Blockchain Technology and Distributed Systems
type: channel
description: Unlocking the Future: Welcome to the Home of 'Blockchain Technology and Distributed Systems'. Explore the revolutionary landscape where decentralization meets innovation, shaping the way we exchange value and information. Delve into pioneering research, real-world applications, and transformative insights driving the evolution of blockchain technology. Join us at the forefront of a decentralized world, where trust, security, and innovation converge to redefine the future of distributed systems. Journal Snapshot Journal Name: Blockchain Technology and Distributed Systems (BTDS) Editor-in-Chief: Dr. Rahul Ganpatrao Sonkamble (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: BTDS Research Scope: Blockchain Technology, Distributed Systems, Decentralized Applications (DApps), Smart Contracts and Digital Assets, Cryptography and Secure Computing, Consensus Algorithms and Distributed Ledger Technologies, Web3 and Decentralized Infrastructure, Blockchain Security and Privacy, Edge Computing and Distributed Architectures, Enterprise Blockchain and FinTech Systems 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: Blockchain Technology and Distributed Systems
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: Temporal Sequence Modelling and Anomaly Localisation: A Real-Time Deep Learning Framework for Insurance Claims Fraud Detection
items: Stochastic Portfolio Modelling Under Uncertainty: Machine Learning Approaches to Systemic Risk Assessment in Insurance Portfolios
items: Streaming Risk Scoring and Behavioural Pattern Recognition: A Real-Time Machine Learning Architecture for Insurance Fraud Risk Assessment
items: Multi-Target Oncology Lead Identification Through Graph Neural Networks: AI-Driven Platforms for Cancer Drug Discovery
items: Online Demand Sensing and Inventory Allocation Intelligence: A Real-Time Reinforcement Learning Framework for Supply-Demand Matching
items: Optical Character Recognition and Natural Language Classification in Insurance: Deep Learning Architectures for End-to-End Claims Processing Automation
items: Predictive Adverse Event Detection and Proactive Clinical Intervention: Real-Time AI Architectures for Patient Safety Enhancement in Acute Care
items: Predictive Excipient Compatibility and Dissolution Modelling: AI-Driven Optimisation of Solid Dosage Form Formulation
items: Predictive Lead Time Compression in Multi-Tier Retail Networks: A Machine Learning Framework for Supplier Synchronisation
items: Real-Time Behavioural Anomaly Detection in Digital Payments: A Supervised Learning Framework for Financial Transaction Fraud Identification
items: Regime-Aware Asset Allocation Through Recurrent Neural Networks: Machine Learning Approaches to Adaptive Investment Strategy Development
items: Metabolic Phenotype Modelling and Dietary Personalisation: Machine Learning Frameworks for Evidence-Based Nutrition and Lifestyle Intervention Optimisation
items: Ligand-Target Affinity Prediction and Scaffold Optimisation: Deep Learning Approaches to Accelerated Small-Molecule Drug Discovery
items: Spatial Consumer Flow Analysis and Product Placement Intelligence: Machine Learning Approaches to Retail Store Layout Optimisation
items: Intelligent Order Routing and Exception Handling in E-Commerce Logistics: Machine Learning Frameworks for Automated Fulfilment Optimisation
items: Intelligent Triage and Empathetic Dialogue Systems: Transformer-Based Architectures for AI-Augmented Insurance Customer Service
items: Latent Consumer Behaviour Profiling in Omnichannel Environments: Deep Clustering Architectures for Precision Retail Segmentation
items: Automated Plate Analysis and Hit Confirmation Intelligence: Machine Learning Architectures for High-Throughput Screening Workflow Optimisation
items: Biomarker-Stratified Therapeutic Targeting: Machine Learning Frameworks for Precision Drug Development and Patient-Centric Dosing
items: Collaborative Forecasting and Shared Inventory Intelligence: AI-Driven Frameworks for Multi-Partner Retail Supply Chain Synchronisation
items: Competitive Intelligence and Margin Preservation Through Predictive Analytics: An AI-Driven Framework for Retail Pricing Strategy Optimisation
items: Automated Resolution Pathways in Insurance Claims: Reinforcement Learning Models for Settlement Process Optimisation
items: Contextual Demand Elasticity Modelling and Competitive Pricing Intelligence: Reinforcement Learning for Dynamic Retail Price Optimisation
items: Intelligent Nanocarrier Design and Release Kinetics Prediction: Machine Learning Approaches to Targeted Drug Delivery Optimisation
items: Intelligent Document Processing and Regulatory Pathway Mapping: AI-Enhanced Compliance Automation in Pharmaceutical Submissions
items: Federated Learning and Knowledge Graph Integration: AI-Driven Platforms for Cross-Institutional Pharmaceutical Research Collaboration
items: Explainable Credit Decision Engines: Machine Learning Architectures for Automated Loan Origination and Approval
items: Dynamic Safety Stock Calibration and Replenishment Intelligence: Deep Reinforcement Learning for Multi-Echelon Inventory Optimisation
items: Demand-Supply Synchronisation Through Predictive Modelling: A Machine Learning Framework for End-to-End Supply Chain Optimisation
items: Continuous Physiological Signal Analysis and Clinical Alert Generation: Real-Time Machine Learning Systems for Chronic Disease Monitoring and Management

Temporal Sequence Modelling and Anomaly Localisation: A Real-Time Deep Learning Framework for Insurance Claims Fraud Detection
type: item
description: Today, the insurance companies are facing a significant challenge in the form of detecting fraud in insurance claims. It is projected that approximately 10 percent of all non-healthcare insurance claims are fraudulent, amounting to $40 billion in losses in a year in the United States alone. The prevalence and widespread proliferation of the problem of insurance fraud have elicited the need to strengthen the detection mechanisms to address the issue in real time.
subject: Articles
  topic: section
subject: temporal sequence modelling
  topic: keywords
subject: anomaly localisation
  topic: keywords
subject: real-time deep learning framework
  topic: keywords
subject: insurance claims fraud detection
  topic: keywords
subject: machine learning
  topic: keywords
creator: Christopher Müller
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-06-30
publication date: 2026-06-30
starting page: 37
ending page: 52

Stochastic Portfolio Modelling Under Uncertainty: Machine Learning Approaches to Systemic Risk Assessment in Insurance Portfolios
type: item
description: The prosperity of the insurance industry is quite sensitive and may be questionable due to indirect factors that are hard to control. To improve their positions in the market and increase profitability, insurers pay attention to adjustments in the insurance portfolio and the proper classification of risk. The risk of an insurance portfolio is driven by various factors, and the risk profile of a class should be a weighted combination of the mean and variance of several variables.
subject: Articles
  topic: section
subject: stochastic portfolio modelling under uncertainty
  topic: keywords
subject: machine learning approaches to systemic risk assessment
  topic: keywords
subject: insurance portfolios
  topic: keywords
creator: Andrei Tonkoshkur
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-06-30
publication date: 2026-06-30
starting page: 18
ending page: 25

Streaming Risk Scoring and Behavioural Pattern Recognition: A Real-Time Machine Learning Architecture for Insurance Fraud Risk Assessment
type: item
description: Artificial intelligence (AI) has a valuable role to play in the detection and prevention of insurance fraud. This is especially important because insurance fraud can cause substantial financial losses. In the United States alone, fraudulent claims amount to $80 billion annually, and experts estimate that the problem is even more serious for insurance companies in the European Union. Especially in the case of non-life insurance, insurance fraud prevention and detection are gaining in importance.
subject: Articles
  topic: section
subject: streaming risk scoring
  topic: keywords
subject: behavioural pattern recognition
  topic: keywords
subject: real-time machine learning architecture
  topic: keywords
subject: insurance fraud risk assessment
  topic: keywords
creator: Jorge Murillo
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-06-30
publication date: 2026-06-30
starting page: 26
ending page: 36

Multi-Target Oncology Lead Identification Through Graph Neural Networks: AI-Driven Platforms for Cancer Drug Discovery
type: item
description: Cancer is one of the most devastating diseases worldwide. The traditional avenue of treating patients has been through chemotherapy, but a recent innovation has transformed the growth of early 21st-century biotech companies. Drugs that act on molecular targets in tumors have revolutionized the treatment of cancer because each different drug can reduce the size of tumors at a different site.
subject: Articles
  topic: section
subject: multi-target oncology lead identification
  topic: keywords
subject: graph neural networks
  topic: keywords
subject: platforms
  topic: keywords
subject: cancer drug discovery
  topic: keywords
subject: machine learning
  topic: keywords
creator: Wai-Keung Wong
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-06-30
publication date: 2025-06-30
starting page: 31
ending page: 43

Online Demand Sensing and Inventory Allocation Intelligence: A Real-Time Reinforcement Learning Framework for Supply-Demand Matching
type: item
description: Understanding the dynamics of demand and supply in real time can be very beneficial, especially in today's volatile and highly competitive market environment. For instance, some online retailers offer items for sale that are already in transit. Estimations of future demand, which are the output of sophisticated data-driven methods, are used in real-time logistics optimization systems.
subject: Articles
  topic: section
subject: online demand sensing
  topic: keywords
subject: inventory allocation intelligence
  topic: keywords
subject: real-time reinforcement learning framework
  topic: keywords
subject: supply-demand matching
  topic: keywords
subject: machine learning
  topic: keywords
creator: Svetlana Glazkova
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-06-30
publication date: 2025-06-30
starting page: 44
ending page: 51

Optical Character Recognition and Natural Language Classification in Insurance: Deep Learning Architectures for End-to-End Claims Processing Automation
type: item
description: Claims processing is a time-consuming and costly task within the insurance industry. The traditional method involves human assessment but is now slowly being replaced with automated systems. It is believed that AI-driven models can help in cutting down the costs that an insurance company pays to employ personnel to process claims, as well as reduce the time taken in claim processing, which, in general conditions, takes weeks.
subject: Articles
  topic: section
subject: optical character recognition
  topic: keywords
subject: natural language classification
  topic: keywords
subject: insurance
  topic: keywords
subject: deep learning architectures
  topic: keywords
subject: end-to-end claims processing automation
  topic: keywords
subject: machine learning
  topic: keywords
creator: Nkemjika Ezekwembe
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: 8

Predictive Adverse Event Detection and Proactive Clinical Intervention: Real-Time AI Architectures for Patient Safety Enhancement in Acute Care
type: item
description: Although there has been a rapid increase in morbidity and mortality rates due to adverse events in healthcare settings, it has been difficult to properly monitor them because the number of sensors installed to cover various areas has increased, and monitoring has become more complicated. In this regard, there have been attempts to monitor healthcare settings using machine learning, which is a data-driven approach.
subject: Articles
  topic: section
subject: predictive adverse event detection
  topic: keywords
subject: proactive clinical intervention
  topic: keywords
subject: real-time ai architectures
  topic: keywords
subject: patient safety enhancement
  topic: keywords
subject: machine learning
  topic: keywords
creator: Beatrice Kern
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-12-31
publication date: 2025-12-31
starting page: 9
ending page: 17

Predictive Excipient Compatibility and Dissolution Modelling: AI-Driven Optimisation of Solid Dosage Form Formulation
type: item
description: It is widely recognized that drug development, the aim of which is to formulate new, quality preparations in user-friendly dosage forms, is becoming more and more complicated today. Therefore, new solutions based on modern technologies must be sought in order to maintain effectiveness, reduce costs, and increase the efficiency of the entire process—artificial intelligence (AI) is one of them.
subject: Articles
  topic: section
subject: predictive excipient compatibility
  topic: keywords
subject: dissolution modelling
  topic: keywords
subject: optimisation
  topic: keywords
subject: solid dosage form formulation
  topic: keywords
subject: machine learning
  topic: keywords
creator: Hatice Özbay
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-12-31
publication date: 2025-12-31
starting page: 18
ending page: 25

Predictive Lead Time Compression in Multi-Tier Retail Networks: A Machine Learning Framework for Supplier Synchronisation
type: item
description: Modern retail has embraced fast-moving fashions and new lifestyles by sequentially updating seasonal collections. In doing so, retailers are faced with increasing urgency in responding to fluctuations in demand drivers such as regional and localized fashion, weather, and new trends. Consequently, retail supply chains continue to be optimized to focus on reducing lead times, as they play a crucial role in enhancing customer satisfaction.
subject: Articles
  topic: section
subject: predictive lead time compression
  topic: keywords
subject: multi-tier retail networks
  topic: keywords
subject: machine learning framework
  topic: keywords
subject: supplier synchronisation
  topic: keywords
creator: Carlos Murillo
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-12-31
publication date: 2025-12-31
starting page: 26
ending page: 36

Real-Time Behavioural Anomaly Detection in Digital Payments: A Supervised Learning Framework for Financial Transaction Fraud Identification
type: item
description: Today's business environments primarily depend on electronic transactions, thereby increasing the urgency of a more powerful fraud detection regime. Research reveals some shocking facts regarding this evil trend. International markets frequently encounter several types of fraud and monetary losses, affecting financial institutions, businesses, and other societies. Most organizations suffer from damaging charges, data, and reputation.
subject: Articles
  topic: section
subject: real-time behavioural anomaly detection
  topic: keywords
subject: digital payments
  topic: keywords
subject: supervised learning framework
  topic: keywords
subject: financial transaction fraud identification
  topic: keywords
subject: machine learning
  topic: keywords
creator: Seungjin Oh
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-12-31
publication date: 2025-12-31
starting page: 37
ending page: 49

Regime-Aware Asset Allocation Through Recurrent Neural Networks: Machine Learning Approaches to Adaptive Investment Strategy Development
type: item
description: Meet the worlds of artificial intelligence (AI) and investing. These separate worlds are now coming together to transform not only how investment strategies are developed, but also who develops them and what can be done with them to address specific investment and operational challenges.
subject: Articles
  topic: section
subject: regime-aware asset allocation
  topic: keywords
subject: recurrent neural networks
  topic: keywords
subject: machine learning approaches to adaptive investment strategy development
  topic: keywords
creator: Matej Rojc
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: 9

Metabolic Phenotype Modelling and Dietary Personalisation: Machine Learning Frameworks for Evidence-Based Nutrition and Lifestyle Intervention Optimisation
type: item
description: Because of the substantial variation in individual responses to diet and lifestyle interventions, tailoring health recommendations on the basis of individual profiles is becoming increasingly popular. An important asset of personalizing recommendations is the ability to respect how people live, rather than trying to change their behaviors to fit the much more generic approach of one-size-fits-all intervention programs.
subject: Articles
  topic: section
subject: metabolic phenotype modelling
  topic: keywords
subject: dietary personalisation
  topic: keywords
subject: machine learning frameworks
  topic: keywords
subject: evidence-based nutrition
  topic: keywords
creator: Juan Gómez-Olmos
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-06-30
publication date: 2025-06-30
starting page: 22
ending page: 30

Ligand-Target Affinity Prediction and Scaffold Optimisation: Deep Learning Approaches to Accelerated Small-Molecule Drug Discovery
type: item
description: AI transformation in the drug discovery area is considered to be one of the most exciting and promising trends in the pharmaceutical industry. Without a doubt, the strategies they use should work. Accumulating experimental data and preclinical research on single drug candidates may take an enormous amount of time and resources, without any promise of the expected benefit.
subject: Articles
  topic: section
subject: ligand-target affinity prediction
  topic: keywords
subject: scaffold optimisation
  topic: keywords
subject: deep learning approaches to accelerated small-molecule drug discovery
  topic: keywords
subject: machine learning
  topic: keywords
creator: Minh Nguyen
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-06-30
publication date: 2025-06-30
starting page: 13
ending page: 21

Spatial Consumer Flow Analysis and Product Placement Intelligence: Machine Learning Approaches to Retail Store Layout Optimisation
type: item
description: Retail store layout optimization promises strategic value in ensuring an effective shopping environment. Store layouts can account for patterns of human behavior that manifest in consumer purchasing behaviors. The field of retail store layout has traditionally been dominated by intuition-based decisions. However, new technologies are reshaping how retail is conducted. New technology is providing new avenues of approach to help businesses influence consumer behavior.
subject: Articles
  topic: section
subject: spatial consumer flow analysis
  topic: keywords
subject: product placement intelligence
  topic: keywords
subject: machine learning approaches to retail store layout optimisation
  topic: keywords
creator: Tatsiana Stsebakhova
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-06-30
publication date: 2026-06-30
starting page: 10
ending page: 17

Intelligent Order Routing and Exception Handling in E-Commerce Logistics: Machine Learning Frameworks for Automated Fulfilment Optimisation
type: item
description: In the retail sector, organizations grapple with tens of thousands, if not millions, of orders, which are increasingly submitted online and require shipping to be completed within a few days. As demand grows, many organizations will resort to automating processing workflows. This essay shines a light on how artificial intelligence, or AI, in particular, can support this automation in relation to such order fulfillment processing.
subject: Articles
  topic: section
subject: intelligent order routing
  topic: keywords
subject: exception handling
  topic: keywords
subject: e-commerce logistics
  topic: keywords
subject: machine learning frameworks
  topic: keywords
subject: automated fulfilment optimisation
  topic: keywords
creator: (Dr.) Ndubuisi Enem
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

Intelligent Triage and Empathetic Dialogue Systems: Transformer-Based Architectures for AI-Augmented Insurance Customer Service
type: item
description: The need for the latest in enhanced customer service and connectivity has never been so important as it is today. As consumer attitudes and buying habits continue to evolve, becoming more and more digital-centric, companies across all segments are rapidly attempting to make themselves more efficient, more personal, and more responsive to the clients and customers they serve.
subject: Articles
  topic: section
subject: intelligent triage
  topic: keywords
subject: empathetic dialogue systems
  topic: keywords
subject: transformer-based architectures
  topic: keywords
subject: ai-augmented insurance customer service
  topic: keywords
subject: machine learning
  topic: keywords
creator: Alexandre Bayen
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: 42

Latent Consumer Behaviour Profiling in Omnichannel Environments: Deep Clustering Architectures for Precision Retail Segmentation
type: item
description: Customer segmentation is the process of dividing a company's customers into groups with similarities according to different factors. In the retail industry, customer segmentation provides retailers with an enhanced ability to focus their marketing efforts to identify the most profitable customers. Retailers can tailor their marketing programs to the needs of the most profitable market segments.
subject: Articles
  topic: section
subject: latent consumer behaviour profiling
  topic: keywords
subject: omnichannel environments
  topic: keywords
subject: deep clustering architectures
  topic: keywords
subject: precision retail segmentation
  topic: keywords
subject: machine learning
  topic: keywords
creator: João Madeira
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: 12

Automated Plate Analysis and Hit Confirmation Intelligence: Machine Learning Architectures for High-Throughput Screening Workflow Optimisation
type: item
description: High-throughput screening (HTS) is a drug discovery process typically conducted at both the academic and industrial levels. It is employed during early discovery stages of drug development to validate bioactivity leading to the development of a clinical candidate as part of a disease treatment. Globalized drug discovery requiring the screening of vast chemical libraries demands such cost- and time-saving methods as HTS.
subject: Articles
  topic: section
subject: automated plate analysis
  topic: keywords
subject: hit confirmation intelligence
  topic: keywords
subject: machine learning architectures
  topic: keywords
subject: high-throughput screening workflow optimisation
  topic: keywords
creator: Sunita Singh
rights: Copyright (c) 2021 https://creativecommons.org/licenses/by/4.0/
date: 2021-12-31
publication date: 2021-12-31
starting page: 44
ending page: 55

Biomarker-Stratified Therapeutic Targeting: Machine Learning Frameworks for Precision Drug Development and Patient-Centric Dosing
type: item
description: Personalized medicine tailors treatments to individual characteristics. This is typically done with genetic information but includes other factors such as environment, age, lifestyle, and concomitant medications. An important goal in developing individual patient treatment strategies is to maximize efficacy and minimize adverse effects.
subject: Articles
  topic: section
subject: biomarker-stratified therapeutic targeting
  topic: keywords
subject: machine learning frameworks
  topic: keywords
subject: precision drug development
  topic: keywords
creator: Iben Nielsen
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/
date: 2022-12-31
publication date: 2022-12-31
starting page: 70
ending page: 81

Collaborative Forecasting and Shared Inventory Intelligence: AI-Driven Frameworks for Multi-Partner Retail Supply Chain Synchronisation
type: item
description: Retail supply chains are growing in complexity, with omnichannel retailing, price optimization, and sustainability now all expected activities. This has been driving increasing innovation in the development of advanced retail supply chain technologies.
subject: Articles
  topic: section
subject: collaborative forecasting
  topic: keywords
subject: shared inventory intelligence
  topic: keywords
subject: frameworks
  topic: keywords
subject: multi-partner retail supply chain synchronisation
  topic: keywords
subject: machine learning
  topic: keywords
creator: Ali Jaber
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/
date: 2023-06-30
publication date: 2023-06-30
starting page: 43
ending page: 56

Competitive Intelligence and Margin Preservation Through Predictive Analytics: An AI-Driven Framework for Retail Pricing Strategy Optimisation
type: item
description: Retail pricing strategies concern decisions regarding pricing levels, techniques, policies, and price adaptation. One common pricing policy in retailing is determining regular prices and subsequently incorporating price promotions using different formats or techniques. Several pricing models have analyzed how, when, and to what extent prices should be reduced by price promotions to attract and retain consumers.
subject: Articles
  topic: section
subject: competitive intelligence
  topic: keywords
subject: margin preservation
  topic: keywords
subject: predictive analytics
  topic: keywords
subject: an ai-driven framework
  topic: keywords
subject: retail pricing strategy optimisation
  topic: keywords
subject: machine learning
  topic: keywords
creator: Haejin Kim
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/
date: 2023-06-30
publication date: 2023-06-30
starting page: 57
ending page: 68

Automated Resolution Pathways in Insurance Claims: Reinforcement Learning Models for Settlement Process Optimisation
type: item
description: The growing complexity of the insurance business should enhance the need for innovative tools that can assist the government and the policyholders alike in dealing with insurance. Today, the business has been facing newer challenges regarding ensuring operational accuracy and efficiency.
subject: Articles
  topic: section
subject: automated resolution pathways
  topic: keywords
subject: insurance claims
  topic: keywords
subject: reinforcement learning models
  topic: keywords
subject: settlement process optimisation
  topic: keywords
subject: machine learning
  topic: keywords
creator: Manuela Veloso
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/
date: 2022-12-31
publication date: 2022-12-31
starting page: 60
ending page: 69

Contextual Demand Elasticity Modelling and Competitive Pricing Intelligence: Reinforcement Learning for Dynamic Retail Price Optimisation
type: item
description: Artificial intelligence (AI) technologies and machine learning algorithms have become increasingly relevant in recent retail experiences. They can help retailers of all sizes, especially online stores, create unique pricing strategies tailored to a specific product, customer segment, micro-segment, or contextual regulation. This essay outlines these pricing strategies and some real problems faced by enterprises.
subject: Articles
  topic: section
subject: contextual demand elasticity modelling
  topic: keywords
subject: competitive pricing intelligence
  topic: keywords
subject: reinforcement learning
  topic: keywords
subject: dynamic retail price optimisation
  topic: keywords
subject: machine learning
  topic: keywords
creator: Andrés Herrera
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

Intelligent Nanocarrier Design and Release Kinetics Prediction: Machine Learning Approaches to Targeted Drug Delivery Optimisation
type: item
description: Drug delivery systems are a strategic part of modern medical treatments. The ability to reach specific tissues, cells, and even intracellular compartments using tailored delivery vehicles in combination with a multidisciplinary approach allows for the development of safer treatments with minimized side effects.
subject: Articles
  topic: section
subject: intelligent nanocarrier design
  topic: keywords
subject: release kinetics prediction
  topic: keywords
subject: machine learning approaches to targeted drug delivery optimisation
  topic: keywords
creator: Giovanna Di Guglielmo
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/
date: 2024-12-31
publication date: 2024-12-31
starting page: 18
ending page: 25

Intelligent Document Processing and Regulatory Pathway Mapping: AI-Enhanced Compliance Automation in Pharmaceutical Submissions
type: item
description: In light of global health and safety standards, the pharmaceutical sector is a highly regulated industry. Compliance with these regulatory bodies should be the primary concern because the pharmaceutical product of a company not meeting these safety and regulatory standards may not be prescribed to the public.
subject: Articles
  topic: section
subject: intelligent document processing
  topic: keywords
subject: regulatory pathway mapping
  topic: keywords
subject: ai-enhanced compliance automation
  topic: keywords
subject: pharmaceutical submissions
  topic: keywords
subject: machine learning
  topic: keywords
creator: Joseph Msabila
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/
date: 2024-12-31
publication date: 2024-12-31
starting page: 10
ending page: 17

Federated Learning and Knowledge Graph Integration: AI-Driven Platforms for Cross-Institutional Pharmaceutical Research Collaboration
type: item
description: Transformative changes in the ways by which scientists can collate data and formulate hypotheses for the R&D pipeline are timely and relevant to pharmaceutical research collaboration. The challenges and complexities of enhancing global health through sporadic research conducted by disparate entities without sharing data, and the difficulty in conducting intra-consortium studies unacceptable to multiple consortium members have been discussed.
subject: Articles
  topic: section
subject: federated learning
  topic: keywords
subject: knowledge graph integration
  topic: keywords
subject: platforms
  topic: keywords
subject: cross-institutional pharmaceutical research collaboration
  topic: keywords
subject: machine learning
  topic: keywords
creator: Javad Salehi
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: 9

Explainable Credit Decision Engines: Machine Learning Architectures for Automated Loan Origination and Approval
type: item
description: Artificial intelligence (AI) is transforming how financial institutions operate and deliver value. The use of AI in financial institutions has continued to grow exponentially as the technology enables a variety of functions to be improved, such as customer service and accuracy. By understanding the different types of AI and their operational nuances, financial institutions can leverage these technologies to automate the loan approval process.
subject: Articles
  topic: section
subject: explainable credit decision engines
  topic: keywords
subject: machine learning architectures
  topic: keywords
subject: automated loan origination
  topic: keywords
creator: Astrid Lwoga
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/
date: 2024-06-30
publication date: 2024-06-30
starting page: 66
ending page: 73

Dynamic Safety Stock Calibration and Replenishment Intelligence: Deep Reinforcement Learning for Multi-Echelon Inventory Optimisation
type: item
description: Inventory management is a crucial domain in the retail sector that influences various operations. Managing inventory generically implies having the right items, at the right price, in the right quantity, in the right place, at the right time, and in the right condition. This task ensures that goods are available when needed, service levels are maintained, and costs of excess stock are minimized.
subject: Articles
  topic: section
subject: dynamic safety stock calibration
  topic: keywords
subject: replenishment intelligence
  topic: keywords
subject: deep reinforcement learning
  topic: keywords
subject: multi-echelon inventory optimisation
  topic: keywords
subject: machine learning
  topic: keywords
creator: Andrés Ortiz
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/
date: 2023-12-31
publication date: 2023-12-31
starting page: 51
ending page: 59

Demand-Supply Synchronisation Through Predictive Modelling: A Machine Learning Framework for End-to-End Supply Chain Optimisation
type: item
description: In the contemporary global marketplace, supply chain management and operational efficiency are key to success. Supply chains need to be able to adapt and plan for changing customer and business requirements but are often measured on how efficiently resources are used within their operations, since a highly efficient supply chain is a cost-effective one and a cost-effective supply chain can offer increased return on investment for both suppliers and customers.
subject: Articles
  topic: section
subject: demand-supply synchronisation
  topic: keywords
subject: predictive modelling
  topic: keywords
subject: machine learning framework
  topic: keywords
subject: end-to-end supply chain optimisation
  topic: keywords
creator: Charalampos Stylios
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/
date: 2023-12-31
publication date: 2023-12-31
starting page: 40
ending page: 50

Continuous Physiological Signal Analysis and Clinical Alert Generation: Real-Time Machine Learning Systems for Chronic Disease Monitoring and Management
type: item
description: The incorporation of technology into healthcare is predicted to significantly improve patient outcomes, improve overall patient care, assist healthcare professionals in making clinical decisions, and enable the rapid diagnosis and management of a range of health conditions. An area of growing interest in healthcare is real-time systems as part of the Internet of Things.
subject: Articles
  topic: section
subject: continuous physiological signal analysis
  topic: keywords
subject: clinical alert generation
  topic: keywords
subject: real-time machine learning systems
  topic: keywords
subject: chronic disease monitoring
  topic: keywords
creator: Fatima Ibrahim
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: 39