Journal of Computational Intelligence and Robotics
type: channel
description: The "Journal of Computational Intelligence and Robotics" (JCIR) stands as a pioneering platform at the forefront of interdisciplinary research, marrying the realms of computational intelligence and robotics to foster innovation and transformative breakthroughs. With a relentless pursuit of excellence, JCIR endeavors to push the boundaries of knowledge in these dynamic fields, serving as a beacon for scholars, researchers, and practitioners alike. Scope: JCIR welcomes high-quality contributions spanning a wide spectrum of topics within computational intelligence and robotics, including but not limited to: Machine learning algorithms and techniques Evolutionary computation and optimization Swarm intelligence and collective robotics Cognitive robotics and artificial intelligence Autonomous systems and robotics Human-robot interaction and collaboration Intelligent control and automation Applications of computational intelligence in robotics, such as healthcare, manufacturing, transportation, and more. Mission: Our mission at JCIR is to advance the frontiers of computational intelligence and robotics through rigorous research, innovation, and collaboration. We strive to facilitate interdisciplinary exchange, foster academic discourse, and disseminate cutting-edge research to a global audience. By providing a platform for the dissemination of high-quality research, JCIR aims to drive innovation, inspire creativity, and contribute to the collective advancement of these dynamic fields. Target Audience: JCIR caters to a diverse audience of researchers, academics, industry professionals, and students with a keen interest in computational intelligence and robotics. Our audience includes but is not limited to: Researchers and scholars seeking to publish innovative research findings and insights. Practitioners and engineers involved in the development and deployment of intelligent robotic systems. Educators and students interested in staying abreast of the latest advancements in…
publisher: The Science Brigade Publishers
language: en
publication name: Journal of Computational Intelligence and Robotics
issn: 2584-1483
copyright: License Terms Ownership and Licensing: Authors of this research paper submitted to the journal owned and operated by The Science Brigade Group retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License. License Permissions: Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal. This license allows for the broad dissemination and utilization of research papers. Additional Distribution Arrangements: Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in this Journal. Online Posting: Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal. Online sharing enhances the visibility and accessibility of the research papers. Responsibility and Liability: Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.
items: Dynamic Scaling of Machine Learning Workloads: A Comparative Study of On-Prem and Cloud-Based Containers
items: Energy Efficiency in Smart Factories: Leveraging IoT, AI, and Cloud Computing for Sustainable Manufacturing
items: Exploring Edge Computing Integrations with AWS CloudFront
items: Optimizing Elastic Kubernetes Services for High Availability Applications
items: Reinforcement Learning from Human Feedback for Enhanced Code Generation and Debugging Capabilities in LLMs
items: Multi-Cloud Security Event Aggregation and Normalization Using Advanced AI/ML Techniques
items: Disaster Recovery Strategies for Cloud-Based Insurance Platforms: Building Resilience and Ensuring Data Security
items: Chaining AI Agents in PaaS Architectures for Multi-Step Workflow Automation
items: Attribute-Based Access Control Frameworks for Granular Data Access in Cloud-Based Insurance Systems
items: Integrating Reinforcement Learning into Business Process Mining for Continuous Process Adaptation and Optimization
items: The Role of Cross-Functional Collaboration in Digital Transformation
items: Developing A Strategic Roadmap For Digital Transformation
items: Cloud Transformation for Mobile Products: Leveraging AI to Automate Infrastructure Management, Scalability, and Cost Efficiency
items: Cybersecurity in Digital Transformation: Using AI to Automate Threat Detection and Response in Multi-Cloud Infrastructures
items: Combining Machine Learning and RAG Models for Enhanced Data Retrieval: Applications in Search Engines, Enterprise Data Systems, and Recommendations
items: Predictive Maintenance in Automotive Telematics Using Machine Learning Algorithms for Enhanced Reliability and Cost Reduction
items: Enhancing Automotive Safety and Efficiency through AI/ML-Driven Telematics Solutions
items: AI-Driven Solutions for Enhancing Data Flow to Common Platforms in Healthcare: Techniques, Standards, and Best Practices
items: Big Data Integration in the Insurance Industry: Enhancing Underwriting and Fraud Detection
items: Real-Time Data Integration in Retail: Improving Supply Chain and Customer Experience
items: ETL vs. ELT: Optimizing Data Integration for Retail and Insurance Analytics
items: Utilizing Transformers for Interactive Chatbot Development
items: Machine Learning for Enhancing Vehicle Safety and Collision Avoidance Systems in Automotive Development: Techniques, Models, and Real-World Applications
items: Machine Learning-Based Systems for Real-Time Traffic Prediction and Management in Automotive Development: Techniques, Models, and Applications
items: Enhancing Customer Service Automation and User Satisfaction: An Exploration of AI-powered Chatbot Implementation within Customer Relationship Management Systems
items: Advanced AI Techniques for Real-Time Anomaly Detection and Incident Response in DevOps Environments: Ensuring Robust Security and Compliance
items: Human-in-the-Loop Moral Decision-Making Frameworks for Situationally Aware Multi-Modal Autonomous Vehicle Networks: An Accessibility-Focused Approach
items: Epidemiology and Oral Health: Harnessing Big Data for Monitoring and Predicting Epidemic Outbreaks
items: Automated Underwriting Systems: Advancements and Challenges in the Age of AI
items: AI and Machine Learning in Healthcare Robotics: Exploring AI-driven algorithms and their role in addressing healthcare challenges, including task optimization and patient care

Dynamic Scaling of Machine Learning Workloads: A Comparative Study of On-Prem and Cloud-Based Containers
type: item
description: This work aims to provide a thorough study of dynamic scaling mechanism for machine learning (ML) workloads, thereby stressing the operational trade-offs between on-site and cloud-based containerized systems. Under different workloads, this study primarily addresses performance elasticity, resource consumption efficiency, orchestration delay, and cost-effectiveness.
subject: Articles
  topic: section
subject: dynamic scaling
  topic: keywords
subject: machine learning workloads
  topic: keywords
subject: containers
  topic: keywords
subject: Kubernetes
  topic: keywords
subject: resource orchestration
  topic: keywords
creator: Vinay Kumar Deeti
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-08-09
publication date: 2023-08-09
starting page: 123
ending page: 137

Energy Efficiency in Smart Factories: Leveraging IoT, AI, and Cloud Computing for Sustainable Manufacturing
type: item
description: The rapid evolution of smart factories has necessitated innovative approaches to mitigate excessive energy consumption while maintaining operational efficiency. This research explores the integration of the Internet of Things (IoT), artificial intelligence (AI), and cloud computing to enhance energy efficiency in smart manufacturing environments. IoT-enabled sensors facilitate real-time energy monitoring, while AI-driven analytics optimize production processes through predictive maintenance and adaptive control strategies. Cloud-based platforms enable scalable data storage and computational capabilities, fostering interoperability and centralized decision-making for energy management. The paper examines the challenges associated with implementing these technologies, including data security, interoperability constraints, and computational overhead. Case studies highlight the effectiveness of AI-augmented energy optimization frameworks in reducing energy waste and improving sustainability. The findings underscore the transformative potential of these technologies in fostering energy-efficient, cost-effective, and environmentally sustainable manufacturing ecosystems.
subject: Articles
  topic: section
subject: energy efficiency
  topic: keywords
subject: smart factories
  topic: keywords
subject: IoT
  topic: keywords
subject: AI
  topic: keywords
creator: Hassan Rehan
rights: Copyright (c) 2021 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2021-06-25
publication date: 2021-06-25
starting page: 18
ending page: 55

Exploring Edge Computing Integrations with AWS CloudFront
type: item
description: Edge computing is turn out to be a revolutionary model in the distributed computing which enables low-latency processing and enhanced performance for modern cloud architectures. Amazon Web Services (AWS) CloudFront Is a globally distributed content delivery network (CDN) which provides a robust infrastructure for integrated edge computing with Achieved by integrating cloud-based applications. This paper aims to explore the architectural principles, technical synergies, and performance enhancements achieved by integrating edge computing with AWS CloudFront.
subject: Articles
  topic: section
subject: edge computing
  topic: keywords
subject: AWS CloudFront
  topic: keywords
creator: Venkata Ramana Gudelli
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-06-13
publication date: 2022-06-13
starting page: 221
ending page: 245

Optimizing Elastic Kubernetes Services for High Availability Applications
type: item
description: Cloud native architectures are adapted rapidly which makes the deployment highly available, resilient, and scalable applications on Kubernetes. Elastic Kubernetes Services (EKS) provides an automated managed environment that facilitates dynamic resource allocation and workload distribution. But optimization of EKS for high availability requires precise tuning of cluster configurations, node auto-scaling policies, and fault tolerance mechanisms. The purpose of this paper is to examine the key architectural components which influences the EKS performance which includes pod disruption budgets, horizontal and vertical scaling strategies, and multi-region failover techniques.
subject: Articles
  topic: section
subject: Kubernetes
  topic: keywords
subject: Elastic Kubernetes Service
  topic: keywords
creator: Venkata Ramana Gudelli
rights: Copyright (c) 2021 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2021-10-22
publication date: 2021-10-22
starting page: 64
ending page: 88

Reinforcement Learning from Human Feedback for Enhanced Code Generation and Debugging Capabilities in LLMs
type: item
description: Reinforcement learning from human feedback (RLHF) has emerged as a transformative paradigm for improving the capabilities of large language models (LLMs) in code generation and debugging. This research focuses on the development and optimization of RLHF pipelines to enhance the coding accuracy of LLMs by integrating human feedback mechanisms. Traditional machine learning approaches, while effective in generating syntactically correct code, often fail to address nuanced requirements, logical errors, or adherence to best practices. By incorporating human feedback loops into reinforcement learning frameworks, LLMs can iteratively refine their outputs, achieving higher levels of correctness, efficiency, and language-agnostic applicability. This paper delineates the architecture of RLHF-based pipelines, emphasizing key components such as reward modeling, policy optimization, and the curation of diverse feedback datasets. A critical aspect of this framework involves designing reward signals that balance syntactic correctness, semantic coherence, and execution reliability, informed by human evaluators’ domain expertise. The integration of domain-specific knowledge within the RLHF paradigm further facilitates the generation of robust, context-aware code, which is instrumental in automating complex programming tasks across diverse environments. A significant portion of the research focuses on debugging workflows, where RLHF is employed to optimize error identification and correction processes. Traditional LLMs frequently overlook edge cases or fail to resolve multi-layered dependencies in large-scale codebases. Through iterative reinforcement, incorporating human-in-the-loop strategies, these limitations are systematically addressed. The feedback loop is operationalized by combining static analysis tools, runtime feedback, and expert human annotations, creating a synergistic mechanism for fine-tuning LLM behavior. Case studies demonstrate the application of this methodology …
subject: Articles
  topic: section
subject: reinforcement learning from human feedback
  topic: keywords
subject: RLHF
  topic: keywords
subject: large language models
  topic: keywords
creator: Aarthi Anbalagan
creator: Muthuraman Saminathan
creator: Vincent Kanka
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-04-10
publication date: 2024-04-10
starting page: 152
ending page: 193

Multi-Cloud Security Event Aggregation and Normalization Using Advanced AI/ML Techniques
type: item
description: The proliferation of multi-cloud environments has introduced a multitude of challenges for cybersecurity, particularly in aggregating, normalizing, and deduplicating security event data across diverse platforms. This research explores the utilization of Natural Language Processing (NLP) and advanced machine learning (ML) models to address these challenges, focusing on the implementation of sophisticated techniques in three major cloud ecosystems: AWS Security Hub, Google Chronicle, and Azure Sentinel. The central premise of this study is the development of a unified framework that employs AI-driven methods to standardize heterogeneous security logs, identify redundancies, and enhance the efficacy of threat detection and response mechanisms. The paper begins with a comprehensive overview of security log generation in multi-cloud environments, highlighting the complexity and heterogeneity of log formats, schemas, and data volumes. The study identifies key obstacles in achieving seamless log aggregation and normalization, including semantic inconsistencies, variations in data syntax, and the presence of redundant or irrelevant entries. By addressing these issues, organizations can significantly enhance their ability to detect, analyze, and respond to security threats in a timely and efficient manner. To tackle these challenges, the research employs advanced NLP techniques, such as contextual embedding models like BERT and GPT variants, to parse, understand, and standardize log data from different cloud platforms. These models are used to extract meaningful insights and harmonize security event descriptions, ensuring consistency across logs originating from AWS, Google Cloud, and Azure. Additionally, the study integrates ML-based anomaly detection and clustering algorithms to identify and eliminate duplicate events, reducing noise in the data and improving signal-to-noise ratios for security teams. A core contribution of this paper is the detailed implementation and eva…
subject: Articles
  topic: section
subject: Multi-cloud security
  topic: keywords
subject: AI/ML techniques
  topic: keywords
subject: NLP in cybersecurity
  topic: keywords
creator: Muthuraman Saminathan
creator: Vincent Kanka
creator: Akhil Reddy Bairi
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-09-19
publication date: 2022-09-19
starting page: 28
ending page: 62

Disaster Recovery Strategies for Cloud-Based Insurance Platforms: Building Resilience and Ensuring Data Security
type: item
description: The proliferation of cloud-based platforms in the insurance industry has introduced transformative efficiencies in operations, scalability, and customer service. However, these platforms are increasingly exposed to risks from natural disasters, cyberattacks, and system failures, necessitating robust disaster recovery strategies to ensure operational continuity and data security. This research paper explores advanced disaster recovery strategies specifically tailored for cloud-based insurance platforms, emphasizing cross-region disaster recovery planning, backup encryption mechanisms, and the importance of regular failover testing. The study investigates the critical challenges faced by cloud-based insurance systems, including regulatory compliance, data integrity, latency concerns during failover, and resource allocation for disaster recovery (DR) initiatives. The cornerstone of disaster recovery in cloud environments lies in cross-region replication and failover strategies, which enable insurers to minimize downtime and maintain service availability during regional outages. This paper provides an in-depth analysis of cross-region disaster recovery models, including active-active and active-passive configurations, while discussing their implications on latency, cost, and operational complexity. Backup encryption is another pivotal component in safeguarding sensitive insurance data from unauthorized access during disaster recovery operations. This research evaluates state-of-the-art encryption protocols, such as Advanced Encryption Standard (AES) and homomorphic encryption, examining their applicability and effectiveness in meeting industry-specific compliance standards such as GDPR, HIPAA, and PCI DSS. Regular failover testing is crucial in validating the reliability of disaster recovery plans. The paper outlines best practices for conducting failover simulations in a production-like environment to identify potential vulnerabilities and ensure that recovery time obj…
subject: Articles
  topic: section
subject: disaster recovery
  topic: keywords
subject: cloud-based insurance platforms
  topic: keywords
subject: cross-region replication
  topic: keywords
creator: Debabrata Das
creator: Aarthi Anbalagan
creator: Muthuraman Saminathan
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-04-09
publication date: 2022-04-09
starting page: 173
ending page: 220

Chaining AI Agents in PaaS Architectures for Multi-Step Workflow Automation
type: item
description: The growing complexity of modern IT systems necessitates innovative approaches to workflow automation, especially in Platform-as-a-Service (PaaS) architectures. This research focuses on the chaining of artificial intelligence (AI) agents, including task-specific models, large language models (LLMs), and decision-making algorithms, to facilitate the automation of multi-step workflows in cloud-native environments. By leveraging frameworks such as LangChain, the integration of heterogeneous AI agents into cohesive multi-agent systems becomes feasible, enabling the decomposition and resolution of intricate tasks across various domains. The paper elaborates on the architectural design principles, interoperability challenges, and optimization techniques involved in chaining AI agents within PaaS ecosystems. Specifically, it explores methods for orchestrating AI agents to achieve modularity, scalability, and fault tolerance, which are critical for supporting dynamic and distributed workflows. A key focus is on how AI-driven orchestration tools ensure efficient task allocation and execution by dynamically selecting and connecting relevant agents based on task-specific requirements. The discussion extends to cloud-native implementation strategies, emphasizing containerization, microservices, and the use of serverless architectures to deploy AI agents as scalable components. By adopting event-driven architectures, these multi-agent systems can efficiently respond to workflow triggers, minimizing latency and maximizing throughput. Additionally, advanced techniques such as reinforcement learning and contextual reasoning are employed to enable agents to adapt their behavior based on real-time data, ensuring robust and context-aware decision-making. Real-world applications are demonstrated through case studies, with a particular focus on IT incident response workflows. These examples illustrate how chaining AI agents can expedite root cause analysis, generate automated remediatio…
subject: Articles
  topic: section
subject: AI agent chaining
  topic: keywords
subject: multi-agent systems
  topic: keywords
creator: Akhil Reddy Bairi
creator: Debabrata Das
creator: Aarthi Anbalagan
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-06-12
publication date: 2023-06-12
starting page: 242
ending page: 285

Attribute-Based Access Control Frameworks for Granular Data Access in Cloud-Based Insurance Systems
type: item
description: The rapid adoption of cloud-based infrastructure in the insurance sector has intensified the need for robust access control mechanisms to manage sensitive datasets securely. Traditional access control models, such as Role-Based Access Control (RBAC) and Mandatory Access Control (MAC), exhibit limitations in addressing the dynamic and granular access requirements of modern insurance platforms. Attribute-Based Access Control (ABAC), characterized by its reliance on attributes—user, object, environmental, and contextual—emerges as a highly adaptable framework for managing access to sensitive information while adhering to stringent regulatory standards such as the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR). This paper investigates the integration of ABAC frameworks into cloud-based insurance systems to enable fine-grained, dynamic, and policy-driven access management. The study begins by delineating the key challenges faced by insurance providers in securing sensitive datasets, particularly in a multi-tenant cloud environment. These challenges include mitigating insider threats, ensuring compliance with complex regulatory requirements, and providing scalable access mechanisms without compromising system performance. The core contribution of this research is a detailed analysis of ABAC's operational principles and its application in insurance platforms. The ABAC model evaluates access requests based on multi-dimensional attributes, providing unparalleled granularity in defining and enforcing access policies. For instance, policies can be formulated to grant access to medical records only to licensed professionals during working hours or to restrict sensitive customer information based on geographical regulations. Such capabilities surpass the rigidity of RBAC, which depends solely on predefined roles. The paper also explores the role of advanced technologies, such as machine learning and natural language …
subject: Articles
  topic: section
subject: Attribute-Based Access Control (ABAC)
  topic: keywords
subject: granular data access
  topic: keywords
creator: Debabrata Das
creator: Vincent Kanka
creator: Manish Tomar
rights: Copyright (c) 2021 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2021-09-17
publication date: 2021-09-17
starting page: 24
ending page: 63

Integrating Reinforcement Learning into Business Process Mining for Continuous Process Adaptation and Optimization
type: item
description: This paper introduces a reinforcement learning (RL) framework for integrating reinforcement learning with business process mining (BPM), aiming to enable continuous adaptation and optimization of business processes in dynamic environments. The proposed approach leverages real-time process data to iteratively enhance process performance by applying RL algorithms that adapt business workflows based on evolving operational conditions. The framework is designed to address the limitations of traditional BPM, which often struggles to adjust in real-time to changing business needs, by enabling autonomous learning and decision-making. The paper explores how RL can be effectively employed to discover process inefficiencies, recommend process modifications, and improve resource allocation by adapting actions based on feedback from previous iterations. Additionally, it presents the integration of RL with process mining techniques, offering a comprehensive model for data-driven decision-making and process improvement. Through case studies and application scenarios, the framework’s potential to enhance operational efficiency, reduce costs, and improve process flexibility is demonstrated. The study concludes by discussing the challenges of applying RL in BPM, such as the need for high-quality data, model interpretability, and scalability, while also identifying future research avenues for the advancement of RL-driven BPM solutions in industry.
subject: Articles
  topic: section
subject: reinforcement learning
  topic: keywords
subject: business process mining
  topic: keywords
creator: Amish Doshi
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-07-05
publication date: 2022-07-05
starting page: 69
ending page: 79

The Role of Cross-Functional Collaboration in Digital Transformation
type: item
description: The rapid progression of digital transformation across industries has underscored the need for a robust and cohesive approach to organizational restructuring and technological adoption. However, the success of these transformative efforts is increasingly understood to be contingent upon effective cross-functional collaboration within organizations. This paper investigates the role of collaboration across diverse organizational functions—such as information technology (IT), human resources (HR), finance, operations, marketing, and research and development (R&D)—as a pivotal factor in driving successful digital transformation initiatives. Digital transformation, often aimed at integrating advanced digital technologies such as artificial intelligence, big data analytics, and cloud computing, involves profound shifts in organizational strategies, processes, and culture. In this context, cross-functional collaboration enables organizations to bridge siloed operational structures, facilitating the alignment of technological initiatives with core business objectives and enhancing decision-making processes. The research explores the theoretical underpinnings of cross-functional collaboration, with particular attention to its role in fostering agility, innovation, and resilience within organizations. By examining the dynamics of team interactions, shared knowledge, and resource allocation across departments, this paper seeks to delineate the mechanisms through which cross-functional collaboration contributes to the achievement of digital transformation goals. One of the primary challenges in digital transformation lies in the inherent complexity and uncertainty surrounding the integration of novel technologies. Cross-functional teams mitigate this complexity by drawing on diverse expertise, thereby improving risk assessment, accelerating the innovation process, and ensuring that transformation initiatives are grounded in comprehensive, organization-wide insights. The paper f…
subject: Articles
  topic: section
subject: cross-functional collaboration
  topic: keywords
subject: digital transformation
  topic: keywords
creator: Tanzeem Ahmad
creator: James Boit
creator: Ajay Aakula
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-04-12
publication date: 2023-04-12
starting page: 205
ending page: 242

Developing A Strategic Roadmap For Digital Transformation
type: item
description: Digital transformation has emerged as an essential driver for organizations seeking to adapt to rapidly evolving technological landscapes, operational demands, and competitive pressures. Developing a strategic roadmap for digital transformation enables an organization to navigate this complex journey by providing a structured, phased approach that aligns digital initiatives with organizational goals, core competencies, and market dynamics. This paper explores the comprehensive methodology for creating an effective strategic roadmap tailored to digital transformation, emphasizing the foundational principles, critical success factors, and tactical elements integral to this process. The research highlights key considerations in formulating digital transformation roadmaps, including the role of organizational vision and mission in shaping transformation objectives, the importance of stakeholder engagement and cross-functional collaboration, and the necessity of a robust governance structure to oversee digital initiatives. It also examines essential technical and operational metrics that should inform roadmap development, such as technology readiness, digital maturity assessments, and capabilities gap analyses, to ensure the alignment of transformation strategies with business objectives and resource capacities. The study advocates for a layered approach to strategic roadmap development that incorporates various stages of digital transformation, from digital enablement and optimization to innovation and digital reinvention, offering organizations a scalable, flexible framework adaptable to their specific contexts and maturity levels. This approach includes mapping the organization’s current technological landscape and identifying potential opportunities for digital intervention, such as automation, artificial intelligence, and data analytics, which can drive operational efficiency, improve customer experience, and unlock new value streams. Furthermore, the paper undersco…
subject: Articles
  topic: section
subject: digital transformation
  topic: keywords
subject: strategic roadmap
  topic: keywords
creator: Tanzeem Ahmad
creator: Ajay Aakula
creator: Marcus Ottori
creator: Vipin Saini
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-08-06
publication date: 2022-08-06
starting page: 28
ending page: 68

Cloud Transformation for Mobile Products: Leveraging AI to Automate Infrastructure Management, Scalability, and Cost Efficiency
type: item
description: In the rapidly evolving landscape of mobile product development, cloud transformation has emerged as a pivotal strategy for enhancing operational efficiencies, optimizing resource utilization, and ensuring scalability. The integration of artificial intelligence (AI) into cloud infrastructure management presents a transformative opportunity to automate complex processes that are traditionally labor-intensive and prone to human error. This research paper explores the multifaceted role of AI in automating infrastructure management, scalability, and cost efficiency within the context of cloud transformation for mobile products. The study begins by delineating the foundational concepts of cloud computing and mobile product architecture, emphasizing the significance of a robust cloud infrastructure in supporting mobile applications' performance and reliability. With the increasing demand for mobile applications to scale seamlessly, organizations face the challenge of maintaining performance standards while managing operational costs. Herein lies the relevance of AI, which offers advanced methodologies to analyze, predict, and adapt infrastructure requirements dynamically. AI techniques such as machine learning (ML) and natural language processing (NLP) are employed to enhance predictive analytics capabilities, enabling organizations to forecast infrastructure demands based on usage patterns and trends. This predictive approach not only facilitates proactive resource allocation but also minimizes downtime, thereby improving user experience. Furthermore, the implementation of AI-driven automation in cloud management significantly reduces the manual overhead associated with routine tasks such as provisioning, monitoring, and scaling resources. The paper also investigates various AI algorithms that contribute to cost efficiency through optimized resource management. By leveraging AI-driven insights, organizations can identify underutilized resources and reallocate them effect…
subject: Articles
  topic: section
subject: Cloud transformation
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: infrastructure management
  topic: keywords
creator: Seema Kumari
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-01-12
publication date: 2024-01-12
starting page: 130
ending page: 151

Cybersecurity in Digital Transformation: Using AI to Automate Threat Detection and Response in Multi-Cloud Infrastructures
type: item
description: The accelerating pace of digital transformation has led organizations to increasingly adopt multi-cloud infrastructures, which offer scalability, flexibility, and cost efficiency. However, these infrastructures also introduce significant security challenges, particularly in terms of managing and mitigating the expanding attack surface. The complexity of securing such environments, coupled with the volume and sophistication of cyber threats, has rendered traditional security mechanisms inadequate. In response, artificial intelligence (AI) has emerged as a transformative technology, capable of automating threat detection and response processes, thereby enhancing security postures and reducing incident response times in multi-cloud environments. This paper investigates the application of AI in automating cybersecurity within multi-cloud infrastructures during digital transformation, exploring its ability to detect, analyze, and respond to sophisticated threats in real-time. The first part of the research focuses on the critical security challenges posed by multi-cloud infrastructures, particularly the heterogeneity of cloud platforms, disparate security controls, and the need for consistent visibility across environments. These challenges exacerbate the difficulty of threat detection and response, which is further compounded by the lack of centralized security governance and the increased vulnerability of cloud-native applications. The paper examines how the dynamic nature of cloud services, such as autoscaling and resource allocation, introduces security risks that traditional methods fail to adequately address. AI-driven threat detection systems leverage advanced machine learning (ML) algorithms, neural networks, and deep learning models to identify anomalous behavior and detect potential threats across multi-cloud environments. The research delves into how AI models can be trained to analyze vast amounts of data generated from various cloud platforms, such as Amazon…
subject: Articles
  topic: section
subject: artificial intelligence
  topic: keywords
subject: threat detection
  topic: keywords
subject: incident response
  topic: keywords
subject: multi-cloud infrastructure
  topic: keywords
creator: Seema Kumari
creator: Sahil Dhir
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-08-23
publication date: 2022-08-23
starting page: 9
ending page: 27

Combining Machine Learning and RAG Models for Enhanced Data Retrieval: Applications in Search Engines, Enterprise Data Systems, and Recommendations
type: item
description: This research paper explores the intersection of machine learning (ML) and retrieval-augmented generation (RAG) models to significantly enhance data retrieval processes in search engines, enterprise-level data systems, and recommendation engines. Data retrieval has become a critical function in high-demand environments where vast quantities of information must be processed and accessed in real time. Traditional retrieval models often rely on basic keyword matching and ranking algorithms, but they struggle with handling nuanced queries, user intent, and the increasing complexity of modern datasets. To address these challenges, this paper presents an in-depth analysis of how the integration of advanced machine learning techniques with RAG models can offer a more robust solution by improving query understanding, relevance scoring, and overall system performance. At the core of this integration is the ability of machine learning models to process vast amounts of unstructured and structured data while learning patterns in user behavior, preferences, and language. Machine learning models such as neural networks and deep learning architectures excel at extracting meaningful features from data, enabling the identification of complex relationships between user queries and datasets. RAG models further augment this process by combining retrieval mechanisms with generative models, thus enhancing the system’s ability to handle open-domain questions and queries that require context-sensitive answers. In this context, RAG models employ dense retrieval techniques to fetch relevant documents or data segments and then generate context-aware responses based on these retrieved items. The paper first outlines the technical workflow for integrating ML and RAG models, focusing on key processes such as query vectorization, the role of embeddings in semantic search, and the use of attention mechanisms to refine the relevance of results. RAG models offer a hybrid approach by connecting retri…
subject: Articles
  topic: section
subject: machine learning
  topic: keywords
subject: retrieval-augmented generation
  topic: keywords
subject: data retrieval
  topic: keywords
subject: search engines
  topic: keywords
creator: Jaswinder Singh
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-03-07
publication date: 2023-03-07
starting page: 163
ending page: 204

Predictive Maintenance in Automotive Telematics Using Machine Learning Algorithms for Enhanced Reliability and Cost Reduction
type: item
description: Predictive maintenance in automotive telematics, empowered by machine learning (ML) algorithms, represents a transformative advancement in vehicle management, offering significant enhancements in reliability and cost efficiency. The integration of ML techniques into telematics systems enables the real-time monitoring and analysis of vehicle performance data, facilitating the early detection of potential failures and optimizing maintenance schedules. This paper investigates the application of various ML algorithms within automotive telematics to predict and prevent vehicle malfunctions, ultimately aiming to improve operational reliability and reduce maintenance costs. Automotive telematics systems collect an extensive array of data from vehicle sensors, including parameters such as engine performance, fuel efficiency, tire pressure, and wear-and-tear metrics. Traditional maintenance approaches rely heavily on scheduled intervals or reactive repairs, which may not address underlying issues until they become critical. In contrast, predictive maintenance leverages historical data and ML algorithms to anticipate failures before they occur, enabling more precise and proactive maintenance interventions. This research delineates the methodological framework for implementing ML-based predictive maintenance systems. It begins by exploring the fundamental principles of ML algorithms, including supervised learning, unsupervised learning, and reinforcement learning, and their applicability to telematics data. Supervised learning algorithms, such as decision trees, random forests, and gradient boosting machines, are particularly effective in predictive maintenance for their ability to model complex relationships between vehicle data features and failure outcomes. Additionally, unsupervised learning techniques, such as clustering and anomaly detection, provide insights into unusual patterns or deviations that may signal emerging issues. Reinforcement learning, though less commonly…
subject: Articles
  topic: section
subject: cost reduction
  topic: keywords
subject: machine learning algorithms
  topic: keywords
creator: Priya Ranjan Parida
creator: Chandan Jnana Murthy
creator: Deepak Venkatachalam
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-10-22
publication date: 2023-10-22
starting page: 44
ending page: 82

Enhancing Automotive Safety and Efficiency through AI/ML-Driven Telematics Solutions
type: item
description: The automotive industry is undergoing a significant transformation, driven by advancements in artificial intelligence (AI) and machine learning (ML) technologies. These innovations are increasingly being integrated into telematics solutions to enhance vehicle safety, fuel efficiency, and overall performance. This paper explores the current state and future prospects of AI/ML-driven telematics systems, focusing on their impact on automotive safety, operational efficiency, and performance optimization. Telematics, which combines telecommunications and monitoring systems, has evolved considerably with the advent of AI and ML. AI algorithms enable real-time data analysis from various vehicle sensors and external sources, leading to more intelligent and adaptive telematics solutions. ML models, particularly those utilizing supervised and unsupervised learning, are instrumental in predicting vehicle maintenance needs, optimizing fuel consumption, and enhancing driver safety. One of the critical applications of AI in telematics is the development of advanced driver-assistance systems (ADAS). These systems leverage computer vision and sensor fusion to provide features such as lane-keeping assistance, adaptive cruise control, and collision avoidance. By analyzing data from cameras, radar, and lidar, AI algorithms can make real-time decisions to assist drivers, thereby reducing the likelihood of accidents and improving overall road safety. In addition to safety, AI/ML-driven telematics systems play a pivotal role in enhancing fuel efficiency. Predictive maintenance, powered by machine learning, allows for the early detection of potential engine issues and the optimization of maintenance schedules. This proactive approach not only reduces the risk of breakdowns but also ensures that vehicles operate at peak efficiency. AI algorithms can also analyze driving patterns and suggest modifications to improve fuel consumption, thus contributing to reduced emissions and cost savings. …
subject: Articles
  topic: section
subject: edge computing
  topic: keywords
subject: fuel efficiency
  topic: keywords
creator: Amsa Selvaraj
creator: Priya Ranjan Parida
creator: Chandan Jnana Murthy
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-10-18
publication date: 2023-10-18
starting page: 82
ending page: 122

AI-Driven Solutions for Enhancing Data Flow to Common Platforms in Healthcare: Techniques, Standards, and Best Practices
type: item
description: In recent years, the integration of artificial intelligence (AI) into healthcare has emerged as a pivotal factor in enhancing data flow and interoperability across common platforms. This research paper investigates AI-driven solutions designed to optimize data exchange and ensure seamless integration within healthcare systems. The focus is on elucidating the various AI techniques, adherence to standards, and best practices that are essential for achieving effective data flow and interoperability. The healthcare sector grapples with significant challenges related to data silos, disparate systems, and varying standards that impede the efficient exchange of health information. AI has the potential to address these challenges by providing advanced tools and methodologies for data integration and management. This paper explores key AI techniques such as machine learning, natural language processing, and data mining, which facilitate the extraction, transformation, and integration of health data from heterogeneous sources. The application of these techniques in the context of healthcare interoperability is critically examined to highlight their contributions to improving data flow. Standards and protocols play a crucial role in enabling interoperability among diverse healthcare systems. This research delves into established standards such as Health Level Seven International (HL7), Fast Healthcare Interoperability Resources (FHIR), and Digital Imaging and Communications in Medicine (DICOM). The alignment of AI technologies with these standards is assessed to ensure that AI solutions can operate effectively within the existing framework of healthcare data exchange. Additionally, the paper discusses the challenges and limitations associated with the implementation of these standards and proposes strategies to overcome these barriers. Best practices for leveraging AI in enhancing data flow are also presented. These practices encompass data governance, privacy considerations, …
subject: Articles
  topic: section
subject: artificial intelligence
  topic: keywords
subject: data flow
  topic: keywords
subject: machine learning
  topic: keywords
subject: healthcare interoperability
  topic: keywords
creator: Navajeevan Pushadapu
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-04-13
publication date: 2022-04-13
starting page: 122
ending page: 172

Big Data Integration in the Insurance Industry: Enhancing Underwriting and Fraud Detection
type: item
description: In the evolving landscape of the insurance industry, the integration of big data has emerged as a pivotal factor in revolutionizing underwriting processes and enhancing fraud detection capabilities. This paper investigates the transformative role of big data integration within the insurance sector, focusing on how the convergence of large and diverse data sets can refine risk assessment methodologies and fortify fraud prevention mechanisms. The advent of big data technologies has enabled insurers to harness vast quantities of information from varied sources, such as transactional data, social media, telematics, and IoT devices, thereby providing a comprehensive view of risk and customer behavior. The integration of big data into underwriting processes has significantly advanced the precision and efficiency of risk evaluation. Traditional underwriting methods, which often relied on limited data sets and heuristic approaches, have been augmented by sophisticated algorithms and predictive models that leverage extensive data to assess risk profiles with greater accuracy. By incorporating real-time data from diverse sources, insurers can now better understand individual risk factors and dynamic changes in risk exposure, leading to more informed underwriting decisions and personalized policy offerings. In the realm of fraud detection, big data integration has introduced a paradigm shift by enabling insurers to identify and mitigate fraudulent activities more effectively. The analysis of large-scale data sets through advanced analytical techniques, including machine learning and artificial intelligence, has enhanced the ability to detect anomalous patterns and suspicious behaviors. This capability is instrumental in uncovering complex fraud schemes that may evade traditional detection methods. The paper delves into various big data technologies and methodologies employed in fraud detection, such as anomaly detection algorithms, network analysis, and behavioral analytics, h…
subject: Articles
  topic: section
subject: big data integration
  topic: keywords
subject: insurance industry
  topic: keywords
subject: underwriting processes
  topic: keywords
subject: fraud detection
  topic: keywords
subject: risk assessment
  topic: keywords
subject: predictive models
  topic: keywords
subject: machine learning
  topic: keywords
creator: Chandrashekar Althati
creator: Venkatesha Prabhu Rambabu
creator: Munivel Devan
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-04-04
publication date: 2023-04-04
starting page: 123
ending page: 162

Real-Time Data Integration in Retail: Improving Supply Chain and Customer Experience
type: item
description: The integration of real-time data in retail operations has emerged as a transformative force, revolutionizing supply chain management and enhancing customer experience. This paper explores the role of real-time data integration in the retail sector, focusing on its implementation, technological underpinnings, and the tangible benefits it offers. As the retail industry becomes increasingly complex, driven by consumer expectations for immediacy and precision, real-time data integration has become a critical enabler of operational efficiency and customer satisfaction. Real-time data integration involves the continuous aggregation and analysis of data from diverse sources, such as point-of-sale systems, inventory management tools, and customer interaction platforms. This seamless flow of information enables retailers to respond promptly to changing conditions, optimize supply chain processes, and deliver personalized customer experiences. The core technologies facilitating real-time data integration include advanced data streaming platforms, cloud-based data warehouses, and sophisticated analytics tools. One of the primary technologies underpinning real-time data integration is data streaming. Platforms such as Apache Kafka and Apache Flink allow retailers to process large volumes of data in motion, providing up-to-the-minute insights that drive decision-making. These technologies support the rapid ingestion and processing of data from various sources, ensuring that information is always current and relevant. Cloud-based data warehouses, like Google BigQuery and Amazon Redshift, play a crucial role in real-time data integration by offering scalable storage and processing capabilities. These platforms enable retailers to handle vast amounts of data efficiently, supporting the integration of disparate data sources and facilitating real-time analytics. By leveraging cloud infrastructure, retailers can achieve greater flexibility and scalability, adapting to the dynamic nat…
subject: Articles
  topic: section
subject: real-time data integration
  topic: keywords
subject: retail
  topic: keywords
subject: supply chain management
  topic: keywords
subject: customer experience
  topic: keywords
subject: data streaming
  topic: keywords
subject: cloud-based data warehouses
  topic: keywords
subject: data analytics
  topic: keywords
subject: inventory management
  topic: keywords
subject: personalized interactions
  topic: keywords
subject: operational efficiency
  topic: keywords
creator: Venkatesha Prabhu Rambabu
creator: Munivel Devan
creator: Chandan Jnana Murthy
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-03-16
publication date: 2023-03-16
starting page: 85
ending page: 122

ETL vs. ELT: Optimizing Data Integration for Retail and Insurance Analytics
type: item
description: In the rapidly evolving landscape of data integration, businesses across sectors, particularly retail and insurance, are increasingly relying on sophisticated methodologies to manage and analyze vast volumes of data. This paper delves into a comparative analysis of two prominent data integration methodologies—Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT)—with a specific focus on their application and optimization in the realms of retail and insurance analytics. Both ETL and ELT serve as pivotal frameworks in the data processing pipeline, but they diverge significantly in their approaches and implications for data management, performance, scalability, and efficiency. ETL, a traditional approach, involves extracting data from source systems, transforming it into a format suitable for analysis, and then loading it into a target data warehouse. This method has been widely adopted due to its structured process, which ensures data is cleaned and transformed before being stored. This pre-processing can enhance the quality and consistency of data but may also introduce latency due to the time-consuming transformation phase. The paper will explore ETL’s historical significance in data warehousing and its ongoing relevance in scenarios where data transformation requirements are complex and stringent. In contrast, ELT flips the sequence by first extracting data from source systems, loading it directly into the target data warehouse, and then performing transformation operations within the warehouse environment. This approach leverages the computational power of modern data warehouses, such as cloud-based platforms, to handle large-scale transformations efficiently. ELT’s inherent advantages include improved scalability and reduced data latency, as transformations are performed on-demand and can be optimized for performance. The paper will assess ELT’s suitability in contemporary analytics scenarios, particularly where the volume of data and real-time proces…
subject: Articles
  topic: section
subject: ETL
  topic: keywords
subject: ELT
  topic: keywords
subject: data integration
  topic: keywords
subject: retail analytics
  topic: keywords
subject: insurance analytics
  topic: keywords
subject: performance analysis
  topic: keywords
subject: scalability
  topic: keywords
subject: efficiency
  topic: keywords
creator: Venkatesha Prabhu Rambabu
creator: Chandrashekar Althati
creator: Amsa Selvaraj
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-01-10
publication date: 2023-01-10
starting page: 37
ending page: 84

Utilizing Transformers for Interactive Chatbot Development
type: item
description: Transformers, a novel architecture introduced by Vaswani et al. in "Attention is All You Need" (2017), have revolutionized natural language processing (NLP) by effectively using attention mechanisms to process and generate human language. This paper explores the implementation of a chatbot using the transformer model, specifically focusing on the practical aspects of tokenization, model selection, and generation of responses. The paper outlines the methods used, presents results from various model configurations, and provides an analysis of the chatbot's performance. Improvements and future directions are also discussed.
subject: Articles
  topic: section
subject: natural language processing
  topic: keywords
subject: NLP
  topic: keywords
subject: Chatbot
  topic: keywords
subject: transformer model
  topic: keywords
subject: tokenization
  topic: keywords
creator: Yichen Zhang
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-08-05
publication date: 2024-08-05
starting page: 124
ending page: 129

Machine Learning for Enhancing Vehicle Safety and Collision Avoidance Systems in Automotive Development: Techniques, Models, and Real-World Applications
type: item
description: The ever-increasing number of vehicles on the road necessitates continuous advancements in automotive safety technologies. Machine learning (ML) presents a transformative approach to this challenge, offering the potential to develop highly sophisticated Collision Avoidance Systems (CAS) and Advanced Driver-Assistance Systems (ADAS) that significantly improve vehicle safety and prevent accidents. This research paper delves into the application of ML for enhancing vehicle safety and CAS development. The paper commences with a comprehensive review of traditional CAS functionalities, highlighting their limitations in complex traffic scenarios. It then explores the fundamental principles of ML, emphasizing its ability to learn complex patterns and relationships from vast datasets. The paper subsequently delves into specific ML techniques prominently employed in CAS development, including object detection, path planning, and behavior prediction. Object detection plays a crucial role in CAS, as accurate and real-time identification of surrounding objects (vehicles, pedestrians, cyclists) is paramount for collision avoidance maneuvers. The paper discusses how ML algorithms, particularly Convolutional Neural Networks (CNNs), excel in this domain. CNN architectures like YOLO (You Only Look Once) and SSD (Single Shot MultiBox Detector) are explored, outlining their strengths in real-time object detection from camera and LiDAR sensor data. Furthermore, the paper examines the role of sensor fusion, a technique that combines data from multiple sensors (cameras, radar, LiDAR) to enhance object recognition accuracy and robustness in diverse environmental conditions. Path planning, another critical aspect of CAS, involves determining a safe trajectory for the vehicle to avoid imminent collisions. The paper investigates how ML algorithms, specifically Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units, are employed for path planning tasks. LSTMs excel at proces…
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Collision Avoidance Systems (CAS)
  topic: keywords
subject: Advanced Driver-Assistance Systems (ADAS)
  topic: keywords
subject: Object Detection
  topic: keywords
subject: Path Planning
  topic: keywords
subject: Autonomous Vehicles
  topic: keywords
subject: Sensor Fusion
  topic: keywords
subject: Convolutional Neural Networks (CNNs)
  topic: keywords
subject: Recurrent Neural Networks (RNNs)
  topic: keywords
subject: Real-World Applications
  topic: keywords
creator: Rahul Ekatpure
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-11-25
publication date: 2023-11-25
starting page: 1
ending page: 43

Machine Learning-Based Systems for Real-Time Traffic Prediction and Management in Automotive Development: Techniques, Models, and Applications
type: item
description: Urban traffic congestion is a growing concern globally, leading to increased travel times, fuel consumption, and emissions. This research paper delves into the application of machine learning (ML) for real-time traffic prediction and management within the realm of automotive development. Our primary focus is on exploring advanced models, techniques, and their real-world implementations that demonstrably improve traffic flow and reduce congestion. The paper commences by establishing the context of the problem. We delve into the detrimental effects of traffic congestion, encompassing economic and environmental costs, alongside the negative impact on public health and overall quality of life. Subsequently, we introduce the concept of Intelligent Transportation Systems (ITS) as a potential solution framework. Here, we highlight the critical role of real-time traffic prediction in enabling proactive management strategies. Next, we embark on a comprehensive exploration of ML techniques for traffic prediction. The paper emphasizes the strengths of various ML algorithms, particularly recurrent neural networks (RNNs) and their variants, such as Long Short-Term Memory (LSTM) networks. These models excel at capturing temporal dependencies within traffic data sequences, crucial for accurate short-term and long-term traffic flow forecasting. Additionally, we discuss the potential of Convolutional Neural Networks (CNNs) for analyzing spatial traffic patterns and extracting meaningful features from sensor data. Furthermore, the paper explores the burgeoning field of reinforcement learning (RL) for real-time traffic management. RL offers a promising avenue for optimizing traffic light control and dynamic route planning based on predicted traffic conditions. We delve into the potential of RL agents learning optimal control strategies through interaction with the simulated or real-world traffic environment. A critical aspect of this research is the integration of ML-based prediction …
subject: Articles
  topic: section
subject: Traffic prediction
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Real-time traffic management
  topic: keywords
subject: Intelligent transportation systems (ITS)
  topic: keywords
subject: Recurrent neural networks (RNNs)
  topic: keywords
subject: Convolutional neural networks (CNNs)
  topic: keywords
subject: Long short-term memory (LSTM)
  topic: keywords
subject: Reinforcement learning
  topic: keywords
subject: Vehicle-to-everything (V2X) communication
  topic: keywords
creator: Rahul Ekatpure
rights: Copyright (c) 2021 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2021-12-24
publication date: 2021-12-24
starting page: 6
ending page: 23

Enhancing Customer Service Automation and User Satisfaction: An Exploration of AI-powered Chatbot Implementation within Customer Relationship Management Systems
type: item
description: The contemporary business landscape is characterized by an unrelenting emphasis on customer experience. In this dynamic environment, organizations are constantly seeking innovative strategies to enhance customer service efficiency and satisfaction. Customer Relationship Management (CRM) systems play a pivotal role in this endeavor, facilitating the organization, analysis, and utilization of customer data to foster stronger client relationships. However, the ever-increasing volume of customer inquiries necessitates the exploration of novel approaches to streamline service delivery and augment agent productivity. Artificial Intelligence (AI) has emerged as a transformative technology with the potential to revolutionize customer service operations. One prominent application of AI within CRM is the integration of chatbots – virtual agents programmed to engage in text-based or voice-based dialogues to address customer queries and resolve issues. This research paper delves into the implementation and effectiveness of AI-powered chatbots within CRM systems, with a particular focus on their impact on customer service automation and user satisfaction. The paper commences with a comprehensive review of the theoretical underpinnings of customer service automation and user satisfaction in the context of CRM. This section explores relevant research on service quality frameworks, user experience (UX) design principles, and the psychological factors influencing customer satisfaction with technology-mediated interactions. Next, the paper delves into the technical aspects of AI-powered chatbots, specifically focusing on the core technologies that underpin their functionality. Natural Language Processing (NLP) techniques are examined, encompassing topics such as intent recognition, sentiment analysis, and dialogue management. Additionally, the paper explores the role of machine learning algorithms in chatbot development, particularly in enabling chatbots to learn and improve their re…
subject: Articles
  topic: section
subject: Customer Relationship Management (CRM)
  topic: keywords
subject: Customer Service Automation
  topic: keywords
subject: User Satisfaction
  topic: keywords
subject: Artificial Intelligence (AI)
  topic: keywords
subject: Chatbots
  topic: keywords
subject: Natural Language Processing (NLP)
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Case Study
  topic: keywords
subject: Empirical Evaluation
  topic: keywords
creator: Ashok Kumar Reddy Sadhu
creator: Maksym Parfenov
creator: Denis Saripov
creator: Maksim Muravev
creator: Amith Kumar Reddy Sadhu
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-02-15
publication date: 2024-02-15
starting page: 103
ending page: 123

Advanced AI Techniques for Real-Time Anomaly Detection and Incident Response in DevOps Environments: Ensuring Robust Security and Compliance
type: item
description: The ever-evolving landscape of DevOps environments, characterized by continuous integration/continuous delivery (CI/CD) pipelines, microservices architectures, and dynamic infrastructure, necessitates a paradigm shift in security and compliance practices. Traditional, static security controls struggle to keep pace with the rapid deployment cycles inherent in DevOps. This research study investigates the application of advanced Artificial Intelligence (AI) techniques for real-time anomaly detection and incident response within these dynamic environments. Our primary objective is to explore how AI can empower DevOps teams to achieve robust security, ensure compliance, and facilitate swift resolution of security incidents. The paper commences with a comprehensive overview of the challenges associated with security and compliance in DevOps. The limitations of traditional security methods, particularly their inability to adapt to rapid changes and the sheer volume of data generated, are highlighted. We then delve into the burgeoning field of AI and its potential to revolutionize security practices in DevOps. We explore a range of advanced AI techniques, including supervised and unsupervised machine learning algorithms, that can be leveraged for anomaly detection. Supervised learning algorithms, trained on historical data labeled as normal or anomalous, excel at identifying patterns indicative of security incidents. Techniques like Support Vector Machines (SVMs) and Random Forests can be employed to classify system behavior as normal or anomalous based on predefined features. Conversely, unsupervised learning algorithms, operating without pre-labeled data, are adept at uncovering hidden patterns in complex datasets. Anomaly detection algorithms based on clustering techniques, such as K-Means clustering, can identify deviations from established baseline behavior, potentially revealing previously unknown threats. The paper delves into the critical consideration of data selec…
subject: Articles
  topic: section
subject: Anomaly Detection
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: DevOps
  topic: keywords
subject: Incident Response
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Real-Time
  topic: keywords
subject: Security
  topic: keywords
subject: Security Information and Event Management (SIEM)
  topic: keywords
subject: Unsupervised Learning
  topic: keywords
subject: Supervised Learning
  topic: keywords
creator: Sumanth Tatineni
creator: Anirudh Mustyala
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-03-13
publication date: 2022-03-13
starting page: 88
ending page: 121

Human-in-the-Loop Moral Decision-Making Frameworks for Situationally Aware Multi-Modal Autonomous Vehicle Networks: An Accessibility-Focused Approach
type: item
description: The ethical implications of autonomous decision-making in AVs necessitate the development of robust frameworks that guide the vehicle's actions in unavoidable accident scenarios. These frameworks must consider not only the safety of the vehicle's occupants but also the potential impact on bystanders and the surrounding environment. Utilitarian ethics, which prioritize maximizing overall utility or well-being, often form the basis of such frameworks. However, this approach can be challenged in situations where difficult choices must be made, such as sacrificing the life of one passenger to save multiple pedestrians. For instance, a utilitarian framework might dictate that an AV swerve to hit a single pedestrian rather than collide with a bus full of passengers. While this decision may maximize overall well-being by saving more lives, it raises concerns about the fairness of sacrificing one innocent person to save others. Additionally, utilitarian frameworks can be difficult to implement in practice, as they often rely on complex calculations of the potential consequences of various actions. These calculations can be fraught with uncertainty, particularly in situations with incomplete information or rapidly evolving circumstances. Beyond the limitations of utilitarianism, other ethical frameworks also present challenges when applied to AV decision-making. Deontological ethics, which emphasizes following universal moral rules, can be inflexible in situations where adhering to a rule might lead to a worse outcome. For example, a deontological framework might dictate that an AV should always prioritize the safety of its occupants, regardless of the potential consequences for bystanders. This approach could result in the AV sacrificing the lives of pedestrians even when a swerving maneuver could avoid a collision altogether. Virtue ethics, which focuses on developing good character traits, offers a less rigid framework but can be subjective and difficult to operationalize…
subject: Articles
  topic: section
subject: Autonomous Vehicles
  topic: keywords
subject: Connected Vehicles
  topic: keywords
subject: Accessibility
  topic: keywords
subject: Elderly
  topic: keywords
subject: 5G Connectivity
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Ethical Decision-Making
  topic: keywords
subject: Human-Computer Interaction
  topic: keywords
subject: Moral Decision-Making Framework
  topic: keywords
creator: Vamsi Vemoori
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-02-10
publication date: 2022-02-10
starting page: 54
ending page: 87

Epidemiology and Oral Health: Harnessing Big Data for Monitoring and Predicting Epidemic Outbreaks
type: item
description: This paper delves into the transformative potential of big data within epidemiology, particularly in the context of oral health-related epidemic outbreaks. With the advent of digital technologies and the proliferation of interconnected systems, vast amounts of data are being generated and can be harnessed to monitor and predict disease trends. Leveraging big data analytics enables proactive interventions and the implementation of effective public health measures for disease prevention. Through a comprehensive review of existing literature and case studies, this paper elucidates the role of big data in revolutionizing epidemiological research, particularly in the realm of oral health. By analyzing diverse datasets ranging from social media posts to electronic health records, researchers can gain valuable insights into the dynamics of oral health epidemics, identify at-risk populations, and anticipate future outbreaks. Furthermore, the integration of advanced computational techniques, such as machine learning algorithms, empowers epidemiologists to forecast disease trajectories with unprecedented accuracy. This paper underscores the importance of interdisciplinary collaboration between public health experts, data scientists, and policymakers in harnessing the full potential of big data for epidemic monitoring and prediction. Ultimately, leveraging big data analytics in epidemiology offers promising avenues for mitigating the burden of oral health-related epidemics and improving overall population health.
subject: Articles
  topic: section
subject: Epidemiology
  topic: keywords
subject: Oral Health
  topic: keywords
subject: Big Data
  topic: keywords
subject: Epidemic Outbreaks
  topic: keywords
subject: Proactive Interventions
  topic: keywords
subject: Public Health
  topic: keywords
subject: Disease Prevention
  topic: keywords
creator: Varshitha Reddy Davarapalli
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-04-15
publication date: 2024-04-15
starting page: 86
ending page: 102

Automated Underwriting Systems: Advancements and Challenges in the Age of AI
type: item
description: Automated Underwriting Systems (AUS) powered by Artificial Intelligence (AI) have revolutionized the landscape of risk assessment and decision-making in various industries, notably finance and insurance. This paper delves into the advancements and challenges intrinsic to AUS in the contemporary AI era. Key areas of focus include regulatory compliance, bias mitigation, and model interpretability. Through an extensive review of literature and analysis of case studies, this research elucidates the evolving role of AI in underwriting, highlighting its potential to streamline processes, enhance accuracy, and improve efficiency. However, the integration of AI in underwriting also poses significant challenges, such as ensuring compliance with regulatory standards, addressing inherent biases, and achieving transparency and interpretability in complex AI models. The paper examines current methodologies, best practices, and emerging technologies aimed at mitigating these challenges. Additionally, it explores the ethical and societal implications of AI-driven underwriting systems, emphasizing the importance of fairness, accountability, and transparency. By synthesizing existing knowledge and identifying gaps in research, this paper provides insights for practitioners, policymakers, and researchers to navigate the intricate landscape of AUS in the age of AI.
subject: Articles
  topic: section
subject: Automated Underwriting Systems
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Regulatory Compliance
  topic: keywords
subject: Bias Mitigation
  topic: keywords
subject: Model Interpretability
  topic: keywords
subject: Risk Assessment
  topic: keywords
subject: Decision-making
  topic: keywords
subject: Ethical Implications
  topic: keywords
subject: Fairness
  topic: keywords
subject: Transparency
  topic: keywords
creator: Dipti Sontakke
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-06-24
publication date: 2022-06-24
starting page: 33
ending page: 53

AI and Machine Learning in Healthcare Robotics: Exploring AI-driven algorithms and their role in addressing healthcare challenges, including task optimization and patient care
type: item
description: Swarm intelligence (SI) has emerged as a promising paradigm for solving complex problems inspired by the collective behavior of social insects. In the field of robotics, SI algorithms have been applied to various tasks, including task allocation and exploration. This paper provides a comprehensive analysis of swarm intelligence in robotics, focusing on the principles of SI, types of algorithms, and their applications. We discuss how SI algorithms can be used to enhance the capabilities of robotic systems, improve efficiency, and achieve robustness in dynamic environments. Through a review of recent research and case studies, we highlight the benefits and challenges of implementing SI in robotics and explore future directions for research in this exciting field.
subject: Articles
  topic: section
subject: Swarm Intelligence
  topic: keywords
subject: Robotics
  topic: keywords
subject: Task Allocation
  topic: keywords
subject: Exploration
  topic: keywords
subject: Algorithms
  topic: keywords
subject: Collective Behavior
  topic: keywords
subject: Social Insects
  topic: keywords
subject: Efficiency
  topic: keywords
subject: Dynamic Environments
  topic: keywords
subject: Robustness
  topic: keywords
creator: Prof. Lucas Ramirez
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-03-28
publication date: 2024-03-28
starting page: 76
ending page: 85