Journal of Computational Intelligence and Robotics
type: channel
description: The "Journal of Computational Intelligence and Robotics" (JCIR) stands as a pioneering platform at the forefront of interdisciplinary research, marrying the realms of computational intelligence and robotics to foster innovation and transformative breakthroughs. With a relentless pursuit of excellence, JCIR endeavors to push the boundaries of knowledge in these dynamic fields, serving as a beacon for scholars, researchers, and practitioners alike. Journal Snapshot Journal Name: Journal of Computational Intelligence and Robotics (JCIR) Editor-in-Chief: Shreyas Shrimant Shinde (see Editorial Board) Publisher Imprint: The Science Brigade Publishers Publisher: Libertatem Media Private Limited Journal Initials: JCIR Research Scope: Computational Intelligence, Artificial Intelligence, Machine Learning and Deep Learning, Robotics Engineering, Autonomous Systems, Computer Vision, Natural Language Processing, Intelligent Control and Automation, Multi-Agent and Distributed Systems, Cognitive and Bio-Inspired Computing and many more. 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 Scope: JCIR welcomes high-quality contributions spanning a wide spectrum of topics within computational intelligence and robotics, including but not limited to: Machine learning algorithms and techniques Evolutionary computation and optimization Swarm intelligence and collective robotics Cognitive robotics and artificial intelligence Autonomous systems and robotics Human-robot interaction and collaboration Intelligent control and automation Applications of computational intelligence in robotics, such as healthcare, manufacturing, transportation, and more. Mission: Our mission at JCIR is to advance the frontiers of computational intelligence and robotics through rigorous research, innovat…
publisher: The Science Brigade Publishers
language: en
publication name: Journal of Computational Intelligence and Robotics
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: Spectroscopic Feature Extraction and Pathogen Classification: AI-Driven Systems for Enhanced Molecular Diagnostic Accuracy and Throughput
items: Rare Variant Pathogenicity Classification and Phenotypic Pattern Recognition: AI-Driven Computational Approaches to Early Detection of Genetic Disorders
items: Risk Stratification and Lifestyle Modification Personalisation: Machine Learning Approaches to Enhanced Preventive Healthcare Delivery
items: Wearable Sensor Integration and Personalised Reminder Intelligence: Real-Time AI Systems for Medication Adherence Monitoring and Management
items: Tumour Mutational Burden Analysis and Immunotherapy Response Prediction: AI-Driven Systems for Personalised Oncology Treatment Optimisation
items: Topological Data Analysis and Multi-Scale Biomedical Signal Processing: AI-Enhanced Systems for Complex Biomedical Data Analysis and Pattern Extraction
items: Synergy Prediction Through Graph-Based Molecular Interaction Modelling: AI-Based Computational Approaches to Drug Combination Therapy Optimisation
items: Structured Data Extraction and Regulatory Narrative Generation: Machine Learning Models for Automated Financial Reporting and Disclosure
items: Straight-Through Processing and Exception Triage Intelligence: AI-Driven Frameworks for Insurance Claims Processing Efficiency Enhancement
items: Streaming Network Analysis and Claim Velocity Scoring: Real-Time AI Frameworks for Insurance Fraud Detection and Prevention
items: Heterogeneous Graph Networks and Behavioural Sequence Modelling: AI-Driven Computational Frameworks for Financial Fraud Detection
items: Intent Classification and Automated Claims Status Intelligence: AI-Driven Natural Language Processing Frameworks for Insurance Customer Service Automation
items: Lifestyle Risk Factor Modelling and Adaptive Intervention Delivery: AI-Driven Platforms for Personalised Health Intervention in Preventive Care Settings
items: Live-Cell Imaging Intelligence and Organelle Dynamics Quantification: Real-Time AI Platforms for Monitoring and Analysis of Cellular Processes
items: Log Anomaly Detection and Incident Probability Forecasting: AI-Based Predictive Maintenance Frameworks for Financial Information Technology Infrastructure
items: Longitudinal Biomarker Trajectories and Transition State Modelling: Machine Learning Approaches to Disease Progression Prediction and Clinical Staging
items: Macroeconomic Signal Integration and Competitive Landscape Modelling: AI-Driven Frameworks for Insurance Market Trend Analysis and Prediction
items: Polygenic Risk Scoring and Treatment Response Prediction: Advances in Machine Learning Models for Precision Personalised Medicine
items: Explainable Risk Factor Decomposition and Automated Underwriting Intelligence: AI-Based Decision Support Architectures for Insurance Underwriting
items: Damage Quantification and Coverage Eligibility Inference: AI-Based Systems for Automated Insurance Claims Assessment and Adjudication
items: Cross-Modal Data Fusion and Pathway Activity Inference: Machine Learning Approaches to Integrated Multi-Omics Data Analysis for Biological Discovery
items: Appointment Demand Forecasting and Slot Optimisation: AI-Based Solutions for Equitable Patient Scheduling and Healthcare Access Improvement
items: Behavioural Segmentation and Next-Best-Action Modelling: AI-Enhanced Customer Relationship Management Frameworks in Retail Banking
items: Credibility Theory Augmentation Through Gradient Boosting: AI-Enhanced Actuarial Modelling Frameworks for Insurance Pricing and Reserving
items: Dynamic Scaling of Machine Learning Workloads: A Comparative Study of On-Prem and Cloud-Based Containers
items: Energy Efficiency in Smart Factories: Leveraging IoT, AI, and Cloud Computing for Sustainable Manufacturing
items: Exploring Edge Computing Integrations with AWS CloudFront
items: Optimizing Elastic Kubernetes Services for High Availability Applications
items: Reinforcement Learning from Human Feedback for Enhanced Code Generation and Debugging Capabilities in LLMs
items: Multi-Cloud Security Event Aggregation and Normalization Using Advanced AI/ML Techniques

Spectroscopic Feature Extraction and Pathogen Classification: AI-Driven Systems for Enhanced Molecular Diagnostic Accuracy and Throughput
type: item
description: Molecular diagnostics, the branch of diagnostics dealing with the analysis of biological substances at the molecular level, has become a vital part of disease diagnosis. In molecular diagnostics, artificial biomarkers or biomolecules are identified that can bind with the disease-conditioned native biomarkers or biomolecules.
subject: Articles
  topic: section
subject: spectroscopic feature extraction
  topic: keywords
subject: pathogen classification
  topic: keywords
subject: systems
  topic: keywords
subject: enhanced molecular diagnostic accuracy
  topic: keywords
subject: machine learning
  topic: keywords
creator: Yan Zhang
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-12-31
publication date: 2025-12-31
starting page: 20
ending page: 28

Rare Variant Pathogenicity Classification and Phenotypic Pattern Recognition: AI-Driven Computational Approaches to Early Detection of Genetic Disorders
type: item
description: Genetic disorders are becoming increasingly common, with 4–6% and 36% of children being born with a major or minor genetic disorder, respectively. These disorders affect an individual’s mental and physical health and lead to significant levels of stress in families. Early detection is essential as it can lead to early intervention that can prevent or ameliorate many of these disorders.
subject: Articles
  topic: section
subject: rare variant pathogenicity classification
  topic: keywords
subject: phenotypic pattern recognition
  topic: keywords
subject: computational approaches to early detection
  topic: keywords
subject: genetic disorders
  topic: keywords
subject: machine learning
  topic: keywords
creator: Giovanna Di Guglielmo
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

Risk Stratification and Lifestyle Modification Personalisation: Machine Learning Approaches to Enhanced Preventive Healthcare Delivery
type: item
description: Growing trends in preventive and personalized healthcare initiatives used in recent times highlight the relevance and timeliness of these emerging fields. Databases and atlases, which promote personalized nutrition and proactivity against future diseases based on genetic makeup or lifestyles, are gaining momentum. This is important because numerous diseases require early detection and timely treatment.
subject: Articles
  topic: section
subject: risk stratification
  topic: keywords
subject: lifestyle modification personalisation
  topic: keywords
subject: machine learning approaches to enhanced preventive healthcare delivery
  topic: keywords
creator: Jing Li
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: 19

Wearable Sensor Integration and Personalised Reminder Intelligence: Real-Time AI Systems for Medication Adherence Monitoring and Management
type: item
description: Medication adherence can be defined as the degree to which patients take the prescribed dose of medication within the agreed interval and follow a described regimen. Patient non-adherence to medications can have a significant impact on health outcomes and health costs; patients who were not adherent to their medication were more likely to have poor outcomes than those who were adherent. Numerous different barriers to adherence to medications have been identified.
subject: Articles
  topic: section
subject: wearable sensor integration
  topic: keywords
subject: personalised reminder intelligence
  topic: keywords
subject: real-time ai systems
  topic: keywords
subject: medication adherence monitoring
  topic: keywords
subject: machine learning
  topic: keywords
creator: Arvind Pandey
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: 40

Tumour Mutational Burden Analysis and Immunotherapy Response Prediction: AI-Driven Systems for Personalised Oncology Treatment Optimisation
type: item
description: Personalized oncology treatments are becoming increasingly critical in patient care. This development is attributed to the heterogeneity of each patient’s condition. Although different individuals may have the same cancer type, each case potentially develops due to a unique interplay of genetic, environmental, or lifestyle factors. Conventional one-size-fits-all strategies, therefore, may not be adequate to ameliorate the survival or prognosis of such cancer patients.
subject: Articles
  topic: section
subject: tumour mutational burden analysis
  topic: keywords
subject: immunotherapy response prediction
  topic: keywords
subject: systems
  topic: keywords
subject: personalised oncology treatment optimisation
  topic: keywords
subject: machine learning
  topic: keywords
creator: Kwame Nkrumah
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

Topological Data Analysis and Multi-Scale Biomedical Signal Processing: AI-Enhanced Systems for Complex Biomedical Data Analysis and Pattern Extraction
type: item
description: Recent developments in AI have played a significant role in revolutionizing different areas of biomedical data analysis. Modern high-throughput assays allow the generation of the most comprehensive description of the living cell at the individual molecular level on a grand scale. Large-scale omics data generation techniques have not only allowed the development of detailed and accelerating knowledge about genes, gene products, metabolites, and interaction networks.
subject: Articles
  topic: section
subject: topological data analysis
  topic: keywords
subject: multi-scale biomedical signal processing
  topic: keywords
subject: ai-enhanced systems
  topic: keywords
subject: complex biomedical data analysis
  topic: keywords
subject: machine learning
  topic: keywords
creator: Joseph Msabila
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-06-30
publication date: 2026-06-30
starting page: 17
ending page: 24

Synergy Prediction Through Graph-Based Molecular Interaction Modelling: AI-Based Computational Approaches to Drug Combination Therapy Optimisation
type: item
description: Drug Combination Therapy. Systemic diseases, such as infectious diseases and cancer, are the result of complex multifactorial interactions of biological systems. Monotherapies face limitations of less than ideal pharmacokinetics, high toxicity due to required high dosages, or the inevitable physiologically optimized signal to cell growth. For these reasons, medical treatments are looking more and more to combination therapies to treat such new diseases.
subject: Articles
  topic: section
subject: synergy prediction
  topic: keywords
subject: graph-based molecular interaction modelling
  topic: keywords
subject: computational approaches to drug combination therapy optimisation
  topic: keywords
subject: machine learning
  topic: keywords
creator: Anna Wilson
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-06-30
publication date: 2026-06-30
starting page: 9
ending page: 16

Structured Data Extraction and Regulatory Narrative Generation: Machine Learning Models for Automated Financial Reporting and Disclosure
type: item
description: Reliability, automation, and accuracy in financial reporting have become more pertinent than ever with rising regulatory requirements, rapid expansion in business activity, and increasing complexities, as these require analysis of larger volumes of financial data than ever before. Artificial intelligence (AI) could be a savior in this world to drive efficient, automated, and compliant financial reporting.
subject: Articles
  topic: section
subject: structured data extraction
  topic: keywords
subject: regulatory narrative generation
  topic: keywords
subject: machine learning models
  topic: keywords
subject: automated financial reporting
  topic: keywords
creator: Xiaojing Wang
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: 8

Straight-Through Processing and Exception Triage Intelligence: AI-Driven Frameworks for Insurance Claims Processing Efficiency Enhancement
type: item
description: Claims processing is a fundamental task for the insurance industry and is pivotal in maintaining healthy payer operations. Health insurance companies alone continue spending millions of dollars primarily to manage the high volume of claims received and adjudicate claims from various channels like customer enrollment, doctor billings, laboratories, hospitals, and pharmacies.
subject: Articles
  topic: section
subject: straight-through processing
  topic: keywords
subject: exception triage intelligence
  topic: keywords
subject: frameworks
  topic: keywords
subject: insurance claims processing efficiency enhancement
  topic: keywords
subject: machine learning
  topic: keywords
creator: Derek Lichti
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-12-31
publication date: 2025-12-31
starting page: 29
ending page: 43

Streaming Network Analysis and Claim Velocity Scoring: Real-Time AI Frameworks for Insurance Fraud Detection and Prevention
type: item
description: The insurance industry is plagued with an onslaught of fraudulent claims. In 2020 alone, U.S. insurance carriers collectively lost 7% to 10% of earned premiums due to fraud, costing an estimated $80 billion. As a result, one of the biggest priorities for many carriers is innovating their fraud detection and prevention methods. Instead of waiting for the claim to be paid and then seeking restitution, insurance carriers would greatly prefer to preemptively stop the payment from going out.
subject: Articles
  topic: section
subject: streaming network analysis
  topic: keywords
subject: claim velocity scoring
  topic: keywords
subject: real-time ai frameworks
  topic: keywords
subject: insurance fraud detection
  topic: keywords
subject: machine learning
  topic: keywords
creator: Fei-Fei Li
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-12-31
publication date: 2025-12-31
starting page: 44
ending page: 51

Heterogeneous Graph Networks and Behavioural Sequence Modelling: AI-Driven Computational Frameworks for Financial Fraud Detection
type: item
description: While the world has seen some significant technological advancements during the last two decades in the field of finance, there has also been a parallel increase in fraudulent activities in financial organizations. The advent of complex financial products and changing financial regulations has only made matters worse. Traditional fraud detection methods in financial services, which were once considered adequate, have been failing for some time now.
subject: Articles
  topic: section
subject: heterogeneous graph networks
  topic: keywords
subject: behavioural sequence modelling
  topic: keywords
subject: computational frameworks
  topic: keywords
subject: financial fraud detection
  topic: keywords
subject: machine learning
  topic: keywords
creator: Andreas Papadopoulos
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/
date: 2024-12-31
publication date: 2024-12-31
starting page: 22
ending page: 37

Intent Classification and Automated Claims Status Intelligence: AI-Driven Natural Language Processing Frameworks for Insurance Customer Service Automation
type: item
description: Insurance is a sector where being able to provide quality customer service is critical in an increasingly competitive market. It is equally well-suited to the application of AI, with a vast array of opportunities to improve customer interactions. The majority of these are improving for the customer what can be a frustrating backlog of queries, such as policy and price queries via the supply of AI chatbots.
subject: Articles
  topic: section
subject: intent classification
  topic: keywords
subject: automated claims status intelligence
  topic: keywords
subject: natural language processing frameworks
  topic: keywords
subject: insurance customer service automation
  topic: keywords
subject: machine learning
  topic: keywords
creator: Małgorzata Michalewicz
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/
date: 2024-12-31
publication date: 2024-12-31
starting page: 38
ending page: 45

Lifestyle Risk Factor Modelling and Adaptive Intervention Delivery: AI-Driven Platforms for Personalised Health Intervention in Preventive Care Settings
type: item
description: The existing strategies to promote health typically involve generic interventions and often reach only a relatively small portion of the population, mostly high-risk individuals, even though these individuals account for only a small percentage of the population's health problems. Allowing individuals to change their health behaviors can help lower the risks of many physical and mental disorders.
subject: Articles
  topic: section
subject: lifestyle risk factor modelling
  topic: keywords
subject: adaptive intervention delivery
  topic: keywords
subject: platforms
  topic: keywords
subject: personalised health intervention
  topic: keywords
subject: machine learning
  topic: keywords
creator: Olga Petrova
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/
date: 2024-12-31
publication date: 2024-12-31
starting page: 46
ending page: 53

Live-Cell Imaging Intelligence and Organelle Dynamics Quantification: Real-Time AI Platforms for Monitoring and Analysis of Cellular Processes
type: item
description: Developing real-time artificial intelligence (AI) solutions to investigate cellular biology in living conditions is gaining interest. As cellular biology evolves, and as we get to know more about mutually supporting networks of regulatory functions, including genetic and epigenetic networks, interest in tracing cellular operations increases. In this context, the real-time acquisition of single-cell data can show the biological systems in their whole complexity.
subject: Articles
  topic: section
subject: live-cell imaging intelligence
  topic: keywords
subject: organelle dynamics quantification
  topic: keywords
subject: real-time ai platforms
  topic: keywords
subject: monitoring
  topic: keywords
subject: machine learning
  topic: keywords
creator: Olga Volkova
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

Log Anomaly Detection and Incident Probability Forecasting: AI-Based Predictive Maintenance Frameworks for Financial Information Technology Infrastructure
type: item
description: Financial institutions are highly dependent on their information systems and networks. It is critical for banks, brokers, and insurance agencies to quickly identify and repair failures in these networks in order to prevent their paralyzing impact. Modern IT solutions based on artificial intelligence, as well as event correlators and monitoring systems designed to identify fault conditions in networks, can help in this selective search for an observed problem.
subject: Articles
  topic: section
subject: log anomaly detection
  topic: keywords
subject: incident probability forecasting
  topic: keywords
subject: predictive maintenance frameworks
  topic: keywords
subject: financial information technology infrastructure
  topic: keywords
subject: machine learning
  topic: keywords
creator: Yasemin Şahin
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: 18

Longitudinal Biomarker Trajectories and Transition State Modelling: Machine Learning Approaches to Disease Progression Prediction and Clinical Staging
type: item
description: Introduction Disease progression prediction is a crucial part of the arsenal of tools available to healthcare providers. By annotating the future states of an individual according to a disease’s natural history, diagnosis and treatment decisions become much more efficient and effective. Biologically, diseases are typically driven by a complex interplay of genetic, lifestyle, and environmental factors that interact according to the disease context to determine disease initiation and progression.
subject: Articles
  topic: section
subject: longitudinal biomarker trajectories
  topic: keywords
subject: transition state modelling
  topic: keywords
subject: machine learning approaches to disease progression prediction
  topic: keywords
subject: clinical staging
  topic: keywords
creator: Daniela Ramos
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-06-30
publication date: 2025-06-30
starting page: 19
ending page: 26

Macroeconomic Signal Integration and Competitive Landscape Modelling: AI-Driven Frameworks for Insurance Market Trend Analysis and Prediction
type: item
description: The increasing shift toward automation is transforming the insurance industry, among others. Specifically, the interest of insurance companies is shifting from digital-only to artificial intelligence-driven, making many insurance processes, like underwriting and pricing, more effective and accurate. An AI evolution reaches five phases of adoption. AI, combined with machine learning and data analytics, is used to group and analyze in-depth insights on market developments and trends.
subject: Articles
  topic: section
subject: macroeconomic signal integration
  topic: keywords
subject: competitive landscape modelling
  topic: keywords
subject: frameworks
  topic: keywords
subject: insurance market trend analysis
  topic: keywords
subject: machine learning
  topic: keywords
creator: Paulo Sérgio
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-06-30
publication date: 2025-06-30
starting page: 27
ending page: 34

Polygenic Risk Scoring and Treatment Response Prediction: Advances in Machine Learning Models for Precision Personalised Medicine
type: item
description: Personalized approaches are becoming pivotal for modern health care. For many years, standard clinical protocols would assume that every patient with the same symptoms should receive the same type of treatment. However, everyone can have different genotypes, backgrounds, symptoms, and various treatment histories. Therefore, personalized medicine suggests that treatment be customized for the individual characteristics of each patient.
subject: Articles
  topic: section
subject: polygenic risk scoring
  topic: keywords
subject: treatment response prediction
  topic: keywords
subject: advances
  topic: keywords
subject: machine learning models
  topic: keywords
creator: Sudarshan Bhattacharyya
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-06-30
publication date: 2025-06-30
starting page: 35
ending page: 45

Explainable Risk Factor Decomposition and Automated Underwriting Intelligence: AI-Based Decision Support Architectures for Insurance Underwriting
type: item
description: Traditionally, the maintained and evaluated records of Quiet often today, underwriters are having to develop stricter guidelines with regard to class evaluation, therefore putting more pressure on the increasingly data-driven decisions made by the underwriting teams. This trend is significantly impacted by the exponentially evolving technology, which is transforming processes previously governed by conventions and talent in taste.
subject: Articles
  topic: section
subject: explainable risk factor decomposition
  topic: keywords
subject: automated underwriting intelligence
  topic: keywords
subject: decision support architectures
  topic: keywords
subject: insurance underwriting
  topic: keywords
subject: machine learning
  topic: keywords
creator: Theophilus Akinbami
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/
date: 2024-12-31
publication date: 2024-12-31
starting page: 13
ending page: 21

Damage Quantification and Coverage Eligibility Inference: AI-Based Systems for Automated Insurance Claims Assessment and Adjudication
type: item
description: A crucial step in the insurance process is the assessment of claims. Traditionally, insurance assessors would process claims by reviewing documentation and determining the validity of the claim after subjective human evaluation. While these traditional methodologies were unstructured and time-consuming, recent studies have explored applying intelligent systems to the claim assessment process to expedite it.
subject: Articles
  topic: section
subject: damage quantification
  topic: keywords
subject: coverage eligibility inference
  topic: keywords
subject: systems
  topic: keywords
subject: automated insurance claims assessment
  topic: keywords
subject: machine learning
  topic: keywords
creator: Tatyana Lyalina
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: 12

Cross-Modal Data Fusion and Pathway Activity Inference: Machine Learning Approaches to Integrated Multi-Omics Data Analysis for Biological Discovery
type: item
description: Cellular phenotypes emerge as a result of complex interactions between a wide variety of molecules. To understand the performance of these biological systems may require a comprehensive knowledge of genetic, transcriptomic, epigenetic, proteomic, and many other mechanisms involved in cell regulation and function.
subject: Articles
  topic: section
subject: cross-modal data fusion
  topic: keywords
subject: pathway activity inference
  topic: keywords
subject: machine learning approaches to integrated multi-omics data analysis
  topic: keywords
subject: biological discovery
  topic: keywords
creator: Maria Cláudia Barbosa
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/
date: 2023-12-31
publication date: 2023-12-31
starting page: 138
ending page: 147

Appointment Demand Forecasting and Slot Optimisation: AI-Based Solutions for Equitable Patient Scheduling and Healthcare Access Improvement
type: item
description: One of the most pressing issues that must be addressed to optimally manage patient demand increasing at an astronomical rate is to come up with efficient means for coordinating patient care. This coordination involves effective patient scheduling aiming to minimize patient waiting times while at the same time making optimal use of practitioners and resources such as imaging facilities and day surgery theaters.
subject: Articles
  topic: section
subject: appointment demand forecasting
  topic: keywords
subject: slot optimisation
  topic: keywords
subject: solutions
  topic: keywords
subject: equitable patient scheduling
  topic: keywords
subject: machine learning
  topic: keywords
creator: Sébastien Lachapelle
rights: Copyright (c) 2021 https://creativecommons.org/licenses/by/4.0/
date: 2021-06-30
publication date: 2021-06-30
starting page: 56
ending page: 65

Behavioural Segmentation and Next-Best-Action Modelling: AI-Enhanced Customer Relationship Management Frameworks in Retail Banking
type: item
description: Customer Relationship Management (CRM) has always played a crucial role in various industries, but it is more vital for banks due to the increasing competition they face today. Not only does having effective CRM help to retain the existing customer base, but it also helps build stronger, long-lasting relationships with customers. These relationships can add significant value through cross-selling and up-selling products and services.
subject: Articles
  topic: section
subject: behavioural segmentation
  topic: keywords
subject: next-best-action modelling
  topic: keywords
subject: ai-enhanced customer relationship management frameworks
  topic: keywords
subject: retail banking
  topic: keywords
subject: machine learning
  topic: keywords
creator: Mohammad Sawan
rights: Copyright (c) 2021 https://creativecommons.org/licenses/by/4.0/
date: 2021-06-30
publication date: 2021-06-30
starting page: 66
ending page: 77

Credibility Theory Augmentation Through Gradient Boosting: AI-Enhanced Actuarial Modelling Frameworks for Insurance Pricing and Reserving
type: item
description: Actuarial science is the discipline that applies mathematical and statistical methods to analyze risk in insurance, finance, and related fields. In like manner, actuarial science evaluates past data to assess the probability of a future trauma and design plans that help alleviate the financial effect of trauma based on the evaluation of the probability of the trauma.
subject: Articles
  topic: section
subject: credibility theory augmentation
  topic: keywords
subject: gradient boosting
  topic: keywords
subject: ai-enhanced actuarial modelling frameworks
  topic: keywords
subject: insurance pricing
  topic: keywords
subject: machine learning
  topic: keywords
creator: Jingxuan He
rights: Copyright (c) 2021 https://creativecommons.org/licenses/by/4.0/
date: 2021-12-31
publication date: 2021-12-31
starting page: 89
ending page: 96

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

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

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

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

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

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