Edinburg Journal of Natural Language Processing and AI
type: channel
description: 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
publisher: EJNLPAI Publishers, UK
language: en
publication name: Edinburg Journal of Natural Language Processing and AI
copyright: License Terms coming soon.
items: Incremental Schema Fingerprinting with MinHash for Drift Detection in Data Lakes
items: Using Machine Learning for Real-Time Analysis of High-Throughput Biological Data
items: Dynamic Pricing Strategies in E-Commerce: Leveraging Machine Learning for Real-Time Decision-Making
items: Deep Learning Algorithms for Demand Forecasting and Inventory Optimization in Global Supply Chains
items: 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
items: Utilizing AI for Personalized Nutritional Genomics: Developing Machine Learning Models for Diet Optimization Based on Genomic, Metabolic, and Microbiome Data
items: Improving Multi- Agent AI Model Based Distributed Intrusion Detection Systems
items: Development of AI-Driven Digital Pathology Platforms for Cancer Diagnosis: Utilizing Convolutional Neural Networks for Automated Image Analysis, Tumor Detection, and Prognostic Assessment
items: Privacy-Preserving Federated Learning for Reward-Redemption Optimization
items: Multi-Agent Negotiation for Adaptive Capacity Scaling in Post-Merger Payment Networks
items: Graph-Neural Threat Detection at the Hypervisor Layer
items: Environment-Aware Failure Propagation Simulator Using Reinforcement Agents
items: AI-Powered Impact Analysis for Multi-Site Schema Evolution
items: Developing AI-Driven Consumer Insights for Omni-Channel Retail Strategies
items: Developing AI-Based Real-Time Genomic Data Analysis Pipelines for Precision Medicine: Leveraging Machine Learning for Variant Calling, Functional Annotation, and Clinical Reporting
items: AI-Enhanced Real-Time Monitoring and Control Systems for Manufacturing Operations: Implementing Machine Learning for Dynamic Process Adjustment, Anomaly Detection, and Performance Optimization
items: AI-Enhanced Fault Detection and Diagnosis in Manufacturing Systems: Developing Machine Learning Models for Early Detection of Equipment Failures and Root Cause Analysis
items: AI-Enhanced Drug Safety Monitoring Systems: Developing Machine Learning Models for Real-Time Risk Assessment, Signal Detection, and Pharmacovigilance in Pharmaceutical Industry
items: AI-Enhanced Computational Approaches for Optimizing Clinical Trial Design: Leveraging Machine Learning for Patient Recruitment, Trial Monitoring, and Outcome Prediction
items: Generative AI for Cyber-Physical System Automated Penetration Testing
items: Federated AI Models for Distributed Network Botnet Detection
items: Multi-Cloud Blockchain Application Trust with AI-Powered Security Analytics
items: Enhancing Critical DevOps Infrastructure Redundancy and Fault Tolerance with AI
items: DevSecOps Pipelines with AI for Real-Time Vulnerability Remediation
items: Cyber Defense Strategy Orchestration Driven by Artificial Intelligence in Federated 5G Networks
items: AI-Based Smart Contract Verification: Securing and Accurate Blockchain Ecosystems

Incremental Schema Fingerprinting with MinHash for Drift Detection in Data Lakes
type: item
description: 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.
subject: Articles
  topic: section
subject: MinHash
  topic: keywords
subject: schema drift
  topic: keywords
subject: data lakes
  topic: keywords
subject: fingerprinting
  topic: keywords
subject: semantic tagging
  topic: keywords
subject: compliance monitoring
  topic: keywords
subject: metadata catalogs
  topic: keywords
subject: drift detection
  topic: keywords
creator: Mohan Vamsi Musunuru
creator: Sai Charan Ponnoju
creator: Chandan Gnana Murthy
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2023-09-06
publication date: 2023-09-06
starting page: 14
ending page: 47
doi: 10.55222/cetft276

Using Machine Learning for Real-Time Analysis of High-Throughput Biological Data
type: item
description: 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 fo…
subject: Articles
  topic: section
subject: machine learning
  topic: keywords
subject: RNA-Seq
  topic: keywords
subject: mass spectrometry
  topic: keywords
subject: bioinformatics
  topic: keywords
subject: real-time analysis
  topic: keywords
subject: proteomics
  topic: keywords
subject: transcriptomics
  topic: keywords
creator: Sreeharsha Burugu
rights: Copyright (c) 2020 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2020-06-25
publication date: 2020-06-25
starting page: 47
ending page: 87

Dynamic Pricing Strategies in E-Commerce: Leveraging Machine Learning for Real-Time Decision-Making
type: item
description: 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 c…
subject: Articles
  topic: section
subject: dynamic pricing
  topic: keywords
subject: machine learning
  topic: keywords
subject: e-commerce
  topic: keywords
subject: revenue optimization
  topic: keywords
subject: real-time decision-making
  topic: keywords
subject: data analysis
  topic: keywords
creator: Sreeharsha Burugu
rights: Copyright (c) 2019 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2019-04-24
publication date: 2019-04-24
starting page: 283
ending page: 320

Deep Learning Algorithms for Demand Forecasting and Inventory Optimization in Global Supply Chains
type: item
description: 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 sophistic…
subject: Articles
  topic: section
subject: deep learning
  topic: keywords
subject: demand forecasting
  topic: keywords
subject: inventory optimization
  topic: keywords
subject: LSTM
  topic: keywords
subject: CNN
  topic: keywords
subject: supply chain management
  topic: keywords
subject: computational complexity
  topic: keywords
subject: predictive analytics
  topic: keywords
creator: Midhun Punukollu
rights: Copyright (c) 2019 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2019-05-08
publication date: 2019-05-08
starting page: 246
ending page: 282

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
type: item
description: 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…
subject: Articles
  topic: section
subject: autonomous parking systems
  topic: keywords
subject: deep learning
  topic: keywords
subject: parking space detection
  topic: keywords
subject: path planning
  topic: keywords
subject: parking maneuver execution
  topic: keywords
subject: smart cities
  topic: keywords
creator: Sricharan Kodali
rights: Copyright (c) 2020 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2020-07-07
publication date: 2020-07-07
starting page: 1
ending page: 46

Utilizing AI for Personalized Nutritional Genomics: Developing Machine Learning Models for Diet Optimization Based on Genomic, Metabolic, and Microbiome Data
type: item
description: 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 r…
subject: Articles
  topic: section
subject: personalized nutrition
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: machine learning
  topic: keywords
subject: genomic data
  topic: keywords
subject: metabolic profiles
  topic: keywords
subject: microbiome analysis
  topic: keywords
creator: Sricharan Kodali
rights: Copyright (c) 2019 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2019-03-20
publication date: 2019-03-20
starting page: 200
ending page: 240

Improving Multi- Agent AI Model Based Distributed Intrusion Detection Systems
type: item
description: 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.
subject: Articles
  topic: section
subject: Distributed Intrusion Detection Systems
  topic: keywords
subject: Multi-Agent Systems
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: Sowmya Gudekota
rights: Copyright (c) 2019 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2019-08-12
publication date: 2019-08-12
starting page: 241
ending page: 245

Development of AI-Driven Digital Pathology Platforms for Cancer Diagnosis: Utilizing Convolutional Neural Networks for Automated Image Analysis, Tumor Detection, and Prognostic Assessment
type: item
description: 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. Prognost…
subject: Articles
  topic: section
subject: artificial intelligence
  topic: keywords
subject: digital pathology
  topic: keywords
subject: convolutional neural networks
  topic: keywords
subject: automated image analysis
  topic: keywords
subject: tumor detection
  topic: keywords
creator: Pavan Punukollu
rights: Copyright (c) 2019 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2019-10-01
publication date: 2019-10-01
starting page: 164
ending page: 199

Privacy-Preserving Federated Learning for Reward-Redemption Optimization
type: item
description: 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.
subject: Articles
  topic: section
subject: federated learning
  topic: keywords
subject: privacy-preserving AI
  topic: keywords
subject: reward redemption
  topic: keywords
subject: encrypted gradients
  topic: keywords
subject: differential privacy
  topic: keywords
creator: Vijay Kumar Soni
creator: Shemeer Sulaiman Kunju
creator: Sai Charan Ponnoju
rights: Copyright (c) 2019 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2019-11-18
publication date: 2019-11-18
starting page: 37
ending page: 67

Multi-Agent Negotiation for Adaptive Capacity Scaling in Post-Merger Payment Networks
type: item
description: 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.
subject: Articles
  topic: section
subject: multi-agent systems
  topic: keywords
subject: negotiation protocols
  topic: keywords
subject: capacity scaling
  topic: keywords
subject: post-merger integration
  topic: keywords
subject: transaction processing
  topic: keywords
creator: Radhakrishnan Pachyappan
creator: Aman Sardana
creator: Amsa Selvaraj
rights: Copyright (c) 2019 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2019-02-03
publication date: 2019-02-03
starting page: 68
ending page: 99

Graph-Neural Threat Detection at the Hypervisor Layer
type: item
description: 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.
subject: Articles
  topic: section
subject: hypervisor
  topic: keywords
subject: graph neural networks
  topic: keywords
subject: virtual machines
  topic: keywords
subject: intrusion detection
  topic: keywords
subject: temporal graphs
  topic: keywords
subject: lateral movement
  topic: keywords
subject: zero-day detection
  topic: keywords
subject: ESXi
  topic: keywords
creator: Tejas Dhanorkar
creator: Tanuj Mathur
creator: Aarthi Anbalagan
rights: Copyright (c) 2019 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2019-01-22
publication date: 2019-01-22
starting page: 100
ending page: 131

Environment-Aware Failure Propagation Simulator Using Reinforcement Agents
type: item
description: 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.
subject: Articles
  topic: section
subject: failure propagation
  topic: keywords
subject: reinforcement learning
  topic: keywords
subject: chaos engineering
  topic: keywords
subject: misconfiguration detection
  topic: keywords
subject: root cause analysis
  topic: keywords
creator: Karthik Mani
creator: Nithin Vunnam
creator: Lavanya Shanmugam
rights: Copyright (c) 2019 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2019-09-16
publication date: 2019-09-16
starting page: 132
ending page: 163

AI-Powered Impact Analysis for Multi-Site Schema Evolution
type: item
description: 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.
subject: Articles
  topic: section
subject: schema evolution
  topic: keywords
subject: AI impact analysis
  topic: keywords
subject: DDL diff simulation
  topic: keywords
subject: multi-site databases
  topic: keywords
subject: dependency propagation
  topic: keywords
creator: Vasudevan Ananthakrishnan
creator: Manish Tomar
creator: Priya Ranjan Parida
rights: Copyright (c) 2019 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2019-10-13
publication date: 2019-10-13
starting page: 1
ending page: 36

Developing AI-Driven Consumer Insights for Omni-Channel Retail Strategies
type: item
description: 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 …
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: category
subject: artificial intelligence
  topic: keywords
subject: machine learning
  topic: keywords
subject: advanced analytics
  topic: keywords
subject: omni-channel retail
  topic: keywords
subject: experience personalization
  topic: keywords
creator: Maruthi Rohit Ayyagari
creator: Pavan Punukollu
creator: Sreeharsha Burugu
creator: Raghuveer Prasad Yerneni
creator: Sowmya Gudekota
creator: Midhun Punukollu
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-05-15
publication date: 2025-05-15
starting page: 55
ending page: 97
doi: 10.55222/EJNLPAI.14

Developing AI-Based Real-Time Genomic Data Analysis Pipelines for Precision Medicine: Leveraging Machine Learning for Variant Calling, Functional Annotation, and Clinical Reporting
type: item
description: 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 facilita…
subject: Articles
  topic: section
subject: AI-based pipelines
  topic: keywords
subject: real-time genomic data analysis
  topic: keywords
subject: precision medicine
  topic: keywords
subject: machine learning
  topic: keywords
creator: Sowmya Gudekota
creator: Midhun Punukollu
creator: Raghuveer Prasad Yerneni
creator: Pavan Punukollu
creator: Sreeharsha Burugu
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2022-02-01
publication date: 2022-02-01
starting page: 128
ending page: 169

AI-Enhanced Real-Time Monitoring and Control Systems for Manufacturing Operations: Implementing Machine Learning for Dynamic Process Adjustment, Anomaly Detection, and Performance Optimization
type: item
description: 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 highe…
subject: Review Papers
  topic: section
subject: artificial intelligence
  topic: keywords
subject: machine learning
  topic: keywords
subject: real-time monitoring
  topic: keywords
subject: dynamic process adjustment
  topic: keywords
subject: anomaly detection
  topic: keywords
subject: performance optimization
  topic: keywords
creator: Nischay Reddy Mitta
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-04-21
publication date: 2025-04-21
starting page: 14
ending page: 54

AI-Enhanced Fault Detection and Diagnosis in Manufacturing Systems: Developing Machine Learning Models for Early Detection of Equipment Failures and Root Cause Analysis
type: item
description: 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 rep…
subject: Articles
  topic: section
subject: Artificial Intelligence
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subject: Machine Learning
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subject: Fault Detection
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subject: Root Cause Analysis
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subject: System Reliability
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creator: Nischay Reddy Mitta
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2022-10-03
publication date: 2022-10-03
starting page: 79
ending page: 127

AI-Enhanced Drug Safety Monitoring Systems: Developing Machine Learning Models for Real-Time Risk Assessment, Signal Detection, and Pharmacovigilance in Pharmaceutical Industry
type: item
description: 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, …
subject: Articles
  topic: section
subject: machine learning
  topic: keywords
subject: artificial intelligence
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subject: drug safety monitoring
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subject: real-time risk assessment
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subject: predictive analytics
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subject: adverse drug reactions
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creator: VinayKumar Dunka
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2022-08-09
publication date: 2022-08-09
starting page: 38
ending page: 78

AI-Enhanced Computational Approaches for Optimizing Clinical Trial Design: Leveraging Machine Learning for Patient Recruitment, Trial Monitoring, and Outcome Prediction
type: item
description: 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 computatio…
subject: Articles
  topic: section
subject: artificial intelligence
  topic: keywords
subject: machine learning
  topic: keywords
subject: clinical trials
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subject: patient recruitment
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subject: trial monitoring
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subject: predictive analytics
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subject: real-time data
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creator: Sateesh Kumar Nallamala
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2022-01-10
publication date: 2022-01-10
starting page: 1
ending page: 37

Generative AI for Cyber-Physical System Automated Penetration Testing
type: item
description: 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.
subject: Articles
  topic: section
subject: Generative AI
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subject: penetration testing
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subject: cyber-physical systems
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subject: cybersecurity
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creator: Lena Fischer
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-05-18
publication date: 2025-05-18
starting page: 8
ending page: 13
doi: 10.55222/EJNLPAI.07

Federated AI Models for Distributed Network Botnet Detection
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description: 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.
subject: Articles
  topic: section
subject: Federated Learning
  topic: keywords
subject: Botnet Detection
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subject: Distributed Networks
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subject: Privacy-Preserving AI
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creator: Sanjay Gupta
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-05-16
publication date: 2025-05-16
starting page: 1
ending page: 7
doi: 10.55222/EJNLPAI.06

Multi-Cloud Blockchain Application Trust with AI-Powered Security Analytics
type: item
description: 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.
subject: Articles
  topic: section
subject: AI
  topic: keywords
subject: blockchain
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subject: security analytics
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subject: multi-cloud
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creator: Maria Costa
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 1
ending page: 6

Enhancing Critical DevOps Infrastructure Redundancy and Fault Tolerance with AI
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description: 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
subject: Articles
  topic: section
subject: DevOps
  topic: keywords
subject: redundancy
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subject: predictive maintenance
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subject: automation
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creator: Wei Liu
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2024-12-29
publication date: 2024-12-29
starting page: 7
ending page: 12

DevSecOps Pipelines with AI for Real-Time Vulnerability Remediation
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description: 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.
subject: Articles
  topic: section
subject: DevSecOps
  topic: keywords
subject: AI
  topic: keywords
subject: vulnerability remediation
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subject: real-time security
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subject: machine learning
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creator: Rafael Silva
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2024-12-29
publication date: 2024-12-29
starting page: 13
ending page: 18

Cyber Defense Strategy Orchestration Driven by Artificial Intelligence in Federated 5G Networks
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: 5G Networks
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subject: Federated Networks
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subject: Cyber Defense
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creator: Tetsuo Yamada
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2023-12-31
publication date: 2023-12-31
starting page: 7
ending page: 13

AI-Based Smart Contract Verification: Securing and Accurate Blockchain Ecosystems
type: item
description: 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.
subject: Articles
  topic: section
subject: smart contracts
  topic: keywords
subject: AI techniques
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subject: blockchain security
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subject: verification
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subject: machine learning
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creator: Klara Novak
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2023-12-29
publication date: 2023-12-29
starting page: 1
ending page: 6