Journal of Computational Intelligence and Robotics
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: Worldwide 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, innovation, and collaboration. We strive to facilitate interdisciplinary exchange, foster academic discourse, and disseminate cutting-edge research to a global audience. By providing a platform for the dissemination of high-quality research, JCIR aims to drive innovation, inspire creativity, and contribute to the collective advancement of these dynamic fields.
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.
Spectroscopic Feature Extraction and Pathogen Classification: AI-Driven Systems for Enhanced Molecular Diagnostic Accuracy and Throughput
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.
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
Rare Variant Pathogenicity Classification and Phenotypic Pattern Recognition: AI-Driven Computational Approaches to Early Detection of Genetic Disorders
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.
rare variant pathogenicity classification
computational approaches to early detection
Risk Stratification and Lifestyle Modification Personalisation: Machine Learning Approaches to Enhanced Preventive Healthcare Delivery
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.
machine learning approaches to enhanced preventive healthcare delivery
Wearable Sensor Integration and Personalised Reminder Intelligence: Real-Time AI Systems for Medication Adherence Monitoring and Management
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.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
Tumour Mutational Burden Analysis and Immunotherapy Response Prediction: AI-Driven Systems for Personalised Oncology Treatment Optimisation
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.
personalised oncology treatment optimisation
Topological Data Analysis and Multi-Scale Biomedical Signal Processing: AI-Enhanced Systems for Complex Biomedical Data Analysis and Pattern Extraction
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.
multi-scale biomedical signal processing
Synergy Prediction Through Graph-Based Molecular Interaction Modelling: AI-Based Computational Approaches to Drug Combination Therapy Optimisation
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.
graph-based molecular interaction modelling
computational approaches to drug combination therapy optimisation
Structured Data Extraction and Regulatory Narrative Generation: Machine Learning Models for Automated Financial Reporting and Disclosure
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.
Straight-Through Processing and Exception Triage Intelligence: AI-Driven Frameworks for Insurance Claims Processing Efficiency Enhancement
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.
insurance claims processing efficiency enhancement
Streaming Network Analysis and Claim Velocity Scoring: Real-Time AI Frameworks for Insurance Fraud Detection and Prevention
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.
Heterogeneous Graph Networks and Behavioural Sequence Modelling: AI-Driven Computational Frameworks for Financial Fraud Detection
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.
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/
Intent Classification and Automated Claims Status Intelligence: AI-Driven Natural Language Processing Frameworks for Insurance Customer Service Automation
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.
Lifestyle Risk Factor Modelling and Adaptive Intervention Delivery: AI-Driven Platforms for Personalised Health Intervention in Preventive Care Settings
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.
Live-Cell Imaging Intelligence and Organelle Dynamics Quantification: Real-Time AI Platforms for Monitoring and Analysis of Cellular Processes
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.
Log Anomaly Detection and Incident Probability Forecasting: AI-Based Predictive Maintenance Frameworks for Financial Information Technology Infrastructure
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.
financial information technology infrastructure
Longitudinal Biomarker Trajectories and Transition State Modelling: Machine Learning Approaches to Disease Progression Prediction and Clinical Staging
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.
machine learning approaches to disease progression prediction
Macroeconomic Signal Integration and Competitive Landscape Modelling: AI-Driven Frameworks for Insurance Market Trend Analysis and Prediction
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.
Polygenic Risk Scoring and Treatment Response Prediction: Advances in Machine Learning Models for Precision Personalised Medicine
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.
Explainable Risk Factor Decomposition and Automated Underwriting Intelligence: AI-Based Decision Support Architectures for Insurance Underwriting
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.
Damage Quantification and Coverage Eligibility Inference: AI-Based Systems for Automated Insurance Claims Assessment and Adjudication
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.
Cross-Modal Data Fusion and Pathway Activity Inference: Machine Learning Approaches to Integrated Multi-Omics Data Analysis for Biological Discovery
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.
machine learning approaches to integrated multi-omics data analysis
Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/
Appointment Demand Forecasting and Slot Optimisation: AI-Based Solutions for Equitable Patient Scheduling and Healthcare Access Improvement
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.
Copyright (c) 2021 https://creativecommons.org/licenses/by/4.0/
Behavioural Segmentation and Next-Best-Action Modelling: AI-Enhanced Customer Relationship Management Frameworks in Retail Banking
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.
ai-enhanced customer relationship management frameworks
Credibility Theory Augmentation Through Gradient Boosting: AI-Enhanced Actuarial Modelling Frameworks for Insurance Pricing and Reserving
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.
ai-enhanced actuarial modelling frameworks
Dynamic Scaling of Machine Learning Workloads: A Comparative Study of On-Prem and Cloud-Based Containers
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.
Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
Energy Efficiency in Smart Factories: Leveraging IoT, AI, and Cloud Computing for Sustainable Manufacturing
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.
Copyright (c) 2021 https://creativecommons.org/licenses/by-nc-sa/4.0
Exploring Edge Computing Integrations with AWS CloudFront
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.
Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
Optimizing Elastic Kubernetes Services for High Availability Applications
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.
Reinforcement Learning from Human Feedback for Enhanced Code Generation and Debugging Capabilities in LLMs
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 in resolving intricate bugs across programming languages, highlighting notable improvements in debugging precision and response time. The effectiveness of RLHF-enhanced LLMs is evaluated through extensive experimentation on benchmark datasets, including real-world programming challenges and competitive coding scenarios. Metrics such as compilation success rates, logical correctness scores, and efficiency of generated code are utilized to quantify performance gains. Comparative analysis with traditional supervised learning models reveals that RLHF not only improves accuracy but also significantly reduces error propagation in iterative workflows. The research also explores generalization capabilities, showcasing the adaptability of RLHF-enhanced LLMs to novel programming languages and paradigms, thus extending their utility in diverse application domains. This study further addresses scalability and computational efficiency challenges inherent in RLHF pipelines. Techniques such as prioritized replay, efficient feedback sampling, and parallelized training architectures are investigated to mitigate resource constraints, enabling broader adoption in industrial and academic settings. Ethical considerations are also examined, particularly concerning the quality and bias of human feedback, ensuring that RLHF models uphold fairness and inclusivity standards. The paper concludes by outlining future research directions, including the integration of RLHF with multi-modal learning systems, the development of self-optimizing reward models, and the exploration of hierarchical RL techniques for complex task decomposition. By advancing the state of the art in LLMs for code generation and debugging, RLHF not only enhances the productivity of developers but also paves the way for more intelligent, autonomous coding systems. The insights and methodologies presented in this research aim to catalyze further innovation in leveraging human feedback for reinforcement learning, driving advancements in AI-driven programming tools.
reinforcement learning from human feedback
Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
Multi-Cloud Security Event Aggregation and Normalization Using Advanced AI/ML Techniques
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 evaluation of the proposed framework within AWS Security Hub, Google Chronicle, and Azure Sentinel. Each platform is analyzed for its unique logging mechanisms, APIs, and security event schemas. The paper describes the design and deployment of custom connectors and parsers that interface with these platforms, leveraging cloud-native tools and AI/ML models for real-time log processing. Performance metrics, including log normalization accuracy, deduplication rates, and processing latency, are presented to demonstrate the effectiveness of the framework. Furthermore, this study emphasizes the scalability and adaptability of the proposed system. By employing transfer learning and modular architectures, the framework can be extended to accommodate emerging cloud platforms and evolving log schemas. The implications of this work extend beyond multi-cloud environments, offering valuable insights for enterprise security operations centers (SOCs) that manage diverse and voluminous security data. The research concludes by addressing limitations and future directions. Key challenges, such as computational overhead, data privacy concerns, and the need for continual model retraining, are discussed alongside potential solutions, including federated learning and edge AI techniques. Additionally, the paper highlights opportunities for integrating this framework with broader cybersecurity paradigms, such as Security Information and Event Management (SIEM) systems and Threat Intelligence Platforms (TIPs).