Blockchain Technology and Distributed Systems
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: India Language: English Editorial Contact - editor@thesciencebrigade.com
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Temporal Sequence Modelling and Anomaly Localisation: A Real-Time Deep Learning Framework for Insurance Claims Fraud Detection
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.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
Stochastic Portfolio Modelling Under Uncertainty: Machine Learning Approaches to Systemic Risk Assessment in Insurance Portfolios
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.
stochastic portfolio modelling under uncertainty
machine learning approaches to systemic risk assessment
Streaming Risk Scoring and Behavioural Pattern Recognition: A Real-Time Machine Learning Architecture for Insurance Fraud Risk Assessment
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.
Multi-Target Oncology Lead Identification Through Graph Neural Networks: AI-Driven Platforms for Cancer Drug Discovery
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.
multi-target oncology lead identification
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
Online Demand Sensing and Inventory Allocation Intelligence: A Real-Time Reinforcement Learning Framework for Supply-Demand Matching
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.
real-time reinforcement learning framework
Optical Character Recognition and Natural Language Classification in Insurance: Deep Learning Architectures for End-to-End Claims Processing Automation
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.
Predictive Adverse Event Detection and Proactive Clinical Intervention: Real-Time AI Architectures for Patient Safety Enhancement in Acute Care
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.
Predictive Excipient Compatibility and Dissolution Modelling: AI-Driven Optimisation of Solid Dosage Form Formulation
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.
Predictive Lead Time Compression in Multi-Tier Retail Networks: A Machine Learning Framework for Supplier Synchronisation
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.
Real-Time Behavioural Anomaly Detection in Digital Payments: A Supervised Learning Framework for Financial Transaction Fraud Identification
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.
financial transaction fraud identification
Regime-Aware Asset Allocation Through Recurrent Neural Networks: Machine Learning Approaches to Adaptive Investment Strategy Development
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.
machine learning approaches to adaptive investment strategy development
Metabolic Phenotype Modelling and Dietary Personalisation: Machine Learning Frameworks for Evidence-Based Nutrition and Lifestyle Intervention Optimisation
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.
Ligand-Target Affinity Prediction and Scaffold Optimisation: Deep Learning Approaches to Accelerated Small-Molecule Drug Discovery
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.
deep learning approaches to accelerated small-molecule drug discovery
Spatial Consumer Flow Analysis and Product Placement Intelligence: Machine Learning Approaches to Retail Store Layout Optimisation
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.
machine learning approaches to retail store layout optimisation
Intelligent Order Routing and Exception Handling in E-Commerce Logistics: Machine Learning Frameworks for Automated Fulfilment Optimisation
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.
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/
Intelligent Triage and Empathetic Dialogue Systems: Transformer-Based Architectures for AI-Augmented Insurance Customer Service
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.
Latent Consumer Behaviour Profiling in Omnichannel Environments: Deep Clustering Architectures for Precision Retail Segmentation
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.
Automated Plate Analysis and Hit Confirmation Intelligence: Machine Learning Architectures for High-Throughput Screening Workflow Optimisation
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.
high-throughput screening workflow optimisation
Copyright (c) 2021 https://creativecommons.org/licenses/by/4.0/
Biomarker-Stratified Therapeutic Targeting: Machine Learning Frameworks for Precision Drug Development and Patient-Centric Dosing
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.
biomarker-stratified therapeutic targeting
Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/
Collaborative Forecasting and Shared Inventory Intelligence: AI-Driven Frameworks for Multi-Partner Retail Supply Chain Synchronisation
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.
multi-partner retail supply chain synchronisation
Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/
Competitive Intelligence and Margin Preservation Through Predictive Analytics: An AI-Driven Framework for Retail Pricing Strategy Optimisation
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.
Automated Resolution Pathways in Insurance Claims: Reinforcement Learning Models for Settlement Process Optimisation
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.
Contextual Demand Elasticity Modelling and Competitive Pricing Intelligence: Reinforcement Learning for Dynamic Retail Price Optimisation
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.
Intelligent Nanocarrier Design and Release Kinetics Prediction: Machine Learning Approaches to Targeted Drug Delivery Optimisation
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.
machine learning approaches to targeted drug delivery optimisation
Intelligent Document Processing and Regulatory Pathway Mapping: AI-Enhanced Compliance Automation in Pharmaceutical Submissions
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.
Federated Learning and Knowledge Graph Integration: AI-Driven Platforms for Cross-Institutional Pharmaceutical Research Collaboration
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.
cross-institutional pharmaceutical research collaboration
Explainable Credit Decision Engines: Machine Learning Architectures for Automated Loan Origination and Approval
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.
Dynamic Safety Stock Calibration and Replenishment Intelligence: Deep Reinforcement Learning for Multi-Echelon Inventory Optimisation
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.
Demand-Supply Synchronisation Through Predictive Modelling: A Machine Learning Framework for End-to-End Supply Chain Optimisation
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.
Continuous Physiological Signal Analysis and Clinical Alert Generation: Real-Time Machine Learning Systems for Chronic Disease Monitoring and Management
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.
continuous physiological signal analysis