Human-Computer Interaction Perspectives
type: channel
description: Embark on an Intellectual Journey: Welcome to 'Human-Computer Interaction Perspectives', a scholarly haven where the synergy between humans and technology unfolds. Immerse yourself in the intricate tapestry of user experience, interface design, and cognitive ergonomics, as we traverse the intricate landscape of human-computer interaction. Through a prism of interdisciplinary inquiry, we unveil the nuances of digital interfaces, societal impacts, and the symbiotic relationship between users and technology. Join us as we navigate the complexities of this dynamic field, exploring novel insights and shaping the future of human-centered design. Journal Snapshot Journal Name: Human-Computer Interaction Perspectives (HCIP) Editor-in-Chief: Dr. Richa Gupta (see Editorial Board) Publisher Imprint: The Science Brigade Publishers Publisher: Libertatem Media Private Limited h-index: 15 (By Google Scholar) i10-index: 22 (By Google Scholar) Journal Initials: HCIP Research Scope: Human-Computer Interaction (HCI), User Experience (UX) Design, User Interface (UI) Design, Human-Centered Computing, Accessibility and Inclusive Technologies, Extended Reality (AR/VR/MR), Multimodal Interaction Systems, Information Visualization and Visual Analytics, Intelligent Interactive Systems, Usability Engineering and User Behavior Analysis Publication Mode: Digital (On this Website) Frequency: Semi-Annual (2 Issues a year) Launch Year: 2021 Review Mode: Double Blind Peer Review Plagiarism Allowed: 10% (as per Turnitin) Coverage: Worldwide Language: English Editorial Contact - editor@thesciencebrigade.com
publisher: The Science Brigade Publishers
language: en
publication name: Human-Computer Interaction Perspectives
issn: 0000-0000
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: Predictive Energy Load Modelling and Process Decarbonisation: AI-Driven Energy Efficiency Frameworks for Sustainable U.S. Aerospace Manufacturing
items: Point-of-Sale Event Streaming and Automated Replenishment Triggers: Real-Time Machine Learning Frameworks for Demand Forecasting and Inventory Optimisation
items: Planogram Optimisation and Cross-Category Affinity Intelligence: AI-Enhanced Analytical Frameworks for Retail Merchandising Strategy
items: Payer Segmentation and Reimbursement Pathway Intelligence: AI-Driven Frameworks for Pharmaceutical and Healthcare Market Access Strategy
items: Predictive Scheduling and Skill-Task Matching Intelligence: AI-Based Workforce Optimisation Frameworks in American Aerospace Manufacturing
items: Proactive Disruption Intelligence and Inventory Hedging Models: AI-Enhanced Supply Chain Resilience Frameworks for U.S. Manufacturing Competitiveness
items: Real-Time Statistical Process Control and Root Cause Intelligence: AI-Driven Quality Assurance Systems for U.S. Manufacturing Revitalisation
items: Remaining Useful Life Estimation and Anomaly Detection in Industrial Assets: AI-Driven Predictive Maintenance for Operational Efficiency in General Manufacturing
items: Supplier Performance Intelligence and Dynamic Relationship Scoring: AI-Based Frameworks for Strategic Supplier Development in U.S. Manufacturing
items: Temporal Convolutional Networks and Seasonal Decomposition: AI-Based Predictive Modelling for Retail Sales Volume Forecasting
items: Vibration Signature Analysis and Fault Prognosis Through Deep Learning: AI-Driven Predictive Maintenance for Downtime Reduction in American Defense Manufacturing
items: Yield-Maximising Process Control Through Reinforcement Learning: AI-Driven Optimisation of Semiconductor Fabrication for U.S. Manufacturing Competitiveness
items: Policyholder Lifecycle Modelling and Offer Optimisation: AI-Driven Frameworks for Personalised Insurance Product Recommendation
items: Consumer Electronics Demand Signal Processing and SKU-Level Forecasting: AI-Powered Predictive Analytics for U.S. Laptop Manufacturing Operational Efficiency
items: Contextual Need Assessment and Policy Matching Intelligence: Machine Learning Frameworks for Enhanced Insurance Policy Recommendation Systems
items: Cross-Functional Data Integration and Process Automation: AI-Driven Frameworks for Retail Supply Chain Efficiency Enhancement
items: Operational Technology Threat Detection and Intrusion Response: AI-Enhanced Cybersecurity Frameworks for U.S. Manufacturing Infrastructure Protection
items: Demand-Driven Staffing Intelligence and Employee Performance Analytics: AI-Based Workforce Optimisation in American Retail Management
items: Disruption Propagation Modelling and Recovery Optimisation: AI-Driven Strategies for Supply Chain Resilience Enhancement in U.S. Manufacturing
items: Dynamic Risk Signal Processing and Adaptive Underwriting Intelligence: Real-Time AI Frameworks for Insurance Risk Assessment and Policy Adjustment
items: End-to-End Network Flow Optimisation and Demand-Supply Synchronisation: AI-Driven Supply Chain Strategies for U.S. Manufacturing Efficiency and Competitiveness
items: Fabrication Process Integrity Monitoring and Insider Threat Detection: AI-Enhanced Cybersecurity for U.S. Semiconductor Manufacturing Infrastructure
items: Hierarchical Demand Signal Integration and Forecast Reconciliation: AI-Based Approaches to Demand Forecasting Accuracy in American Retail Management
items: In-Line Sensor Fusion and Predictive Quality Modelling: AI-Driven Quality Control Frameworks for Revitalising U.S. Manufacturing Operations
items: Multi-Tier Supplier Risk Modelling and Logistics Contingency Planning: AI-Enhanced Supply Chain Resilience for U.S. Aerospace Manufacturing Competitiveness
items: Omnichannel Sentiment Analysis and Personalised Journey Orchestration: AI-Powered Frameworks for Customer Experience Management in Retail
items: Cross-Facility Energy Demand Forecasting and Carbon Arbitrage: AI-Driven Energy Efficiency Strategies for Sustainable U.S. Manufacturing and Logistics
items: Component Scarcity Forecasting and Multi-Tier Supplier Risk Modelling: AI-Driven Supply Chain Resilience for U.S. Technology Product Manufacturing
items: Component-Level Power Consumption Modelling and Assembly Line Optimisation: AI-Driven Energy Efficiency Solutions for U.S. Laptop Manufacturing
items: Computer Vision-Based Defect Classification and Process Deviation Detection: AI-Driven Quality Control Systems for U.S. Defense Manufacturing Revitalisation

Predictive Energy Load Modelling and Process Decarbonisation: AI-Driven Energy Efficiency Frameworks for Sustainable U.S. Aerospace Manufacturing
type: item
description: With the increasingly urgent need for sustainability, research centered on clean and productive aerospace manufacturing has gained rapid growth. As energy accounts for a significant proportion of manufacturing cost, the utilization of energy-intensive manufacturing processes favors a heavy consumption of energy, and in turn poses many environmental issues, such as air pollution, global warming, and the depletion of natural resources.
subject: Articles
  topic: section
subject: predictive energy load modelling
  topic: keywords
subject: process decarbonisation
  topic: keywords
subject: energy efficiency frameworks
  topic: keywords
subject: sustainable u.s. aerospace manufacturing
  topic: keywords
subject: machine learning
  topic: keywords
creator: Yasushi Wada
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-12-31
publication date: 2025-12-31
starting page: 14
ending page: 23

Point-of-Sale Event Streaming and Automated Replenishment Triggers: Real-Time Machine Learning Frameworks for Demand Forecasting and Inventory Optimisation
type: item
description: Demand forecasting and inventory replenishment are two important components of supply chain management that must evolve with the market. Accurate forecasting enables better planning decisions that help to minimize excess inventory levels as well as to meet customer demands in a timely and efficient manner. To date, traditional forecasting methods have contributed to the compiled forecasts for inventory replenishment.
subject: Articles
  topic: section
subject: point-of-sale event streaming
  topic: keywords
subject: automated replenishment triggers
  topic: keywords
subject: real-time machine learning frameworks
  topic: keywords
subject: demand forecasting
  topic: keywords
creator: Yang Wang
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-06-30
publication date: 2025-06-30
starting page: 46
ending page: 56

Planogram Optimisation and Cross-Category Affinity Intelligence: AI-Enhanced Analytical Frameworks for Retail Merchandising Strategy
type: item
description: There has been much written recently about the importance of artificial intelligence (AI) to the retail industry, and we are now beginning to see the proliferation of truly AI-enhanced merchandising strategies. These strategies are designed to offer a more contextually aware, analytics-based approach to traditional merchandising retail strategies. Yet, surprisingly little has been written about the foundational principles of this emerging field.
subject: Articles
  topic: section
subject: planogram optimisation
  topic: keywords
subject: cross-category affinity intelligence
  topic: keywords
subject: ai-enhanced analytical frameworks
  topic: keywords
subject: retail merchandising strategy
  topic: keywords
subject: machine learning
  topic: keywords
creator: Anna Westin
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-06-30
publication date: 2025-06-30
starting page: 38
ending page: 45

Payer Segmentation and Reimbursement Pathway Intelligence: AI-Driven Frameworks for Pharmaceutical and Healthcare Market Access Strategy
type: item
description: The pharmaceutical industry is characterized by highly regulated markets, complex pricing models, and the increasing demand for personalized medicine, making market access strategies both critical and challenging. In response to these complexities, the integration of artificial intelligence (AI) and machine learning (ML) into market access frameworks is revolutionizing how pharmaceutical companies approach drug pricing and access.
subject: Articles
  topic: section
subject: payer segmentation
  topic: keywords
subject: reimbursement pathway intelligence
  topic: keywords
subject: frameworks
  topic: keywords
subject: pharmaceutical
  topic: keywords
subject: machine learning
  topic: keywords
creator: Thomas Jensen
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-06-30
publication date: 2025-06-30
starting page: 17
ending page: 37

Predictive Scheduling and Skill-Task Matching Intelligence: AI-Based Workforce Optimisation Frameworks in American Aerospace Manufacturing
type: item
description: While AI techniques have not been widely adopted in the aerospace business due to their great complexity and interdisciplinary nature, this paper aims to explore AI fields and techniques. In the context of AI techniques in the aerospace industry, the AI-based tools discussed in this review are specifically curated for workforce optimization. In general, workforce optimization can encompass not only employee scheduling and training, but also planning factory operations.
subject: Articles
  topic: section
subject: predictive scheduling
  topic: keywords
subject: skill-task matching intelligence
  topic: keywords
subject: workforce optimisation frameworks
  topic: keywords
subject: american aerospace manufacturing
  topic: keywords
subject: machine learning
  topic: keywords
creator: Daniel Nikulin
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-12-31
publication date: 2025-12-31
starting page: 24
ending page: 32

Proactive Disruption Intelligence and Inventory Hedging Models: AI-Enhanced Supply Chain Resilience Frameworks for U.S. Manufacturing Competitiveness
type: item
description: U.S. manufacturers are rethinking their sourcing decisions in the wake of significant disruptions to global supply chains and the realization that having everything sourced from faraway places may not be such a good idea. Policy initiatives from federal and state governments, such as the CHIPS act and a new Michigan initiative, are underway to at least partially bring offshored industries back home.
subject: Articles
  topic: section
subject: proactive disruption intelligence
  topic: keywords
subject: inventory hedging models
  topic: keywords
subject: ai-enhanced supply chain resilience frameworks
  topic: keywords
subject: u.s. manufacturing competitiveness
  topic: keywords
subject: machine learning
  topic: keywords
creator: Serkan Gürsoy
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-12-31
publication date: 2025-12-31
starting page: 33
ending page: 41

Real-Time Statistical Process Control and Root Cause Intelligence: AI-Driven Quality Assurance Systems for U.S. Manufacturing Revitalisation
type: item
description: Laying the groundwork for understanding AI-driven quality control systems focuses on the fundamental concepts of machine learning and deep learning. There are different types of traditional machine learning models, and two successful vision-based quality control inspection systems are presented as representative examples of moving toward AI-driven systems.
subject: Articles
  topic: section
subject: real-time statistical process control
  topic: keywords
subject: root cause intelligence
  topic: keywords
subject: quality assurance systems
  topic: keywords
subject: u.s. manufacturing revitalisation
  topic: keywords
subject: machine learning
  topic: keywords
creator: Maria Fox
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-12-31
publication date: 2025-12-31
starting page: 42
ending page: 49

Remaining Useful Life Estimation and Anomaly Detection in Industrial Assets: AI-Driven Predictive Maintenance for Operational Efficiency in General Manufacturing
type: item
description: Upscaling a manufacturing system consists of equipment acquisition and mounting activities that should be planned so that upgrading the capacity of the system will not disrupt the operations excessively [2].
subject: Articles
  topic: section
subject: remaining useful life estimation
  topic: keywords
subject: anomaly detection
  topic: keywords
subject: industrial assets
  topic: keywords
subject: predictive maintenance
  topic: keywords
subject: operational efficiency
  topic: keywords
subject: machine learning
  topic: keywords
creator: Henrique Sentieiro
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-06-30
publication date: 2026-06-30
starting page: 1
ending page: 15

Supplier Performance Intelligence and Dynamic Relationship Scoring: AI-Based Frameworks for Strategic Supplier Development in U.S. Manufacturing
type: item
description: The exploration of AI-based solutions for enhancing supplier relationships in U.S. manufacturing is motivated by several factors. The integration of AI technologies in manufacturing processes is seen as a long-term investment, promoting economic viability, social cohesiveness, inclusion, and environmental sustainability [2]. Additionally, the digital revolution, with AI at its core, is profoundly impacting various aspects of society, including manufacturing and supply chains [1].
subject: Articles
  topic: section
subject: supplier performance intelligence
  topic: keywords
subject: dynamic relationship scoring
  topic: keywords
subject: frameworks
  topic: keywords
subject: strategic supplier development
  topic: keywords
subject: machine learning
  topic: keywords
creator: Anna Westin
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-06-30
publication date: 2026-06-30
starting page: 16
ending page: 24

Temporal Convolutional Networks and Seasonal Decomposition: AI-Based Predictive Modelling for Retail Sales Volume Forecasting
type: item
description: AI-based forecasting systems have caught the attention of numerous retailers in recent years. An accurate and reliable prediction of sales is an important concern among business professionals because sales predictions are crucial in enhancing the decision-making abilities of any business. AI has a remarkable influence on various areas when integrated into prediction systems. This includes marketing, consumer behavior analysis, and much more.
subject: Articles
  topic: section
subject: temporal convolutional networks
  topic: keywords
subject: seasonal decomposition
  topic: keywords
subject: predictive modelling
  topic: keywords
subject: retail sales volume forecasting
  topic: keywords
subject: machine learning
  topic: keywords
creator: Cristina Mateos
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-06-30
publication date: 2026-06-30
starting page: 25
ending page: 32

Vibration Signature Analysis and Fault Prognosis Through Deep Learning: AI-Driven Predictive Maintenance for Downtime Reduction in American Defense Manufacturing
type: item
description: Manufacturing processes have been transforming due to the next technological revolution in automation, known as industry 4.0. As such, machine manufacturers and suppliers of machine parts have been focusing on automating processes with autonomous or fully controlled smart machines [3]. This trend is also being adopted in the defense industry, where unmanned technologies in aviation, land, and underwater vehicles have been actively developed in recent years.
subject: Articles
  topic: section
subject: vibration signature analysis
  topic: keywords
subject: fault prognosis
  topic: keywords
subject: deep learning
  topic: keywords
subject: predictive maintenance
  topic: keywords
subject: downtime reduction
  topic: keywords
subject: machine learning
  topic: keywords
creator: Yang Wang
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-06-30
publication date: 2026-06-30
starting page: 33
ending page: 45

Yield-Maximising Process Control Through Reinforcement Learning: AI-Driven Optimisation of Semiconductor Fabrication for U.S. Manufacturing Competitiveness
type: item
description: Today's chips are designed by aligning billions of transistors in three dimensions. Not surprisingly, the complexity is exceeding our ability to understand them. Therefore, to handle the complexity, our best solution is simply to let the chips optimize themselves. That way, overly complicated tools and models are not needed to design and manufacture the complicated chips.
subject: Articles
  topic: section
subject: yield-maximising process control
  topic: keywords
subject: reinforcement learning
  topic: keywords
subject: optimisation
  topic: keywords
subject: semiconductor fabrication
  topic: keywords
subject: machine learning
  topic: keywords
creator: Emily Chen
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-06-30
publication date: 2026-06-30
starting page: 46
ending page: 57

Policyholder Lifecycle Modelling and Offer Optimisation: AI-Driven Frameworks for Personalised Insurance Product Recommendation
type: item
description: Personalized insurance offers are increasingly gaining importance in today’s growing digital world. Insurance organizations aim to tailor their products and services to the increasing needs and requirements of their customers. The major objective is to maximize customer satisfaction and enhance customer retention and loyalty. Traditional insurance organizations operate on the premise that all clients have the same needs and requirements.
subject: Articles
  topic: section
subject: policyholder lifecycle modelling
  topic: keywords
subject: offer optimisation
  topic: keywords
subject: frameworks
  topic: keywords
subject: personalised insurance product recommendation
  topic: keywords
subject: machine learning
  topic: keywords
creator: Emily Chen
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-12-31
publication date: 2025-12-31
starting page: 1
ending page: 13

Consumer Electronics Demand Signal Processing and SKU-Level Forecasting: AI-Powered Predictive Analytics for U.S. Laptop Manufacturing Operational Efficiency
type: item
description: Predicting the future is a complex task, especially predicting future demand. In a digitally connected world, increasing demand complexity and constantly changing customer trends put pressure on manufacturers and service providers to determine future demand. Demand forecasting is crucial for all types of manufacturing companies and many others dealing with the public.
subject: Articles
  topic: section
subject: consumer electronics demand signal processing
  topic: keywords
subject: sku-level forecasting
  topic: keywords
subject: predictive analytics
  topic: keywords
subject: u.s. laptop manufacturing operational efficiency
  topic: keywords
subject: machine learning
  topic: keywords
creator: Thomas Jensen
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/
date: 2023-06-30
publication date: 2023-06-30
starting page: 68
ending page: 75

Contextual Need Assessment and Policy Matching Intelligence: Machine Learning Frameworks for Enhanced Insurance Policy Recommendation Systems
type: item
description: Although insurance has evolved into a proactive platform, the essence of risk relaying is yet to be encompassed in insurance frameworks. These traditional models fall short of influencing customer choices based on personalized suggestions. Subsequently, they emanate from all available products instead of a strategic attempt to leverage futuristic insights.
subject: Articles
  topic: section
subject: contextual need assessment
  topic: keywords
subject: policy matching intelligence
  topic: keywords
subject: machine learning frameworks
  topic: keywords
subject: enhanced insurance policy recommendation systems
  topic: keywords
creator: Daniel Nikulin
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/
date: 2023-12-31
publication date: 2023-12-31
starting page: 1
ending page: 12

Cross-Functional Data Integration and Process Automation: AI-Driven Frameworks for Retail Supply Chain Efficiency Enhancement
type: item
description: Understanding consumer behavior is key to achieving efficiency in the modern retail supply chain. Shopping is integral to contemporary routines, and demands across this sphere have diversified across countless mediums, locations, and markets. On subsequent levels, the largest industries in the supply chain are faced with increasingly complex business offerings and operations.
subject: Articles
  topic: section
subject: cross-functional data integration
  topic: keywords
subject: process automation
  topic: keywords
subject: frameworks
  topic: keywords
subject: retail supply chain efficiency enhancement
  topic: keywords
subject: machine learning
  topic: keywords
creator: Serkan Gürsoy
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/
date: 2023-12-31
publication date: 2023-12-31
starting page: 27
ending page: 36

Operational Technology Threat Detection and Intrusion Response: AI-Enhanced Cybersecurity Frameworks for U.S. Manufacturing Infrastructure Protection
type: item
description: Over the past decade, there has been rapid digitalization and automation of many processes in the manufacturing industry across multiple critical infrastructures. It is expected that this trend will continue and that cyber threats will also steadily increase in frequency and complexity. The U.S. manufacturing sector is prime for cyber-attack, particularly given increasing reliance on the Internet and interconnected systems.
subject: Articles
  topic: section
subject: operational technology threat detection
  topic: keywords
subject: intrusion response
  topic: keywords
subject: ai-enhanced cybersecurity frameworks
  topic: keywords
subject: u.s. manufacturing infrastructure protection
  topic: keywords
subject: machine learning
  topic: keywords
creator: Cristina Mateos
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-06-30
publication date: 2025-06-30
starting page: 9
ending page: 16

Demand-Driven Staffing Intelligence and Employee Performance Analytics: AI-Based Workforce Optimisation in American Retail Management
type: item
description: Workforce optimization is of paramount importance in the retail sector, directly influencing the efficiency and success of retail operations. [1] emphasize the significance of understanding and predicting the impact of management practices on retail store productivity. Their approach involves a case study integrating qualitative and quantitative data collection methods, such as participant observations, staff interviews, and questionnaires.
subject: Articles
  topic: section
subject: demand-driven staffing intelligence
  topic: keywords
subject: employee performance analytics
  topic: keywords
subject: workforce optimisation
  topic: keywords
subject: american retail management
  topic: keywords
subject: machine learning
  topic: keywords
creator: Daniel Nikulin
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/
date: 2023-12-31
publication date: 2023-12-31
starting page: 37
ending page: 44

Disruption Propagation Modelling and Recovery Optimisation: AI-Driven Strategies for Supply Chain Resilience Enhancement in U.S. Manufacturing
type: item
description: Supply chain resilience is a critical concept that encompasses the ability of a supply chain to withstand and recover from various disruptions. These disruptions can arise from a multitude of sources, including changes in the business environment, intentional threats, external pressures, limited resources, and risks associated with multiple tiers of customers and suppliers.
subject: Articles
  topic: section
subject: disruption propagation modelling
  topic: keywords
subject: recovery optimisation
  topic: keywords
subject: strategies
  topic: keywords
subject: supply chain resilience enhancement
  topic: keywords
subject: machine learning
  topic: keywords
creator: Serkan Gürsoy
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/
date: 2023-12-31
publication date: 2023-12-31
starting page: 45
ending page: 52

Dynamic Risk Signal Processing and Adaptive Underwriting Intelligence: Real-Time AI Frameworks for Insurance Risk Assessment and Policy Adjustment
type: item
description: In order to make the most accurate and informed decisions, individuals need to have the most up-to-date information relayed to them through a policy that guarantees data about the real-time environment. Stakeholders require a good understanding of the importance of discussing instantaneous data for the insurance industry, particularly with regard to timely risk management.
subject: Articles
  topic: section
subject: dynamic risk signal processing
  topic: keywords
subject: adaptive underwriting intelligence
  topic: keywords
subject: real-time ai frameworks
  topic: keywords
subject: insurance risk assessment
  topic: keywords
subject: machine learning
  topic: keywords
creator: Maria Fox
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/
date: 2024-06-30
publication date: 2024-06-30
starting page: 82
ending page: 92

End-to-End Network Flow Optimisation and Demand-Supply Synchronisation: AI-Driven Supply Chain Strategies for U.S. Manufacturing Efficiency and Competitiveness
type: item
description: [1]. The modern supply chain's network-based architecture and the data it generates provide a framework for the scalability of AI-driven technologies. AI algorithms excel in leveraging extensive datasets, enabling machines to derive unique insights and perform tasks more efficiently than humans.
subject: Articles
  topic: section
subject: end-to-end network flow optimisation
  topic: keywords
subject: demand-supply synchronisation
  topic: keywords
subject: supply chain strategies
  topic: keywords
subject: u.s. manufacturing efficiency
  topic: keywords
subject: machine learning
  topic: keywords
creator: Maria Fox
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/
date: 2024-12-31
publication date: 2024-12-31
starting page: 1
ending page: 10

Fabrication Process Integrity Monitoring and Insider Threat Detection: AI-Enhanced Cybersecurity for U.S. Semiconductor Manufacturing Infrastructure
type: item
description: Sensors and built-in self-tests have become critical for successfully scaling beyond the 5-nanometer node. As such, U.S. semiconductor manufacturing is faced with vulnerable technology that offers adversaries opportunities to access sophisticated hidden backdoors. Enhanced cybersecurity solutions will aid our nation against cyberwars ranging from mere information collection to physical destruction of our power infrastructure.
subject: Articles
  topic: section
subject: fabrication process integrity monitoring
  topic: keywords
subject: insider threat detection
  topic: keywords
subject: ai-enhanced cybersecurity
  topic: keywords
subject: u.s. semiconductor manufacturing infrastructure
  topic: keywords
subject: machine learning
  topic: keywords
creator: Henrique Sentieiro
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/
date: 2024-12-31
publication date: 2024-12-31
starting page: 11
ending page: 25

Hierarchical Demand Signal Integration and Forecast Reconciliation: AI-Based Approaches to Demand Forecasting Accuracy in American Retail Management
type: item
description: Accurate demand forecasting is paramount for retail management as it directly impacts inventory optimization, resource management, and the ability to meet customer needs [3]. Particularly for weather-sensitive products, accurate forecasts with minimal errors are crucial for an efficient inventory control system.
subject: Articles
  topic: section
subject: hierarchical demand signal integration
  topic: keywords
subject: forecast reconciliation
  topic: keywords
subject: approaches to demand forecasting accuracy
  topic: keywords
subject: american retail management
  topic: keywords
subject: machine learning
  topic: keywords
creator: Anna Westin
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/
date: 2024-12-31
publication date: 2024-12-31
starting page: 26
ending page: 33

In-Line Sensor Fusion and Predictive Quality Modelling: AI-Driven Quality Control Frameworks for Revitalising U.S. Manufacturing Operations
type: item
description: Artificial intelligence (AI) and machine learning (ML) are changing the face of a variety of fields including agriculture, social media marketing, healthcare, and manufacturing. One of the most expected applications for ML in the future is the successful automation of the quality control (QC) process for high-volume production in manufacturing. ML could solve the challenges posed by older methods of QC and reduce cost while maintaining or improving on product quality.
subject: Articles
  topic: section
subject: in-line sensor fusion
  topic: keywords
subject: predictive quality modelling
  topic: keywords
subject: quality control frameworks
  topic: keywords
subject: revitalising u.s. manufacturing operations
  topic: keywords
subject: machine learning
  topic: keywords
creator: Yang Wang
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/
date: 2024-12-31
publication date: 2024-12-31
starting page: 34
ending page: 41

Multi-Tier Supplier Risk Modelling and Logistics Contingency Planning: AI-Enhanced Supply Chain Resilience for U.S. Aerospace Manufacturing Competitiveness
type: item
description: The idea behind supply chain resilience could be traced back to the 1950s. It refers to a system's ability to absorb disruptions and restore itself in the face of damaged performance as a result of a disruption. Supply chain resilience can enable the supply chain to handle large-scale uncertainties due to its adaptive capacity and ambidextrous activities. Specifically, adaptive capacity is the ability of a supply chain to make rapid adjustments and recover from operational trauma.
subject: Articles
  topic: section
subject: multi-tier supplier risk modelling
  topic: keywords
subject: logistics contingency planning
  topic: keywords
subject: ai-enhanced supply chain resilience
  topic: keywords
subject: u.s. aerospace manufacturing competitiveness
  topic: keywords
subject: machine learning
  topic: keywords
creator: Emily Chen
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/
date: 2024-12-31
publication date: 2024-12-31
starting page: 42
ending page: 52

Omnichannel Sentiment Analysis and Personalised Journey Orchestration: AI-Powered Frameworks for Customer Experience Management in Retail
type: item
description: Today, successful retailing is all about creating exceptional customer experiences that go beyond the ordinary. Those who fail to do so will end up competing primarily on price or product features. These experiences need to be relevant, engaging, consistent, and seamless—personal, yet scalable. A positive retail customer experience exists across the complete customer journey including browsing, purchasing, contacting customer service if needed, and post-purchase.
subject: Articles
  topic: section
subject: omnichannel sentiment analysis
  topic: keywords
subject: personalised journey orchestration
  topic: keywords
subject: frameworks
  topic: keywords
subject: customer experience management
  topic: keywords
subject: machine learning
  topic: keywords
creator: Henrique Sentieiro
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-06-30
publication date: 2025-06-30
starting page: 1
ending page: 8

Cross-Facility Energy Demand Forecasting and Carbon Arbitrage: AI-Driven Energy Efficiency Strategies for Sustainable U.S. Manufacturing and Logistics
type: item
description: The significance of AI-driven energy efficiency solutions in the U.S. manufacturing and logistics sectors lies in their potential to not only enhance economic viability but also to promote social cohesiveness, inclusion, and environmental sustainability. As highlighted by [3] , the costs associated with implementing AI applications in manufacturing should be viewed as long-term investments that contribute to the overall well-being of society and the environment.
subject: Articles
  topic: section
subject: cross-facility energy demand forecasting
  topic: keywords
subject: carbon arbitrage
  topic: keywords
subject: energy efficiency strategies
  topic: keywords
subject: sustainable u.s. manufacturing
  topic: keywords
subject: machine learning
  topic: keywords
creator: Yasushi Wada
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/
date: 2023-12-31
publication date: 2023-12-31
starting page: 13
ending page: 26

Component Scarcity Forecasting and Multi-Tier Supplier Risk Modelling: AI-Driven Supply Chain Resilience for U.S. Technology Product Manufacturing
type: item
description: In World War II's initial days, U.S. companies dramatically altered their fields of expertise to manufacture supplies for war. Today, as the COVID-19 pandemic has disrupted "business as usual," the supply chain and component shortages contribute to federal policy discussions for revitalizing U.S. domestic tech product manufacturing.
subject: Articles
  topic: section
subject: component scarcity forecasting
  topic: keywords
subject: multi-tier supplier risk modelling
  topic: keywords
subject: supply chain resilience
  topic: keywords
subject: u.s. technology product manufacturing
  topic: keywords
subject: machine learning
  topic: keywords
creator: Yang Wang
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/
date: 2022-12-31
publication date: 2022-12-31
starting page: 42
ending page: 54

Component-Level Power Consumption Modelling and Assembly Line Optimisation: AI-Driven Energy Efficiency Solutions for U.S. Laptop Manufacturing
type: item
description: The focus on AI-driven energy efficiency solutions for sustainable U.S. Laptop Manufacturing is rooted in the need to address the existing challenges in the manufacturing sector related to energy consumption and sustainability. As highlighted by [1] , the rise of highly efficient data factories, known as hyperscale facilities, has demonstrated the potential to significantly reduce energy consumption in conventional data centers.
subject: Articles
  topic: section
subject: component-level power consumption modelling
  topic: keywords
subject: assembly line optimisation
  topic: keywords
subject: energy efficiency solutions
  topic: keywords
subject: u.s. laptop manufacturing
  topic: keywords
subject: machine learning
  topic: keywords
creator: Emily Chen
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/
date: 2022-12-31
publication date: 2022-12-31
starting page: 55
ending page: 66

Computer Vision-Based Defect Classification and Process Deviation Detection: AI-Driven Quality Control Systems for U.S. Defense Manufacturing Revitalisation
type: item
description: In most of recorded history, the "quality" of goods and services was a function of the experience, care, skill, and expertise of individual craftspeople. The industrial revolution led to the development of less expensive means of manufacturing goods by replacing the highly autonomous person with machinery, tools, and process.
subject: Articles
  topic: section
subject: computer vision-based defect classification
  topic: keywords
subject: process deviation detection
  topic: keywords
subject: quality control systems
  topic: keywords
subject: u.s. defense manufacturing revitalisation
  topic: keywords
subject: machine learning
  topic: keywords
creator: Cristina Mateos
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/
date: 2022-12-31
publication date: 2022-12-31
starting page: 67
ending page: 76