in NLP,
including pre-training methods, cross-lingual transfer, and domain adaptation. Additionally, we present a novel
framework that combines adversarial training with multi-task learning to enhance the effectiveness of transfer
learning in low-resource settings. Our experimental results demonstrate the efficacy of this approach across various
NLP tasks, including machine translation, named entity recognition, and sentiment analysis. The proposed method
shows particular promise in scenarios where labeled data is scarce, outperforming existing baselines by a significant
margin. This research contributes to the ongoing efforts to democratize NLP technologies and make them accessible
to a wider range of languages and domains
TIJER - INTERNATIONAL RESEARCH JOURNAL , 2020
The Internet of Things (IoT) has revolutionized data collection across various domains, generatin... more The Internet of Things (IoT) has revolutionized data collection across various domains, generating massive
amounts of heterogeneous data at unprecedented rates. This surge in data volume and velocity presents both
opportunities and challenges for data analytics. Cloud computing environments offer a promising solution for
processing and analyzing IoT data due to their scalability and resource elasticity. This paper presents a
comprehensive review and analysis of scalable machine learning models designed for IoT data analytics in
cloud environments. We explore the synergies between IoT, cloud computing, and machine learning,
discussing the challenges of processing IoT data at scale and the advantages of cloud-based solutions. The
paper examines various machine learning algorithms and architectures optimized for cloud deployment,
including distributed learning frameworks, federated learning, and edge-cloud collaborative models. We also
present case studies demonstrating the application of these models in real-world IoT scenarios, such as smart
cities, industrial IoT, and healthcare. Our findings highlight the importance of scalable machine learning
models in extracting valuable insights from IoT data and the role of cloud environments in enabling efficient,
large-scale data analytics
TIJER - INTERNATIONAL RESEARCH JOURNAL, 2023
Enterprise Resource Planning (ERP) systems have always been the core of business processes and ar... more Enterprise Resource Planning (ERP) systems have always been the core of business processes and are designed to
integrate all the processes of an organization. As new technologies continue to come into the market, including AI, IoT,
and Blockchain, ERP systems are in the process of being disrupted. This paper further discusses how these technologies
can be incorporated into the next generation of ERP systems to transform business automation, decision-making, and
operations. By identifying how AI can support predictive analytics, IoT for real-time data capture, and Blockchain for
secure, decentralized record keeping, this paper offers an understanding of how new generation ERP influences business
processes. The selected early adopters, technological frameworks, and industry best practices are discussed so the reader
can understand how these innovations can be applied to gain better operational efficiency and competitiveness.
TIJER - INTERNATIONAL RESEARCH JOURNAL, 2022
These areas have been transformed since Workday adopted generative AI and incorporated it into it... more These areas have been transformed since Workday adopted generative AI and incorporated it into its cloud-based Human
Capital Management (HCM) system. As such, this paper focuses on developing Workday’s AI-related features,
particularly its capacity to revolutionize some core processes within workforce management, such as recruitment,
employee engagement, and overall workforce planning. Using generative AI in Workday for prediction, enhanced
employee experience, and future-oriented workforce management, companies can improve their operational outcomes
and achieve better talent acquisition strategies in a fiercely competitive global environment. The paper also discusses
ethical issues, privacy preservation, and legal concerns concerning future trends in human capital management through
AI technologies. For HR professionals and policymakers, future research directions and appropriate recommendations
on AI usage are provided
TIJER - INTERNATIONAL RESEARCH JOURNAL, 2021
Business processes in various industries are rapidly being transformed by artificial intelligence... more Business processes in various industries are rapidly being transformed by artificial intelligence (AI) and machine
learning (ML) technology, particularly in human resource management (HRM). In this paper, the article explores the
effect of AI-driven HR management on talent acquisition through predictive analytics and machine learning utilized in
predicting a candidate’s propensity to join the organization using the application of AI in HR management. This research
explores the benefits and drawbacks of AI adoption in HR recruitment via a general analysis of literature and case studies
through the improvements of efficiency, accuracy, and experience of potential candidates. Furthermore, the implications
of these technologies in regard to decision-making and narrowing bias in the recruitment process are explored. Industry
case study empirical evidence demonstrates AI has the power to revolutionize talent acquisition and how organizations
can leverage it to predict candidate success, automate recruitment workflows, and make data-informed hires. Finally,
this research concludes with recommendations on how to incorporate AI in HR systems and directions for future research
in AI-based HR management
International Journal of Creative Research Thoughts, 2021
When automation is driving the competitiveness of your business, organizations need ML to optimiz... more When automation is driving the competitiveness of your business, organizations need ML to optimize their
process. Process automation is vital within enterprise systems such as SAP to decrease manual interference,
raise accuracy, and enhance operational flexibility. This paper investigates how to integrate ML into the SAP
frameworks to automate core business functions, such as predictive analytics, anomaly detection, and decision
processes. This study offers a framework of process automation using contemporary ML algorithms applied to
process data, which exploits SAP's inherent capabilities for optimization. The research is to review existing
literature on SAP’s capacity to automate and implement ML and the challenges in implementation. The paper
further details a complete methodology to embed ML models into SAP’s enterprise systems and discusses case
studies for implementations. Performance metrics and optimization outcomes are referenced via graphs and
tables as visuals. Finally, this study concludes with future research directions and the possibility of future
advancements in ML-enabled SAP process automation.
International Journal of All Research Education and Scientific Methods, 2021
This study evaluates the data privacy and security frameworks in SAP ERP HCM ECC6 and the issues ... more This study evaluates the data privacy and security frameworks in SAP ERP HCM ECC6 and the issues that affect
compliance and risk management globally. The multifold enhancement in the complexity of the regulations, for
instance, the GDPR in Europe and the CCPA in the United States, has made it a challenge for multinational firms to
effectively maintain the compliance of their human capital management systems concerning data privacy laws. Some
of the inherent security features of the SAP ERP HCM ECC6 include encryption, role-based access controls, and
audit trails to help prevent risks. Nevertheless, such systems have several drawbacks because of improper settings,
changing legal frameworks, and difficulty coordinating data management across legal borders. This paper aims to
elaborateon the challenges that organizations face and provides a comprehensive guide to best practices to achieve
compliance, minimize risk, and enhance the configuration of SAP ERP HCM. The study also provides a set of
practical implications for strengthening security architectures and identifies further research opportunities for
increasing data protection in enterprise systems
World Journal of Advanced Research and Reviews, 2019
Over the last five years, predictive analytics and machine learning have been integrated into ERP... more Over the last five years, predictive analytics and machine learning have been integrated into ERP systems, mainly SAP
ERP HCM ECC6, to improve the strategic HCM. This has made organizations use big data and machine learning
algorithms in making the right decisions in talent acquisition, workforce planning, and employee retention. The current
paper aims to establish how and to what extent predictive analytics and machine learning can be applied in the SAP ERP
HCM ECC6 to enhance the HCM process. A literature review of the current literature is performed to evaluate the
feasibility of using predictive analytics and machine learning in improving decision-making based on HCM data. The
paper also provides a framework for adopting these tools within ERP systems and presents conclusions, research
implications, and suggestions for future research. The research outcomes of this study indicate that predictive analytics
and machine learning are useful in the decision-making process in an organization in that they provide information on
employee performance, turnover, and planning for the workforce. These technologies have practical applications in
talent management, payroll, and employee engagement and, therefore, improve the HCM processes. Figures in tables
and graphs are included to support the discussion of the ideas and results. -Finally, the study's future research directions
and practical implications are discussed, focusing on the competitive benefit of intelligent data analysis
ICONIC RESEARCH AND ENGINEERING JOURNALS, 2019
In the era of Industry 4.0, the integration
of Artificial Intelligence (AI) and Internet of Thing... more In the era of Industry 4.0, the integration
of Artificial Intelligence (AI) and Internet of Things
(IoT) technologies has revolutionized industrial
maintenance practices. This paper presents a
comprehensive review and analysis of AI-driven
predictive maintenance in IoT-enabled industrial
systems. We explore the synergies between AI
algorithms and IoT sensor networks in predicting
equipment failures, optimizing maintenance
schedules, and enhancing overall system reliability.
The study covers various AI techniques, including
machine learning, deep learning, and reinforcement
learning, applied to predictive maintenance. We also
discuss the challenges and opportunities in
implementing these technologies across different
industrial sectors. Our findings indicate that AIdriven predictive maintenance significantly reduces
downtime, cuts maintenance costs, and improves the
longevity of industrial equipment. The paper
concludes with future research directions and
potential implications for industry practitioners.
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Papers by Thejaswi Adimulam
development of sophisticated deep learning models. However, these models often require vast amounts of labeled
data to achieve high performance, which poses a significant challenge in low-resource scenarios. This paper explores
the application of transfer learning techniques in NLP to address the challenges associated with low-resource
languages and domains. We provide a comprehensive review of current transfer learning approaches in NLP,
including pre-training methods, cross-lingual transfer, and domain adaptation. Additionally, we present a novel
framework that combines adversarial training with multi-task learning to enhance the effectiveness of transfer
learning in low-resource settings. Our experimental results demonstrate the efficacy of this approach across various
NLP tasks, including machine translation, named entity recognition, and sentiment analysis. The proposed method
shows particular promise in scenarios where labeled data is scarce, outperforming existing baselines by a significant
margin. This research contributes to the ongoing efforts to democratize NLP technologies and make them accessible
to a wider range of languages and domains
amounts of heterogeneous data at unprecedented rates. This surge in data volume and velocity presents both
opportunities and challenges for data analytics. Cloud computing environments offer a promising solution for
processing and analyzing IoT data due to their scalability and resource elasticity. This paper presents a
comprehensive review and analysis of scalable machine learning models designed for IoT data analytics in
cloud environments. We explore the synergies between IoT, cloud computing, and machine learning,
discussing the challenges of processing IoT data at scale and the advantages of cloud-based solutions. The
paper examines various machine learning algorithms and architectures optimized for cloud deployment,
including distributed learning frameworks, federated learning, and edge-cloud collaborative models. We also
present case studies demonstrating the application of these models in real-world IoT scenarios, such as smart
cities, industrial IoT, and healthcare. Our findings highlight the importance of scalable machine learning
models in extracting valuable insights from IoT data and the role of cloud environments in enabling efficient,
large-scale data analytics
integrate all the processes of an organization. As new technologies continue to come into the market, including AI, IoT,
and Blockchain, ERP systems are in the process of being disrupted. This paper further discusses how these technologies
can be incorporated into the next generation of ERP systems to transform business automation, decision-making, and
operations. By identifying how AI can support predictive analytics, IoT for real-time data capture, and Blockchain for
secure, decentralized record keeping, this paper offers an understanding of how new generation ERP influences business
processes. The selected early adopters, technological frameworks, and industry best practices are discussed so the reader
can understand how these innovations can be applied to gain better operational efficiency and competitiveness.
Capital Management (HCM) system. As such, this paper focuses on developing Workday’s AI-related features,
particularly its capacity to revolutionize some core processes within workforce management, such as recruitment,
employee engagement, and overall workforce planning. Using generative AI in Workday for prediction, enhanced
employee experience, and future-oriented workforce management, companies can improve their operational outcomes
and achieve better talent acquisition strategies in a fiercely competitive global environment. The paper also discusses
ethical issues, privacy preservation, and legal concerns concerning future trends in human capital management through
AI technologies. For HR professionals and policymakers, future research directions and appropriate recommendations
on AI usage are provided
learning (ML) technology, particularly in human resource management (HRM). In this paper, the article explores the
effect of AI-driven HR management on talent acquisition through predictive analytics and machine learning utilized in
predicting a candidate’s propensity to join the organization using the application of AI in HR management. This research
explores the benefits and drawbacks of AI adoption in HR recruitment via a general analysis of literature and case studies
through the improvements of efficiency, accuracy, and experience of potential candidates. Furthermore, the implications
of these technologies in regard to decision-making and narrowing bias in the recruitment process are explored. Industry
case study empirical evidence demonstrates AI has the power to revolutionize talent acquisition and how organizations
can leverage it to predict candidate success, automate recruitment workflows, and make data-informed hires. Finally,
this research concludes with recommendations on how to incorporate AI in HR systems and directions for future research
in AI-based HR management
process. Process automation is vital within enterprise systems such as SAP to decrease manual interference,
raise accuracy, and enhance operational flexibility. This paper investigates how to integrate ML into the SAP
frameworks to automate core business functions, such as predictive analytics, anomaly detection, and decision
processes. This study offers a framework of process automation using contemporary ML algorithms applied to
process data, which exploits SAP's inherent capabilities for optimization. The research is to review existing
literature on SAP’s capacity to automate and implement ML and the challenges in implementation. The paper
further details a complete methodology to embed ML models into SAP’s enterprise systems and discusses case
studies for implementations. Performance metrics and optimization outcomes are referenced via graphs and
tables as visuals. Finally, this study concludes with future research directions and the possibility of future
advancements in ML-enabled SAP process automation.
compliance and risk management globally. The multifold enhancement in the complexity of the regulations, for
instance, the GDPR in Europe and the CCPA in the United States, has made it a challenge for multinational firms to
effectively maintain the compliance of their human capital management systems concerning data privacy laws. Some
of the inherent security features of the SAP ERP HCM ECC6 include encryption, role-based access controls, and
audit trails to help prevent risks. Nevertheless, such systems have several drawbacks because of improper settings,
changing legal frameworks, and difficulty coordinating data management across legal borders. This paper aims to
elaborateon the challenges that organizations face and provides a comprehensive guide to best practices to achieve
compliance, minimize risk, and enhance the configuration of SAP ERP HCM. The study also provides a set of
practical implications for strengthening security architectures and identifies further research opportunities for
increasing data protection in enterprise systems
ERP HCM ECC6, to improve the strategic HCM. This has made organizations use big data and machine learning
algorithms in making the right decisions in talent acquisition, workforce planning, and employee retention. The current
paper aims to establish how and to what extent predictive analytics and machine learning can be applied in the SAP ERP
HCM ECC6 to enhance the HCM process. A literature review of the current literature is performed to evaluate the
feasibility of using predictive analytics and machine learning in improving decision-making based on HCM data. The
paper also provides a framework for adopting these tools within ERP systems and presents conclusions, research
implications, and suggestions for future research. The research outcomes of this study indicate that predictive analytics
and machine learning are useful in the decision-making process in an organization in that they provide information on
employee performance, turnover, and planning for the workforce. These technologies have practical applications in
talent management, payroll, and employee engagement and, therefore, improve the HCM processes. Figures in tables
and graphs are included to support the discussion of the ideas and results. -Finally, the study's future research directions
and practical implications are discussed, focusing on the competitive benefit of intelligent data analysis
of Artificial Intelligence (AI) and Internet of Things
(IoT) technologies has revolutionized industrial
maintenance practices. This paper presents a
comprehensive review and analysis of AI-driven
predictive maintenance in IoT-enabled industrial
systems. We explore the synergies between AI
algorithms and IoT sensor networks in predicting
equipment failures, optimizing maintenance
schedules, and enhancing overall system reliability.
The study covers various AI techniques, including
machine learning, deep learning, and reinforcement
learning, applied to predictive maintenance. We also
discuss the challenges and opportunities in
implementing these technologies across different
industrial sectors. Our findings indicate that AIdriven predictive maintenance significantly reduces
downtime, cuts maintenance costs, and improves the
longevity of industrial equipment. The paper
concludes with future research directions and
potential implications for industry practitioners.
development of sophisticated deep learning models. However, these models often require vast amounts of labeled
data to achieve high performance, which poses a significant challenge in low-resource scenarios. This paper explores
the application of transfer learning techniques in NLP to address the challenges associated with low-resource
languages and domains. We provide a comprehensive review of current transfer learning approaches in NLP,
including pre-training methods, cross-lingual transfer, and domain adaptation. Additionally, we present a novel
framework that combines adversarial training with multi-task learning to enhance the effectiveness of transfer
learning in low-resource settings. Our experimental results demonstrate the efficacy of this approach across various
NLP tasks, including machine translation, named entity recognition, and sentiment analysis. The proposed method
shows particular promise in scenarios where labeled data is scarce, outperforming existing baselines by a significant
margin. This research contributes to the ongoing efforts to democratize NLP technologies and make them accessible
to a wider range of languages and domains
amounts of heterogeneous data at unprecedented rates. This surge in data volume and velocity presents both
opportunities and challenges for data analytics. Cloud computing environments offer a promising solution for
processing and analyzing IoT data due to their scalability and resource elasticity. This paper presents a
comprehensive review and analysis of scalable machine learning models designed for IoT data analytics in
cloud environments. We explore the synergies between IoT, cloud computing, and machine learning,
discussing the challenges of processing IoT data at scale and the advantages of cloud-based solutions. The
paper examines various machine learning algorithms and architectures optimized for cloud deployment,
including distributed learning frameworks, federated learning, and edge-cloud collaborative models. We also
present case studies demonstrating the application of these models in real-world IoT scenarios, such as smart
cities, industrial IoT, and healthcare. Our findings highlight the importance of scalable machine learning
models in extracting valuable insights from IoT data and the role of cloud environments in enabling efficient,
large-scale data analytics
integrate all the processes of an organization. As new technologies continue to come into the market, including AI, IoT,
and Blockchain, ERP systems are in the process of being disrupted. This paper further discusses how these technologies
can be incorporated into the next generation of ERP systems to transform business automation, decision-making, and
operations. By identifying how AI can support predictive analytics, IoT for real-time data capture, and Blockchain for
secure, decentralized record keeping, this paper offers an understanding of how new generation ERP influences business
processes. The selected early adopters, technological frameworks, and industry best practices are discussed so the reader
can understand how these innovations can be applied to gain better operational efficiency and competitiveness.
Capital Management (HCM) system. As such, this paper focuses on developing Workday’s AI-related features,
particularly its capacity to revolutionize some core processes within workforce management, such as recruitment,
employee engagement, and overall workforce planning. Using generative AI in Workday for prediction, enhanced
employee experience, and future-oriented workforce management, companies can improve their operational outcomes
and achieve better talent acquisition strategies in a fiercely competitive global environment. The paper also discusses
ethical issues, privacy preservation, and legal concerns concerning future trends in human capital management through
AI technologies. For HR professionals and policymakers, future research directions and appropriate recommendations
on AI usage are provided
learning (ML) technology, particularly in human resource management (HRM). In this paper, the article explores the
effect of AI-driven HR management on talent acquisition through predictive analytics and machine learning utilized in
predicting a candidate’s propensity to join the organization using the application of AI in HR management. This research
explores the benefits and drawbacks of AI adoption in HR recruitment via a general analysis of literature and case studies
through the improvements of efficiency, accuracy, and experience of potential candidates. Furthermore, the implications
of these technologies in regard to decision-making and narrowing bias in the recruitment process are explored. Industry
case study empirical evidence demonstrates AI has the power to revolutionize talent acquisition and how organizations
can leverage it to predict candidate success, automate recruitment workflows, and make data-informed hires. Finally,
this research concludes with recommendations on how to incorporate AI in HR systems and directions for future research
in AI-based HR management
process. Process automation is vital within enterprise systems such as SAP to decrease manual interference,
raise accuracy, and enhance operational flexibility. This paper investigates how to integrate ML into the SAP
frameworks to automate core business functions, such as predictive analytics, anomaly detection, and decision
processes. This study offers a framework of process automation using contemporary ML algorithms applied to
process data, which exploits SAP's inherent capabilities for optimization. The research is to review existing
literature on SAP’s capacity to automate and implement ML and the challenges in implementation. The paper
further details a complete methodology to embed ML models into SAP’s enterprise systems and discusses case
studies for implementations. Performance metrics and optimization outcomes are referenced via graphs and
tables as visuals. Finally, this study concludes with future research directions and the possibility of future
advancements in ML-enabled SAP process automation.
compliance and risk management globally. The multifold enhancement in the complexity of the regulations, for
instance, the GDPR in Europe and the CCPA in the United States, has made it a challenge for multinational firms to
effectively maintain the compliance of their human capital management systems concerning data privacy laws. Some
of the inherent security features of the SAP ERP HCM ECC6 include encryption, role-based access controls, and
audit trails to help prevent risks. Nevertheless, such systems have several drawbacks because of improper settings,
changing legal frameworks, and difficulty coordinating data management across legal borders. This paper aims to
elaborateon the challenges that organizations face and provides a comprehensive guide to best practices to achieve
compliance, minimize risk, and enhance the configuration of SAP ERP HCM. The study also provides a set of
practical implications for strengthening security architectures and identifies further research opportunities for
increasing data protection in enterprise systems
ERP HCM ECC6, to improve the strategic HCM. This has made organizations use big data and machine learning
algorithms in making the right decisions in talent acquisition, workforce planning, and employee retention. The current
paper aims to establish how and to what extent predictive analytics and machine learning can be applied in the SAP ERP
HCM ECC6 to enhance the HCM process. A literature review of the current literature is performed to evaluate the
feasibility of using predictive analytics and machine learning in improving decision-making based on HCM data. The
paper also provides a framework for adopting these tools within ERP systems and presents conclusions, research
implications, and suggestions for future research. The research outcomes of this study indicate that predictive analytics
and machine learning are useful in the decision-making process in an organization in that they provide information on
employee performance, turnover, and planning for the workforce. These technologies have practical applications in
talent management, payroll, and employee engagement and, therefore, improve the HCM processes. Figures in tables
and graphs are included to support the discussion of the ideas and results. -Finally, the study's future research directions
and practical implications are discussed, focusing on the competitive benefit of intelligent data analysis
of Artificial Intelligence (AI) and Internet of Things
(IoT) technologies has revolutionized industrial
maintenance practices. This paper presents a
comprehensive review and analysis of AI-driven
predictive maintenance in IoT-enabled industrial
systems. We explore the synergies between AI
algorithms and IoT sensor networks in predicting
equipment failures, optimizing maintenance
schedules, and enhancing overall system reliability.
The study covers various AI techniques, including
machine learning, deep learning, and reinforcement
learning, applied to predictive maintenance. We also
discuss the challenges and opportunities in
implementing these technologies across different
industrial sectors. Our findings indicate that AIdriven predictive maintenance significantly reduces
downtime, cuts maintenance costs, and improves the
longevity of industrial equipment. The paper
concludes with future research directions and
potential implications for industry practitioners.