Edinburg Journal of Natural Language Processing and AI
The Edinburgh Journal of Natural Language Processing and AI (EJNLPAI) is a peer-reviewed, open-access academic journal that provides a specialized platform for advanced research in natural language processing (NLP), computational linguistics, and artificial intelligence. Established in 2017 and published annually from Edinburgh, Scotland, EJNLPAI curates high-impact scholarship ranging from foundational NLP theories to cutting-edge AI applications in text analytics, language generation, semantic modeling, and conversational agents. It supports multidisciplinary work that bridges linguistics, deep learning, cognitive computing, and AI ethics. The journal adheres to a rigorous double-blind peer review process and global editorial standards. Journal Snapshot Journal Name: Edinburgh Journal of Natural Language Processing and AI (EJNLPAI) ISSN: 2789-5627 Impact Factor: 5.8 (By ResearchBib) Journal Initials: EJNLPAI Research Scope: NLP, Computational Linguistics, Machine Translation, Text Analytics, AI in Language Understanding, Language Modeling, Conversational AI, Cognitive Computing Publication Mode: Digital (On this Website) Frequency: Annual (1 Volume per Year) Launch Year: 2017 Review Mode: Double Blind Peer Review Plagiarism Allowed: 10% (as per Turnitin) Coverage: UK and Global Language: English
Incremental Schema Fingerprinting with MinHash for Drift Detection in Data Lakes
The work incrementally fingerprints MinHash schema. Finding schema drift in huge data lakes is quicker and easier with this strategy. This method creates small hashes of changing table schema column names, data types, and semantic tags. Weekly lightweight comparisons may show quality or compliance issues. Structured monitoring employing semantic fingerprinting may uncover minor schema semantic changes that mask origin or governance. Open-source datasets and real-world intake evaluate system accuracy, recall, and computation runtime. To assist data stewards examine schemas, metadata catalogue systems identify and display schema drift. Experimental findings show reliable, space-efficient detection for near-real-time governance enforcement in petabyte data lakes. Compliance-aware observability and scalable summarisation improve schema monitoring.
Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-nd/4.0
Using Machine Learning for Real-Time Analysis of High-Throughput Biological Data
ML and high-throughput biological data have revolutionised bioinformatics, helping us comprehend complex biological processes. Strong computers can analyse and interpret massive amounts of RNA sequencing (RNA-Seq) and mass spectrometry data, which is rising quickly. Real-time machine learning retrieves biological data from massive databases. Statistics cannot handle noisy, high-dimensional, non-linear biological data like ML. Most transcriptome profiling methods use RNA-Seq, which produces millions of sequences per sample. High-dimensional data pattern discovery, organisation, and forecasting need complex processing. Supervised and unsupervised machine learning can detect gene regulatory networks, track gene expression, and predict cell responses. Mistakes and biases in RNA-Seq sequencing hinder gene expression analysis. Random forests, SVMs, and deep learning architectures have created strong gene expression profile representations to solve these difficulties. More accuracy and reliability. For proteomics research, mass spectrometry delivers complex, dynamic data. Deep and reinforcement learning are common mass spectrometry data analysis methods. It studies protein, peptide, and post-translational modifications. ML finds protein isoforms, biomarkers, and interactions. ML feature extraction accelerates processing. ML on high-throughput biological data remains tough despite progress. Tag-free training data hinders supervised learning. On little datasets, ML algorithms may overfit or fail to generalise. Researchers improve models using semi-supervised, unsupervised, and transfer learning without large annotated datasets. Strong ML models are challenging to understand since biological data is connected. XAI represents biological knowledge ML model selection. Real-time biological data analysis requires fast computers. Personalised medicine and large-scale epidemiology need real-time high-throughput data processing. Real-time bioinformatics requires improved ML models for streaming data, parallel computation, and cloud computing. HPC and cloud systems evaluate huge biological data live. AI may find biomarkers, disease subtypes, and therapy targets. These findings may impact medication and tailored medicine development. The ML may find biological correlations statistics overlook. ML algorithms can learn biology and disease from transcriptomics, proteomics, and genetics. Precision medicine predicts and cures complex diseases utilising multi-omics data. But machine learning and high-throughput biological data processing have restrictions. Batch effects, variable data, and experimental platforms reduce reproducibility. Test ML after standardising data pretreatment and normalisation. Machine learning in biomedical research raises ethical and legal concerns. Patient data must be safe in healthcare. This work analyses high-throughput biological data using machine learning. The issues and solutions of ML algorithms for RNA-Seq and mass spectrometry data processing are presented. Bioinformatics ML. Tool shows complex biological processes. Finally, it discusses how multi-omics data, deep learning, explainable AI, and real-time data analysis will transform bioinformatics machine learning.
Copyright (c) 2020 https://creativecommons.org/licenses/by-nc-nd/4.0
Dynamic Pricing Strategies in E-Commerce: Leveraging Machine Learning for Real-Time Decision-Making
E-commerce's rapid growth altered pricing. They change prices in real time using dynamic pricing based on numerous variables. Machine learning (ML) can analyse complex, multidimensional data streams including market trends, competitor behaviour, and customer buying patterns to create dynamic pricing models. This article shows how ML-driven dynamic pricing boosts income, gives you an advantage, and customises purchases. Dynamic pricing incorporates deep learning, reinforcement, and supervised/unsupervised learning. We employ these algorithms in e-commerce. Introduction: Dynamic pricing matters online. E-commerce platforms immediately adjust to demand, supply chain issues, and competition via algorithms. Comparison between machine learning vs rule-based pricing strengths and downsides. It shows how big data analytics may help ML adapt quickly to market changes. Site scraping, sales history, customer contact logs, and social media analytics are their tasks. This aids pricing judgements. The paper analyses ML-based dynamic pricing. Demand and baseline price prediction using linear and logistic regression. Random Forests, Decision Trees, and GBMs mimic non-linear price-consumer response relationships. The study also examines how neural networks, particularly deep learning models, can accurately predict prices from complicated patterns in high-dimensional data sets. Long-term profit-maximizing adaptive pricing incorporates reinforcement learning. Trial-and-error and feedback loops may assist RL algorithms model prices. This method fits fast-changing clientele or marketplaces. Case studies demonstrate how RL-driven dynamic pricing boosts e-commerce responsiveness and profitability. Dynamic pricing machine learning issues include database quality, feature engineering, computing costs, and algorithmic bias. This matters since it may affect model fairness and accuracy. Data governance and morality are needed for machine learning pricing. Also, consumer safety and law. Model clarity increases client trust and regulatory compliance. Data administration, e-commerce platform upgrades, and machine learning model training/retraining are addressed. ML-driven dynamic pricing, inventory management, and CRM systems demonstrate how a well-connected technology ecosystem may improve strategic choices and benefit everyone. A research suggests consumer behaviour analysis might help firms price. Market segmentation and clustering allow machine learning consumer pricing. Product reviews, purchase history, and surfing may assist organisations assess price sensitivity and readiness to pay. Price targeting may keep consumers pleased and loyal. The research contrasts ML-based and conventional dynamic pricing. Meta-analysis of Amazon, Alibaba, and other e-commerce giants' studies and examples reveals machine learning can optimise price. These case studies demonstrate how machine learning-based pricing may increase revenue, reduce expenses, and enhance inventory management. Real-time data analysis is needed for customer and market advancements. The research suggests e-commerce dynamic pricing using machine learning. AI may provide more complicated pricing approaches with better ML frameworks and more data sets. Data security and competitive pricing study may employ machine learning, blockchain, and multi-agent systems. After additional study and adaptation, ML-driven dynamic pricing may help e-commerce enterprises negotiate complex and competitive markets.
Copyright (c) 2019 https://creativecommons.org/licenses/by-nc-nd/4.0
Deep Learning Algorithms for Demand Forecasting and Inventory Optimization in Global Supply Chains
Growing worldwide supply networks need new computer technology for efficiency and reaction. Recent deep learning algorithms have tackled these difficulties, notably demand prediction and inventory optimisation. LSTMs and CNNs may help complicated supply chain networks estimate demand and manage inventories in real time. Long-used time series forecasting methods include ARIMA and exponential smoothing. Because they cannot capture complicated, non-linear patterns and interdependencies in large datasets, they seldom function. This is particularly true for complex, shifting global marketplaces. Deep learning improves forecasts and operational choices by modelling complex relationships and learning from massive data sets. RNNs like LSTM may forecast time series utilising long-term correlations and patterns. Gating and memory cells assist LSTMs handle gradient erosion. Short- and long-term temporal correlations train the model. It forecasts supply chain demand using trends, seasonality, marketing, and macroeconomics. LSTM models help companies forecast demand and make wise inventory and buying choices. CNNs can find picture hierarchies and local time series patterns. CNNs may predict demand using time series data to extract demand-affecting elements. Geographic and temporal localisation are CNN strengths. Sales data analysis by product category, location, and time period is their specialisation. They can uncover complicated patterns that conventional forecasting misses, allowing them to construct better demand models that adapt swiftly to client behaviour and market conditions. Deep learning optimises inventory by predicting global supply chain demand. More accurate projections synchronise supply-demand. Reduces inventory, stockouts, and storage expenses. Supply chain managers may manage inventories and plan for difficulties using real-time inventory rules updated by deep learning algorithms. As logistical operations speed up, inventory management solutions with sophisticated deep learning algorithms may boost JIT and supply chain flexibility. Deep learning demand forecasting works best with large, diverse sales, economic, consumer mood, and meteorological datasets. Forecasting models may benefit from all demand components across univariate or multivariate time series inputs. Data may change deep learning. Prediction models fluctuate with market conditions. Deep learning has strengths and downsides for demand forecasting and inventory optimisation. Computers with large worldwide supply chains and big historical data struggle with deep learning model training. High-performance computers or cloud parallel processing are required. Deep learning models are "black boxes" thus supply chain managers doubt estimates. These issues need XAI methods to explain deep learning model choices. Deep learning may influence SCM compatibility and scalability. Legacy systems and deep learning frameworks share data formats and procedures. These issues are addressed by data scientists, SCM experts, and IT professionals via seamless integration. Data pre-processing enhances deep learning model representation. Normalisation, missing value filling, and feature engineering may make or fail deep learning. Case studies with real-world deep learning demand forecasting and inventory optimisation improved accuracy and efficiency. Stockouts and operational costs are reduced by LSTM models predicting worldwide retailer and corporate consumer preferences. CNNs for multi-dimensional time series analysis help companies manage inventories by identifying complex economic and seasonal demand spikes.
Utilizing AI for Real-Time Autonomous Parking Systems: Developing Deep Learning Models for Parking Space Detection, Path Planning, and Parking Maneuver Execution in Smart Cities
The integration of artificial intelligence (AI) into real-time autonomous parking systems represents a significant advancement in smart city infrastructure, addressing the growing need for efficient urban mobility solutions. This paper explores the development and implementation of deep learning models designed to enhance the functionality and performance of autonomous parking systems. The primary focus is on three core components: parking space detection, path planning, and parking maneuver execution. Each component is crucial for achieving seamless and reliable autonomous parking in diverse environments, including on-street, multi-level, and underground parking facilities. Parking space detection involves the use of advanced deep learning techniques to identify and classify available parking spaces in real-time. Convolutional neural networks (CNNs) and other state-of-the-art object detection algorithms are employed to analyze visual data captured by cameras installed in vehicles or surrounding infrastructure. The paper discusses various approaches for enhancing detection accuracy and robustness, including the use of synthetic data augmentation, transfer learning, and multi-sensor fusion. These methods aim to overcome challenges such as varying lighting conditions, occlusions, and diverse parking environments. Path planning is another critical aspect addressed in this research. The paper examines the application of deep reinforcement learning (DRL) and other algorithmic approaches to develop efficient path planning strategies. These strategies are designed to optimize the trajectory of autonomous vehicles as they navigate from the point of entry to the designated parking space. Emphasis is placed on developing algorithms that can adapt to dynamic and complex environments, ensuring that the planned paths are not only optimal in terms of distance and time but also safe and compliant with traffic regulations. The execution of parking maneuvers involves translating the planned paths into precise vehicle control actions. This paper explores the integration of control algorithms with deep learning models to achieve accurate and smooth parking maneuvers. Techniques such as model predictive control (MPC) and end-to-end learning approaches are discussed, highlighting their effectiveness in handling various parking scenarios, including parallel parking, perpendicular parking, and tight parking spots. The challenges associated with real-time control and the need for high-resolution feedback systems are also addressed. In addition to these technical components, the paper considers the broader implications of implementing AI-driven autonomous parking systems in smart cities. It examines the potential benefits, such as reduced traffic congestion, improved parking efficiency, and enhanced safety for both drivers and pedestrians. The research also addresses potential challenges and limitations, including the need for robust sensor calibration, the integration of AI systems with existing infrastructure, and the ethical considerations related to data privacy and security. The findings presented in this paper contribute to the growing body of knowledge in the field of autonomous driving and smart city technologies. By leveraging deep learning models for real-time parking space detection, path planning, and maneuver execution, this research aims to advance the state of the art in autonomous parking systems, providing a foundation for future innovations and deployments in urban environments.
Utilizing AI for Personalized Nutritional Genomics: Developing Machine Learning Models for Diet Optimization Based on Genomic, Metabolic, and Microbiome Data
The burgeoning field of personalized nutritional genomics has emerged as a critical frontier in advancing individualized healthcare, leveraging the convergence of artificial intelligence (AI) and multi-omic data to optimize dietary recommendations. This research paper investigates the application of AI-driven methodologies to personalize nutritional guidance based on a comprehensive integration of genomic, metabolic, and microbiome data. The primary objective of this study is to develop sophisticated machine learning models capable of interpreting complex biological data to enhance dietary strategies tailored to individual genetic predispositions, metabolic responses, and gut microbiome profiles. The integration of AI into nutritional genomics holds promise for revolutionizing the way dietary recommendations are formulated, moving beyond the traditional one-size-fits-all approach. By harnessing the power of AI, particularly advanced machine learning techniques, it is possible to dissect intricate relationships between genetic markers and dietary responses, thus facilitating more precise and effective dietary interventions. This paper delves into the methodologies employed to analyze genomic sequences, metabolic pathways, and microbiome composition, elucidating how these factors collectively influence nutritional requirements and health outcomes. A central component of this research involves the development and validation of machine learning models that can assimilate and analyze heterogeneous data sources. These models are designed to identify patterns and correlations between genetic variants, metabolic profiles, and microbiome characteristics, thereby generating actionable insights for dietary optimization. The study explores various AI techniques, including supervised learning, unsupervised learning, and ensemble methods, to ascertain their efficacy in predicting individual nutritional needs and responses. The implementation of these models is evaluated through rigorous testing and validation processes to ensure their accuracy and reliability. The paper also examines the practical implications of applying AI in personalized nutrition, addressing the potential for improving health outcomes and preventing diet-related diseases. By providing tailored dietary recommendations based on an individual's unique biological makeup, AI-driven tools have the potential to enhance the efficacy of nutritional interventions, mitigate the risk of chronic diseases, and promote overall well-being. The study further explores the ethical considerations and challenges associated with the use of genomic and microbiome data in personalized nutrition, including issues related to data privacy, informed consent, and the potential for algorithmic bias. In addition to the technical aspects, the paper provides a comprehensive review of current advancements in the field, including notable case studies and examples of AI-driven nutritional genomics applications. These case studies illustrate the practical benefits and limitations of utilizing AI for personalized dietary recommendations, offering insights into future research directions and potential improvements in model performance. Ultimately, this research underscores the transformative potential of integrating AI with genomic, metabolic, and microbiome data to advance personalized nutrition. By developing and refining machine learning models that can effectively interpret and apply complex biological data, this study contributes to the evolving landscape of precision nutrition, paving the way for more individualized and effective dietary strategies. The findings presented herein have significant implications for both clinical practice and public health, highlighting the promise of AI in enhancing dietary recommendations and improving health outcomes across diverse populations.
Improving Multi- Agent AI Model Based Distributed Intrusion Detection Systems
Standard intrusion detection systems (IDS) are not sufficient for appropriate network security considering the increasing complexity and frequency of attacks. One significant advance are distributed intrusion detection systems (DIDS), which follow large, dispersed networks with scalability and agility. This paper explores approaches to enhance the multi-agent artificial intelligence (AI) models in DIDS performance. Multi-agent systems (MAS) provide real-time reactivity to complex cyber threats by means of distributed, cooperative, and autonomous detecting components. Important traits define their adaptability in changing threat situations, efficient use of resources, and ability to function in diverse network environments. This paper investigates MAS-driven DIDS design, implementation, and efficacy to underline their part in spotting and lowering known as well as novel attack routes. By means of machine learning, reinforcement learning, and cooperative algorithms, MAS increases DIDS's speed, accuracy, and robustness, hence increasing its capabilities. This work also addresses future directions in the usage of MAS-enhanced DIDS, implementation challenges, and pragmatic case studies.
Development of AI-Driven Digital Pathology Platforms for Cancer Diagnosis: Utilizing Convolutional Neural Networks for Automated Image Analysis, Tumor Detection, and Prognostic Assessment
The advent of artificial intelligence (AI) has revolutionized numerous fields, with digital pathology emerging as a significant domain benefiting from these advancements. This research paper delves into the development and application of AI-driven digital pathology platforms, focusing particularly on the utilization of convolutional neural networks (CNNs) for enhancing cancer diagnosis. Digital pathology, which involves the acquisition, management, and interpretation of pathology information in a digital format, is poised to transform traditional practices through automation and advanced analytical capabilities. Central to this transformation is the deployment of CNNs, a class of deep learning algorithms known for their efficacy in image processing tasks. These networks, designed to automatically and adaptively learn spatial hierarchies of features from images, are particularly well-suited for analyzing complex histopathological images. The integration of CNNs into digital pathology platforms aims to streamline the diagnostic workflow by automating the image analysis process, thereby reducing the manual workload of pathologists and improving diagnostic accuracy. The primary focus of this research is on the development of AI models capable of performing automated image analysis for cancer detection. The paper provides a comprehensive examination of CNN architectures tailored for pathology applications, including their training methodologies, optimization techniques, and performance evaluation metrics. By leveraging large datasets of annotated histopathological images, these models are trained to identify and classify cancerous cells with a high degree of precision. The research further explores how these models contribute to tumor detection, offering insights into the identification of various cancer types and grades based on image patterns. In addition to tumor detection, the study investigates the potential of AI-driven platforms for prognostic assessment. Prognostic models, powered by CNNs, are designed to predict patient outcomes based on the analysis of histopathological images in conjunction with clinical data. This capability is crucial for stratifying patients into different risk categories and guiding personalized treatment strategies. The integration of CNN-based prognostic assessment into digital pathology platforms represents a significant advancement in cancer care, providing clinicians with actionable insights that enhance decision-making processes. The research also addresses the challenges associated with developing and implementing AI-driven digital pathology platforms. These challenges include the need for high-quality, annotated image datasets, the computational demands of training complex models, and the integration of AI systems into existing pathology workflows. The paper discusses strategies to overcome these challenges, such as the use of transfer learning, data augmentation techniques, and collaborative efforts between AI researchers and clinical practitioners. By enhancing diagnostic accuracy, reducing pathologist workload, and providing rapid, reliable cancer diagnoses, AI-driven digital pathology platforms hold the potential to revolutionize cancer care. This research highlights the transformative impact of convolutional neural networks on digital pathology, offering a detailed analysis of their application in automated image analysis, tumor detection, and prognostic assessment. The findings underscore the promise of AI technologies in improving cancer diagnosis and treatment, paving the way for future advancements in the field.
Privacy-Preserving Federated Learning for Reward-Redemption Optimization
The objective of this paper is to describe a federated learning architecture to optimise reward-redemption behaviour in mobile wallet ecosystems at the same time protecting user privacy and complying with national data protection laws. Edge learners within wallet clients utilise wallet balance, transaction history, and other data to predict whether a user would spend a reward.
Multi-Agent Negotiation for Adaptive Capacity Scaling in Post-Merger Payment Networks
After the merger of digital payment platforms, transaction throughput and integration complexity can increase exponentially, which might be too much for old infrastructure to handle. This aim of this study is to suggests a multi-agent negotiation paradigm for flexible capacity scaling across diverse authorisation clusters. Agents engage in decentralised reasoning over latency service-level goals (SLOs) and compute-cost budgets, dynamically compute reallocation, memory, and message queue quotas.
Graph-Neural Threat Detection at the Hypervisor Layer
The objective of this paper is to present a threat detection approach at the hypervisor layer method which uses Graph Neural Networks (GNNs) to examine inter-VM communication patterns. An abnormal behaviour of lateral movement represented in temporal communication flows as dynamic graphs is detected using a lightweight GNN while traditional intrusion detection systems (IDS) miss these behaviours, while this solution supports Microsoft Hyper-V and VMware ESXi which allows to see real-time zero-day exploit footprints without affecting performance.
Environment-Aware Failure Propagation Simulator Using Reinforcement Agents
The objective of this paper is to present an environment-aware failure propagation simulator, reinforcement learning agents which is used to simulate chaotic development and QA clusters. This system explores pipeline misconfigurations that randomly pass promotion gates and agents may choose high-risk setups by adjusting failure distributions and learning from errors.
AI-Powered Impact Analysis for Multi-Site Schema Evolution
Schema evolution in distributed, multi-site database ecosystems is complex and error-prone which causes unanticipated propagation failures across the staging and production tiers. The objective of this paper is to present an AI-driven impact analysis technique that uses knowledge graph-based reasoning which combines DDL diffs and reproduce their transitive effects across related contexts.
Developing AI-Driven Consumer Insights for Omni-Channel Retail Strategies
In the era of digital transformation, omni-channel retailing has emerged as a critical strategy for businesses aiming to deliver a cohesive and engaging customer experience across diverse touchpoints. The integration of artificial intelligence (AI) into this paradigm is redefining how retailers understand and interact with their customers. This research paper delves into the development of AI-driven consumer insights to enhance omni-channel retail strategies, focusing on the application of machine learning and advanced analytics for customer journey mapping, behavior prediction, and experience personalization. The study explores how machine learning algorithms and advanced analytical techniques can be leveraged to generate deep customer insights that inform strategic decisions. Customer journey mapping, a fundamental component of omni-channel retail, involves tracking and analyzing the customer’s interactions across various channels—such as online platforms, mobile applications, and physical stores. The paper investigates how AI technologies facilitate a granular understanding of these interactions, enabling retailers to construct comprehensive journey maps that accurately reflect customer experiences and preferences. Behavior prediction is another critical area where AI and machine learning provide substantial value. By analyzing historical data and employing predictive modeling techniques, retailers can anticipate future customer behaviors with high accuracy. This predictive capability allows for proactive engagement, enabling personalized marketing strategies and targeted promotions that resonate with individual customer needs and preferences. The paper examines various machine learning models and algorithms used in behavior prediction, assessing their effectiveness and practical implementation within the retail context. Experience personalization, an essential goal of omni-channel strategies, is significantly enhanced through AI-driven insights. Personalization extends beyond mere product recommendations to encompass a tailored shopping experience that adjusts in real-time based on customer interactions and preferences. The research highlights how advanced analytics and AI can create personalized shopping experiences, thereby increasing customer satisfaction and loyalty. The study also addresses the technological and operational challenges involved in implementing such personalization strategies, including data integration across channels and the need for real-time analytics. The impact of AI-driven consumer insights on key retail performance metrics, such as customer loyalty, sales conversion rates, and brand engagement, is thoroughly analyzed. The paper presents empirical evidence and case studies demonstrating the effectiveness of AI-powered strategies in achieving superior outcomes across these dimensions. It also explores the broader implications of these technologies for the retail industry, including the potential for transforming traditional business models and the future outlook of AI in retail. Integration of machine learning and advanced analytics into omni-channel retail strategies represents a significant advancement in understanding and engaging with consumers. By leveraging AI-driven insights, retailers can achieve a higher level of personalization and strategic alignment, leading to enhanced customer experiences and improved business performance. The study provides a comprehensive examination of these developments, offering valuable insights for researchers, practitioners, and industry stakeholders aiming to navigate the evolving landscape of omni-channel retail.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
Developing AI-Based Real-Time Genomic Data Analysis Pipelines for Precision Medicine: Leveraging Machine Learning for Variant Calling, Functional Annotation, and Clinical Reporting
The burgeoning field of precision medicine necessitates the development of sophisticated genomic data analysis pipelines capable of accommodating the vast scale and complexity of modern genomic datasets. This paper investigates the creation and optimization of AI-based real-time genomic data analysis pipelines, focusing on leveraging machine learning to enhance the efficiency and accuracy of variant calling, functional annotation, and clinical reporting. Precision medicine demands rapid and precise genomic analyses to guide personalized treatment strategies, and traditional methods often fall short in meeting these needs due to their limitations in speed, accuracy, and scalability. In addressing these challenges, this research presents an in-depth exploration of AI-driven workflows designed to automate and optimize critical aspects of genomic analysis. Variant calling, a cornerstone of genomic interpretation, benefits significantly from machine learning algorithms that can discern subtle genetic variants with high precision. This paper delves into the application of advanced machine learning techniques, including deep learning models and ensemble methods, to improve the accuracy of variant detection and reduce false-positive and false-negative rates. Furthermore, the integration of AI into functional annotation processes is examined, where machine learning models are employed to predict the biological impact of genetic variants and to elucidate their roles in disease mechanisms. This includes the use of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to interpret complex genomic features and their functional consequences. Clinical reporting, another critical component of genomic analysis, is addressed through the development of AI-powered systems capable of generating comprehensive and interpretable reports. These systems are designed to integrate variant calls and functional annotations into clinically actionable insights, thereby facilitating more informed decision-making in precision medicine. The paper discusses the implementation of natural language processing (NLP) techniques and automated report generation frameworks that enhance the clarity and usability of clinical reports for healthcare professionals. The research highlights several case studies demonstrating the practical application of these AI-based pipelines in real-world scenarios, showcasing improvements in both the speed and accuracy of genomic analyses. Additionally, the paper explores the challenges associated with the deployment of AI-driven genomic workflows, including data integration, computational efficiency, and the interpretability of machine learning models. It also considers the implications of these advancements for the broader field of genomics-based medicine, emphasizing the potential for AI to transform the landscape of personalized healthcare.
Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-nd/4.0
AI-Enhanced Real-Time Monitoring and Control Systems for Manufacturing Operations: Implementing Machine Learning for Dynamic Process Adjustment, Anomaly Detection, and Performance Optimization
The rapid evolution of artificial intelligence (AI) and machine learning (ML) technologies has heralded transformative changes across various sectors, with manufacturing operations standing at the forefront of this revolution. This paper explores the integration of AI-enhanced real-time monitoring and control systems in manufacturing, emphasizing the role of machine learning algorithms in dynamic process adjustment, anomaly detection, and performance optimization. The study aims to demonstrate how these AI-driven systems can significantly enhance operational efficiency, product quality, and process stability in manufacturing environments. At the core of this investigation is the implementation of advanced ML techniques to enable real-time monitoring of manufacturing processes. Traditional monitoring systems, often reliant on static thresholds and manual interventions, are progressively being replaced by dynamic, AI-enhanced solutions that offer continuous, adaptive oversight. This transition is facilitated by ML algorithms capable of analyzing vast amounts of data in real-time, enabling more precise and timely adjustments to manufacturing processes. These algorithms leverage historical and real-time data to build predictive models that can forecast potential deviations and recommend corrective actions before they escalate into significant issues. A key aspect of the research is the application of ML for anomaly detection. Traditional methods for identifying anomalies in manufacturing processes typically involve predefined rules and thresholds, which often fail to account for complex, non-linear interactions within the system. In contrast, ML-based anomaly detection techniques utilize advanced statistical models and pattern recognition algorithms to identify deviations from normal operating conditions with higher accuracy. These systems can detect subtle anomalies that might otherwise go unnoticed, thereby reducing the risk of defects and downtime, and ensuring higher product quality. Performance optimization through AI is another focal point of this paper. By employing ML algorithms, manufacturing operations can achieve unprecedented levels of efficiency and productivity. AI-driven systems analyze performance data to identify inefficiencies and areas for improvement. This analysis enables dynamic adjustments to process parameters, such as temperature, pressure, and speed, optimizing the manufacturing process in real-time. The result is a more agile and responsive production system capable of adapting to varying demands and conditions, thus improving overall operational stability. The paper also addresses the integration challenges and practical considerations associated with implementing AI-enhanced monitoring and control systems. While the benefits of AI are substantial, their deployment in manufacturing environments requires overcoming several obstacles, including data integration, system compatibility, and the need for robust infrastructure. The research provides insights into these challenges and offers recommendations for successful implementation, including strategies for data management, model training, and system integration. Integration of AI and machine learning into real-time monitoring and control systems represents a significant advancement in manufacturing technology. The ability to dynamically adjust processes, detect anomalies with greater precision, and optimize performance in real-time promises substantial improvements in operational efficiency and product quality. This paper contributes to the understanding of how AI-driven systems can be effectively utilized in manufacturing operations, offering a comprehensive analysis of their potential to transform traditional manufacturing practices.
AI-Enhanced Fault Detection and Diagnosis in Manufacturing Systems: Developing Machine Learning Models for Early Detection of Equipment Failures and Root Cause Analysis
The advent of artificial intelligence (AI) has significantly transformed numerous sectors, with manufacturing being one of the most promising fields for its application. This study delves into the enhancement of fault detection and diagnosis within manufacturing systems through the utilization of AI and machine learning models. The primary objective is to develop advanced machine learning algorithms capable of early detection of equipment failures and accurate root cause analysis. In manufacturing environments, where the efficiency and reliability of equipment are paramount, timely fault detection and diagnosis play a crucial role in maintaining operational continuity and minimizing downtime. Machine learning models, particularly those based on supervised learning, unsupervised learning, and deep learning techniques, offer powerful tools for analyzing complex data generated by manufacturing systems. These models are trained on historical data that includes various failure modes, operational conditions, and maintenance records. The ability to identify patterns and anomalies in this data enables these models to predict potential failures before they occur. By leveraging algorithms such as neural networks, support vector machines, and ensemble methods, this study explores how these techniques can be effectively applied to enhance fault detection mechanisms. The research investigates several key areas. Firstly, it examines the integration of AI-driven predictive maintenance strategies with existing manufacturing processes. This involves the collection and preprocessing of data from sensors and other monitoring systems, the application of machine learning algorithms to identify signs of impending failures, and the implementation of these predictions into maintenance schedules. Secondly, the study focuses on the development of diagnostic models that not only detect faults but also determine their underlying causes. This aspect is critical for effective maintenance and repair, as understanding the root cause of a failure allows for more targeted interventions, thereby improving overall system reliability. Furthermore, the research evaluates the performance of various machine learning models in real-world manufacturing scenarios. This includes assessing their accuracy, robustness, and ability to generalize across different types of equipment and failure modes. The study also addresses challenges associated with the deployment of these models, such as data quality issues, computational complexity, and integration with existing manufacturing systems. In addition to technical considerations, the study highlights the practical implications of implementing AI-enhanced fault detection and diagnosis in manufacturing systems. It discusses the potential benefits, including reduced downtime, extended equipment lifespan, and improved maintenance efficiency. The research also explores the economic impact of these advancements, considering both the initial investment in AI technologies and the long-term savings resulting from improved system reliability. The outcomes of this study contribute to the growing body of knowledge in AI applications for manufacturing and provide actionable insights for practitioners and researchers alike. By demonstrating the effectiveness of machine learning models in predicting equipment failures and diagnosing their causes, this research offers a comprehensive framework for leveraging AI to enhance fault detection and diagnosis in manufacturing environments.
AI-Enhanced Drug Safety Monitoring Systems: Developing Machine Learning Models for Real-Time Risk Assessment, Signal Detection, and Pharmacovigilance in Pharmaceutical Industry
In the context of the pharmaceutical industry, ensuring drug safety is paramount to safeguarding public health and maintaining regulatory compliance. Traditional methods of drug safety monitoring, which primarily rely on passive reporting and periodic reviews, have proven insufficient in addressing the complexities and rapid pace of modern pharmacovigilance. To address these challenges, this paper explores the integration of artificial intelligence (AI) into drug safety monitoring systems, focusing on the development of machine learning models for real-time risk assessment, signal detection, and overall pharmacovigilance. The core objective of this research is to advance drug safety by leveraging AI-driven systems capable of dynamically assessing risks associated with pharmaceutical products, identifying safety signals at an early stage, and ensuring adherence to regulatory standards. The study investigates various machine learning techniques and their applicability to pharmacovigilance, including supervised learning, unsupervised learning, and ensemble methods. It emphasizes the importance of integrating these techniques with existing data sources such as electronic health records (EHRs), patient registries, and adverse event reporting databases to enhance the robustness and accuracy of safety assessments. One significant aspect covered is the development and deployment of real-time risk assessment models. These models utilize predictive analytics to forecast potential safety issues before they manifest, thereby enabling proactive risk management strategies. The paper further explores advanced signal detection methodologies, which utilize anomaly detection and natural language processing (NLP) to uncover hidden patterns and emerging safety signals from vast datasets. This proactive approach is essential in identifying adverse drug reactions (ADRs) and other safety concerns that might not be immediately apparent through conventional monitoring methods. Furthermore, the research delves into the regulatory implications of implementing AI-enhanced drug safety systems. It examines how these systems can facilitate compliance with stringent regulatory requirements by providing more accurate and timely safety data, thereby improving the overall efficiency of the pharmacovigilance process. The study also addresses the challenges associated with the integration of AI technologies, including data privacy concerns, the need for transparent algorithms, and the necessity for continuous validation and updating of machine learning models to adapt to evolving safety profiles. Case studies are presented to illustrate the practical applications of AI in drug safety monitoring, highlighting successful implementations and the impact on risk assessment and signal detection. These examples demonstrate the potential of AI to transform pharmacovigilance by providing more precise and actionable insights, ultimately leading to enhanced patient safety and more informed decision-making processes.
AI-Enhanced Computational Approaches for Optimizing Clinical Trial Design: Leveraging Machine Learning for Patient Recruitment, Trial Monitoring, and Outcome Prediction
The application of artificial intelligence (AI) in clinical trial design represents a significant advancement in addressing the complexities and inefficiencies traditionally associated with the clinical trial process. This study focuses on AI-enhanced computational approaches that leverage machine learning (ML) to optimize key components of clinical trials, including patient recruitment, trial monitoring, and outcome prediction. Clinical trials are often burdened by the challenges of identifying eligible participants, ensuring timely monitoring of trial progress, and accurately predicting trial outcomes, all of which can lead to delays, increased costs, and trial failures. The integration of AI and ML techniques into these processes promises to mitigate these issues by improving efficiency, reducing timelines, and enhancing the probability of trial success. In the realm of patient recruitment, AI-enhanced methods use vast amounts of structured and unstructured data from electronic health records (EHRs), medical histories, and demographic databases to identify eligible patients more efficiently than traditional methods. Machine learning algorithms can process these datasets to detect patterns that indicate eligibility, taking into account the intricacies of inclusion and exclusion criteria specific to each trial. Moreover, predictive analytics models can forecast recruitment feasibility based on population health trends, geographic distributions, and historical recruitment performance. These approaches not only expedite the recruitment process but also ensure a more diverse and representative participant pool, which is critical for the generalizability and reliability of trial results. By automating the patient-matching process, AI significantly reduces human error and bias, which have historically impeded patient selection, and consequently, trial timelines. Trial monitoring, another critical component of clinical trials, can also benefit from AI-enhanced computational techniques. In traditional clinical trials, monitoring is labor-intensive and often prone to delays, particularly in detecting adverse events or protocol deviations. AI-powered monitoring systems, on the other hand, provide real-time analytics by continuously analyzing data from multiple sources, including patient-reported outcomes, sensor data, and clinical measurements. This dynamic monitoring allows for the early detection of irregularities or risks that may compromise the trial's integrity. Machine learning models can predict potential adverse events by analyzing historical and real-time data, enabling preemptive adjustments to trial protocols. Furthermore, AI-driven automation in trial monitoring reduces the need for manual oversight, lowering costs and improving the precision and timeliness of data collection and interpretation. Outcome prediction is another area where AI shows immense potential in transforming clinical trial design. Machine learning algorithms, trained on large datasets from past clinical trials, can predict the likely outcomes of ongoing or future trials with a high degree of accuracy. These models use historical data, such as biomarker levels, genetic information, and patient responses to previous treatments, to identify factors that are most likely to influence trial success. This enables researchers to make informed decisions early in the trial process, including refining trial protocols, adjusting dosage regimens, or even halting trials that are unlikely to produce favorable outcomes. By forecasting trial outcomes, AI significantly reduces the risk of trial failure and the associated costs. Additionally, predictive models can inform adaptive trial designs, where modifications to the trial are made in real-time based on interim data, further optimizing resource allocation and improving trial success rates. The implementation of AI in clinical trial design also presents challenges that must be addressed. Data quality and integration are central concerns, as the success of machine learning algorithms depends on the availability of high-quality, well-structured data. Clinical trial data are often fragmented across different platforms, making it difficult to achieve seamless integration. Moreover, the ethical and regulatory considerations surrounding the use of AI in clinical research, such as data privacy, algorithmic transparency, and compliance with guidelines from regulatory bodies, remain critical hurdles. Ensuring that AI algorithms used in patient recruitment and monitoring are free from bias and meet ethical standards is essential for maintaining the integrity and fairness of clinical trials. Another challenge is the computational complexity involved in deploying advanced machine learning models, which requires sophisticated infrastructure and expertise, often necessitating collaboration between clinicians, data scientists, and engineers. This study explores the transformative potential of AI in enhancing the design and execution of clinical trials, with a focus on machine learning applications in patient recruitment, trial monitoring, and outcome prediction. By analyzing case studies and current AI-driven models, the paper aims to provide a comprehensive understanding of how AI can reduce clinical trial timelines, lower costs, and improve the success rates of clinical research. In addition, the paper will discuss the practical challenges associated with the adoption of AI technologies in clinical trials, including data integration, algorithmic bias, and ethical considerations. The ultimate goal is to demonstrate how AI-enhanced computational approaches can revolutionize the clinical trial landscape, offering a more efficient, cost-effective, and successful model for conducting clinical research in the modern era.
Generative AI for Cyber-Physical System Automated Penetration Testing
Modern infrastructure links cyber-physical systems to the physical and digital worlds. Rising complexity and interconnection risk cybersecurity. CPS security may improve with generative AI penetration testing. This work investigates whether deep learning and reinforcement learning-based generative AI models can automatically find CPS vulnerabilities. The paper discusses penetration testing using AI and its pros and cons. Generative models may uncover and resolve security flaws using AI-simulated attack scenarios. The study explores ethical issues, constraints, and potential CPS security trends using generative AI, promoting more research. Generative AI has great potential for CPS penetration testing, but implementation, accuracy, and abuse must be addressed.
Federated AI Models for Distributed Network Botnet Detection
Distributed network security is threatened by botnets' complexity and massive attacks. Botnet detection centralized solutions may have privacy and scalability difficulties. Federated AI methods detect large botnets in this research. Federated learning trains machine learning models across network nodes so local data is protected. It discusses federated learning, distributed systems, and botnet detection. Data heterogeneity, communication cost, and model convergence plague federated AI botnet detection. Federated AI identifies botnets in real life. We conclude with future research and how federated AI may improve scattered network cybersecurity.
Multi-Cloud Blockchain Application Trust with AI-Powered Security Analytics
Multi-cloud and blockchain integration presents security and trust challenges. AI-automated security analytics is innovative. AI-powered security solutions' intelligent monitoring, anomaly detection, and automated attack response make cloud blockchain applications more reliable. AI security for multi-cloud blockchains is examined in the article. We study real-time monitoring, predictive threat detection, and vulnerability management in multi-cloud systems utilizing blockchain platforms and AI models including machine learning and deep learning. AI-based blockchain security analytics case studies. Scalability, data security, and cyberattacks are issues and trends.
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Enhancing Critical DevOps Infrastructure Redundancy and Fault Tolerance with AI
AI in DevOps improved critical infrastructure management redundancy and fault tolerance. DevOps combines software development with IT operations to ensure service infrastructure dependability, scalability, and efficiency. Redundancy and fault tolerance solutions may lack flexibility and real-time response as remote systems become more complicated
DevSecOps Pipelines with AI for Real-Time Vulnerability Remediation
Security is automated and continuous in DevOps. These pipelines emphasize real-time vulnerability mitigation during security threats. AI automates vulnerability finding, evaluation, and remediation, boosting DevSecOps. Security teams can respond fast and accurately using ML and DL algorithms to analyze large data, discover patterns, and identify dangers. This paper evaluates AI's merits, drawbacks, and future in real-time DevSecOps pipeline vulnerability mitigation. We research AI-driven vulnerability assessment, anomaly detection, and decision-making systems that reduce cleaning and human intervention. Case studies and implementations demonstrate AI DevSecOps security. AI can enhance DevSecOps and minimize new dangers, according to this research.
Cyber Defense Strategy Orchestration Driven by Artificial Intelligence in Federated 5G Networks
Rapid 5G network adoption and development have improved mobile communications capability, hence inspiring innovation and network security concerns. Federalized 5G networks are challenging and dangerous as they rely on infrastructure from many parties. Federated 5G networks need artificial intelligence for cyber security if we are to solve these issues. Attacks may be found via machine learning, which also forecasts vulnerabilities and automates real-time fixes. Federated 5G network cyber security uses artificial intelligence for threat detection, network operations, and real-time decision-making. The benefits, drawbacks, and future directions of research in the federated 5G ecosystem of AI-powered cybersecurity are covered in this paper.
AI-Based Smart Contract Verification: Securing and Accurate Blockchain Ecosystems
Blockchain technology has allowed innovative decentralized networks but highlighted security worries. Blockchain ecosystem automation uses smart contracts, self-executing contracts with programmed terms. Smart contract weaknesses may cause costly security breaches and exploitation. For secure and accurate smart contract verification, AI is powerful. The paper compares static analysis, machine learning, and reinforcement learning smart contract verification AI methods. The research discusses their implementation, pros and cons, and real-world applications where AI has found and eliminated dangers. Further research on AI-driven verification tools and blockchain system robustness is examined.