An Adaptive AI-Driven Framework for Optimizing Core Web Vitals in Large-Scale Digital Platforms

Predicting software fault proneness is very important as the process of fixing these faults after the release is very costly and time-consuming. In order to predict software fault proneness, many machine learning algorithms (e.g., Logistic regression, Naive Bayes, and J48) were used on several datasets, using different metrics as features. The question is what algorithm is the best under which circumstance and what metrics should be applied. Related works suggested that using change metrics leads to the highest accuracy in prediction. In addition, some algorithms perform better than others in certain circumstances. In this work, we use three machine learning algorithms (i.e., logistic regression, Naive Bayes, and J48) on three Eclipse releases (i.e., 2.0, 2.1, 3.0). The results showed that accuracy is slightly better and false positive rates are lower, when we use the reduced set of metrics compared to all change metrics set. However, the recall and the G score are better when we use the complete set of change metrics. Furthermore, J48 outperformed the other classifiers with respect to the G score for the reduced set of change metrics, as well as in almost all cases when the complete set of change metrics, static code metrics, and the combination of both were used.

It is essential to meet climate goals outlined in the Paris Agreement that the Oil and Gas industry reduces carbon emissions along with achieving production targets. The described body of work herein provided a machine learning (ML) framework to predict upcoming shutdown events which often resulted in event driven flaring with increased carbon emission. Such flaring events are common and many times are not analyzed for root cause until unless they start to become bigger problems or occurred numerous times. One of the primary reasons for event driven flaring is poor process and equipment reliability. Therefore, ML framework is focused towards developing a tool to improve system reliability. Some of the challenges predicting these events are changing process, equipment and operating conditions and highly interactive processes which often results in generating false alarms. To address this issue, this paper proposes an adaptive ML framework where principle component analysis (PCA) is used for anomaly detection. PCA model is updated every hour so that it can update the model with changing operating conditions. PCA model utilizes sensor data from upstream and downstream of the process under consideration along with available equipment data to predict shutdown event. In the current work, three level of alarms are generated using threshold levels corresponding to 95%, 99% and 99.9% of confidence limits for D-statistics on the training data. These alarms correspond to 1) low risk (only reports and no urgency to act), 2) moderate risk (event review is must) and 3) high risk (urgency to act) respectively. The ML model was also able to identify top N sensors which contribute towards shutdown event. The solution was applied to an offshore compressor train and it was found that without using adaptive framework, only 70% of shutdown events can be predicted with lot of false alarms. Using adaptive framework all of the shutdown events were predicted. Although, false alarms were predicted, however the frequency at which false alarms were generated was found low and acceptable for practical application. Two case studies were presented which revealed that adaptive ML framework was found effective to capture events several hours in advance. Root-cause analysis was automated using contribution charts and top 4 key sensors were identified which were contributing towards various shutdown events and were resulting in 1 to 10 hours of flaring. After root-cause analysis for each event, a mitigation solution was presented to avoid similar repeated shutdowns. Several process and operational setting changes were identified to avoid future shutdowns caused by compressor train. Overall, annually 46MMSCF reduction in flare volume and 30-thousand-barrel reduction in lost oil production volume was identified.

Abstract[Context]A software vulnerability becomes harmful for software when an attacker successfully exploits the insecure code and reveals the vulnerability. A single vulnerability in code can put the entire software at risk. Therefore, maintaining software security throughout the software life cycle is an important and at the same time challenging task for development teams. This can also leave the door open for vulnerable code being evolved during successive releases. In recent years, researchers have used software metrics‐based vulnerability prediction approaches to detect vulnerable code early and ensure secure code releases. Software metrics have been employed to predict vulnerability specifically in C/C++ and Java‐based systems. However, the prediction performance of metrics at different granularity levels (class level or method level) has not been analyzed. In this paper, we focused on metrics that are specific to lower granularity levels (Java classes and methods). Based on statistical analysis, we first identified a set of class‐level metrics and a set of method‐level metrics and then employed them as features in machine learning techniques to predict vulnerable classes and methods, respectively. This paper describes a comparative study on how our selected metrics perform at different granularity levels. Such a comparative study can help the developers in choosing the appropriate metrics (at the desired level of granularity). [Objective] The goal of this research is to propose a set of metrics at two lower granularity levels and provide evidence for their usefulness during vulnerability prediction (which will help in maintaining secure code and ensure secure software evolution). [Method] For four Java‐based open source systems (including two releases of Apache Tomcat), we designed and conducted experiments based on statistical tests to propose a set of software metrics that can be used for predicting vulnerable code components (i.e., vulnerable classes and methods). Next, we used our identified metrics as features to train supervised machine learning algorithms to classify Java code as vulnerable or non‐vulnerable. [Result] Our study has successfully identified a set of class‐level metrics and a second set of method‐level metrics that can be useful from a vulnerability prediction standpoint. We achieved recall higher than 70% and precision higher than 75% in vulnerability prediction using our identified class‐level metrics as features of machine learning. Furthermore, method‐level metrics showed recall higher than 65% and precision higher than 80%.

As software evolves, new artifacts are created, modified, or removed. One of these main artifacts generated in the development of object-oriented software is the class. Classes have a very dynamic life cycle that can result in additional costs to the project. One way to mitigate this is to detect, in the early stages of the development, classes that are prone to change. Some approaches in the literature adopt Machine Learning (ML) algorithms to predict the change-proneness of a class. However, most of these approaches do not consider the temporal dependency between training instances, i.e., they consider that the instances are independent. To overcome such a limitation, this study presents an approach for predicting change-proneness based on the class change history. The approach adopts the sliding window method and is evaluated to obtain six kinds of models, which are derived by using, as predictors, different sets of metrics: structural, evolutionary, and smell-based. The evaluation uses five systems, four ML algorithms, and also explores some resample techniques to deal with imbalanced data. Regardless of the kind of model analyzed and the algorithm used, our approach overcomes the traditional one in 378 (~80) cases, out of 420, considering all systems, kinds of models, indicators, and algorithms. Moreover, the results show that our approach presents the best performance when the set of evolutionary metrics is used as predictors. There is no improvement when smell-based metrics are added. The Random Forest algorithm with the resampling technique ADA reaches the best performance among the ML algorithms evaluated.

Fair train-test split in machine learning: Mitigating spatial autocorrelation for improved prediction accuracy

Artificial Intelligence and Machine Learning in Dialysis: Ready for Prime Time?

Demand forecasting is a crucial component of demand management, directly impacting manufacturing companies’ planning, revenues, and actors through the supply chain. We evaluate 21 baseline, statistical, and machine learning algorithms to forecast smooth and erratic demand on a real-world use case scenario. The products’ data were obtained from a European original equipment manufacturer targeting the global automotive industry market. Our research shows that global machine learning models achieve superior performance than local models. We show that forecast errors from global models can be constrained by pooling product data based on the past demand magnitude. We also propose a set of metrics and criteria for a comprehensive understanding of demand forecasting models’ performance.

The aim of this study was to develop a consensus list of metrics to measure the quality of care in anesthesia, perioperative care, and acute pain management in Canada. We sought to conduct a modified Delphi study involving a multidisciplinary panel of perioperative health care professionals (anesthesiologists, surgeons, nurses, internal medicine and family medicine physicians, and hospital administrators), patients, and caregivers. Participants reviewed a candidate list of metrics synthesized from a previous scoping review and performed three rounds of independent iterative scoring and feedback to achieve consensus. In round 3, we asked participants to identify priority metrics to include in a list of core metrics, and we also asked health care professionals to assess the feasibility of implementing each metric. There were 80 participants (49 health care professionals, 22 patients, and 9 caregivers) who completed at least one round of voting, with 56 completing all three rounds. The panel achieved consensus on 87 metrics, of which they deemed 33 to be priority core metrics. The health care professional and patient/caregiver subgroups differed in prioritizing core metrics. Most participants voted airway complications, no residual neuromuscular blockade, difficult airway documentation, complication or critical incident reporting, and complications from pain management the highest priority metrics. Most health care professional participants considered the core metrics to be already measured, currently feasible, or likely feasible by 2025. A multidisciplinary panel developed a list of metrics for measuring the quality of anesthesiology care in Canada. Many metrics require further refinement and validation, and future research is required to guide the measurement techniques and implementation approaches.

To achieve optimal performance, you need a performance optimization framework that defines the performance optimization methods and tools for end-to-end performance optimization. A web performance optimization (WPO) framework addresses various concerns of web performance optimizations and provides a blueprint for the web community to achieve performance goals. This chapter proposes a web reference architecture that identifies all the systems, layers, and components involved in end-to-end performance optimization. A web performance optimization framework looks at web performance holistically and elaborates on the performance optimization methods for each layer.

An adaptive framework to accelerate optimization of high flame retardant composites using machine learning

Brain Age Prediction was developed to address the need for early identification and monitoring of neurodegenerative changes before clinical symptoms appear.Traditional methods rely on cognitive testing and expert MRI interpretation, often leading to late diagnoses.This project introduces a web portal that converts routine MRI scans into predictions of healthy-brain lifespan using machine learning and large neuroimaging datasets.Using the OpenBHB dataset (3,985 individuals aged 5.9-88), four volumetric biomarkers-total intracranial volume, cerebrospinal fluid volume, gray matter volume, and white matter volume-were extracted via K-means clustering and standardized.Chronological age served as the regression target.Three neural architectures were developed: Ultra_ResDNN (residual connections), Ultra_WideDeep (wide-linear + deep-MLP), and Ultra_Attention (multi-head self-attention).Outputs were ensembled to yield robust predictions.On a held-out test set, the ensemble achieved a mean absolute error of 0.58 years and explained 96.38% of variance (R = 0.9638), demonstrating clinical-grade accuracy. I.INTRODUCTIONAdvances in MRI and machine learning have enabled quantitative analysis of brain structure beyond traditional radiological reviews.Early detection of accelerated brain aging is crucial for timely interventions (dietary, pharmacological, cognitive, lifestyle).Current methods are limited by manual preprocessing, specialized software, and expertise requirements.This project aims to democratize access by providing a web-based platform that automatically extracts volumetric biomarkers and predicts remaining healthy-brain lifespan. Key challenges addressed include: Designing accurate predictive models across diverse populations and scanner settings. Automating volumetric feature extraction from NIfTI files. Integrating into a secure, scalable web application. Ensuring compliance with healthcare data standards.

The data warehousing process requires an architectural revolution to settle big data challenges and address new data sources such as social networks, recommendation systems, smart cities, and the web to extract value from shared data. In this respect, the pipeline modeling community for the acquisition, storage, and processing of data for analysis purposes is enacting a wide range of technological solutions that present significant challenges and difficulties. More specifically, the choice of the most appropriate tool for the user's specific business needs and the interoperability between the different tools have become a primary challenges. From this perspective, we propose in this paper a new interactive framework based on machine learning techniques (ML) to assist experts in the process of modeling a customized pipeline for data warehousing. More precisely, we elaborate first (i) an analysis of the experts’ requirements and the characteristics of the data to be processed, then (ii) we propose the most appropriate architecture to their requirements from a multitude of specific architectures instantiated from a generic one, by using (iii) several ML methods to predict the most suitable tool for each phase and task within the architecture. Additionally, our framework is validated through two real-world use cases and user feedback.

Although cyberattacks on machine learning (ML) production systems can be harmful, today, security practitioners are ill-equipped, lacking methodologies and tactical tools that would allow them to analyze the security risks of their ML-based systems. In this article, we perform a comprehensive threat analysis of ML production systems. In this analysis, we follow the ontology presented by NIST for evaluating enterprise network security risk and apply it to ML-based production systems. Specifically, we (1) enumerate the assets of a typical ML production system, (2) describe the threat model (i.e., potential adversaries, their capabilities, and their main goal), (3) identify the various threats to ML systems, and (4) review a large number of attacks, demonstrated in previous studies, which can realize these threats. To quantify the risk posed by adversarial machine learning (AML) threat, we introduce a novel scoring system that assigns a severity score to different AML attacks. The proposed scoring system utilizes the analytic hierarchy process (AHP) for ranking—with the assistance of security experts—various attributes of the attacks. Finally, we developed an extension to the MulVAL attack graph generation and analysis framework to incorporate cyberattacks on ML production systems. Using this extension, security practitioners can apply attack graph analysis methods in environments that include ML components thus providing security practitioners with a methodological and practical tool for both evaluating the impact and quantifying the risk of a cyberattack targeting ML production systems.

This review explores the synergy between forensic accounting and data analytics in identifying fraudulent financial transactions. The convergence of these disciplines has redefined traditional fraud detection methods, enabling higher precision through artificial intelligence, machine learning, and big data analysis. By synthesizing insights from recent empirical studies and case analyses, this paper outlines current methodologies, identifies challenges, and recommends future research directions. These findings highlight the necessity for adaptive and technology-integrated forensic frameworks in a data-driven financial landscape. Keywords: Forensic accounting, data analytics, fraud detection, artificial intelligence, machine learning, big data, financial transactions, empirical studies, forensic methodologies, digital forensics, adaptive frameworks, financial integrity, technology integration, future research, predictive modeling, anomaly detection, forensic audits, financial crime, continuous monitoring, corporate governance.

CORR Synthesis: When Should the Orthopaedic Surgeon Use Artificial Intelligence, Machine Learning, and Deep Learning?