Operational Solution Framework: Leveraging Machine Learning and Natural Language Processing for Automatic Well Test Validation

Integrated STEM projects and experiments promote interdisciplinary learning, enhance critical thinking, and prepare students for real-world problem-solving by bridging science, technology, engineering, and mathematics. While numerous research studies have explored the STEM education domain, few have focused on supporting educators in identifying and designing effective integrated STEM projects and experiments. This research addresses this gap by introducing a novel system that leverages machine learning (ML) and large language models (LLMs) to assist educators in classifying and creating integrated STEM projects.We created and labeled a dataset of 1,071 project descriptions in this research work, mapped to one or more STEM areas (Science, Technology, Engineering, Mathematics). The dataset was developed using manually curated records, harvesting online sources, and synthetic data generation. Natural Language Processing (NLP) techniques were applied to prepare project descriptions for analysis. Various ML models, including Random Forest, Support Vector Machines (SVM), Logistic Regression, and Naive Bayes, were used for multi-label classification. The Random Forest model achieved the best performance, with a Precision of 91.99%, Recall of 94.59%, F1 Score of 93.27%, and Hamming Loss of 6.74%.The system operates in two phases: In Phase 1, the instructor proposes a project title with a description, and in response, the ML model identifies one or more STEM areas as the most relevant. In Phase 2, ChatGPT, specifically GPT-4o, assists educators by defining experiment objectives, generating ideas, designing procedures, and suggesting tools and resources. This human-in-the-loop system enhances collaboration between educators, ML models, and LLMs, streamlining the design of effective integrated STEM projects.This work's implications extend to improving the accessibility and effectiveness of integrated STEM education by reducing the barriers educators face in designing interdisciplinary projects. It provides a scalable framework for integrating advanced AI techniques into educational practices and fostering classroom innovation.

The integration of machine learning (ML) models with cloud computing has transformed the landscape of predictive analytics, offering scalable, efficient, and flexible solutions for organizations. Cloud platforms such as AWS, Google Cloud, and Microsoft Azure enable businesses to deploy and manage complex ML models without the need for extensive on-premise infrastructure. However, optimizing these ML models for performance and cost-efficiency in cloud environments presents unique challenges, including resource management, latency, scalability, and data security. This paper focuses on strategies to optimize machine learning models specifically for predictive analytics in cloud environments. It explores key techniques such as auto-scaling, model compression, and hyperparameter tuning, which are critical for improving the accuracy and speed of predictions while minimizing computational costs. The research also examines advanced tools such as containerization, serverless computing, and cloud-native services that further streamline the deployment and management of ML models. In the Indian context, where cloud adoption is growing rapidly, optimizing ML models is crucial for businesses across various sectors, including finance, healthcare, and e-commerce. By leveraging cloud-based ML solutions, Indian companies can enhance their predictive analytics capabilities, driving smarter decision-making and operational efficiency. This abstract presents an overview of how optimized machine learning models can unlock the full potential of predictive analytics in cloud environments, leading to better business outcomes. Through case studies and practical applications, this paper provides actionable insights into the best practices for optimizing ML models in a cloud-based setting.

Understanding individual genetic factors has led to significant improvements in disease prediction, diagnosis and tailored treatment, and data well beyond what the human mind can comprehend can only be managed through machine learning (ML). It focuses on the use of ML techniques for analyzing and interpreting genomic data, particularly in their contributions to the identification of disease-associated genetic variants. ML uses one or a combination of supervised and unsupervised learning models, deep learning architectures, and ensemble methods to guide the extraction of meaningful information from complex genetic data. The feature selection techniques, including recursive feature elimination (RFE) and principal component analysis (PCA), improve model performance by minimizing dimensions and removing noise. Specific disease susceptibility can be predicted with high accuracy from the genetic markers using classification algorithms, such as random forests (RF), support vector machines (SVM), and neural networks. Clustering techniques (k-means, hierarchical clustering, etc.) are also used to identify hidden genetic subgroups associated with particular diseases. With the development of next-generation sequencing (NGS) and genome-wide association studies (GWAS), big data sets have emerged encouraging the application of ML-based approaches for instance to allow fast and accurate variant classification. CNNs (convolutional neural networks) are effective in modelling sequence-based data, while RNNs (recurrent neural networks) have demonstrated utility in recognizing pathogenic mutations. Naturallanguage processing (NLP) is another approach that further improves the variant annotation process by extracting valuable information about variants from biomedical publications. Future Work In this paper reinforcement learning in used for genetic diagnosis and treatment planning. Explainable AI methods make sure that the model is transparent and reliable and mitigate the black-box issue of deep learning models. However, challenges remain such as data heterogeneity, class imbalance, and explaining ML models. Some federated learning frameworks can aid genome privacy-preserving analysis by allowing collaborative model training without the aggregation of sensitive patient data. These approaches come with ethical questions including potential biases in genetic information resources and fair access to ML solutions in healthcare, that need to be constantly monitored. Further research could aim to improve model generalizability across different populations and develop hybrid ML methods that utilize both statistical and deep learning approaches to provide optimal predictive accuracy. This impressive potential for ML to revolutionize precision medicine lies in its ability to achieve disease prediction, targeted therapeutics development, and hence combat diseases at branch level.

Researchers at INFN (National Institute for Nuclear Physics) face challenges from basic to hard science use cases (e.g., big-data latest generation experiments) in many areas: High Energy Physics (HEP), Astrophysics, Quantum Computing, Genomics, etc. Machine Learning (ML) adoption is ubiquitous in these areas, requiring researchers to solve problems related to the specificity of applications (e.g., tailored models and intricate domain knowledge), but also requiring solving general infrastructure-level and ML-workflow related problems. As the demand for ML solutions continues to rise across the diverse research domains, there exists a critical need for an innovative approach to accelerate ML adoption. In this regard we introduce an Artificial Intelligence (AI) platform designed as an application-agnostic Machine Learning as a Service (MLaaS) solution. The platform provides a paradigm shift by offering a flexible and generalized infrastructure that decouples the ML development process from the specific use cases. The AI platform is implemented as a software layer on top of our cloud platform: the INFN Cloud, a dedicated, geographically distributed infrastructure which offers composable, scalable, and open-source solutions. The AI platform leverages INFN Cloud resources and principles, gathering and orchestrating technologies to support end-to-end scalable ML solutions: Kubernetes, Kubeflow, KServe, KNative, Kueue, Horovod, etc., ensuring support for several ML frameworks: TensorFlow, PyTorch, Apache MXNet, XGBoost, etc. This paper describes the platform’s design and principles, as well as some selected use cases from Natural Language Processing and HEP domains that can take advantage of the “aaS” approach.

AimsHeart failure (HF) is a clinical syndrome with no definitive diagnostic tests. HF registries are often based on manual reviews of medical records of hospitalized HF patients identified using International Classification of Diseases (ICD) codes. However, most HF patients are not hospitalized, and manual review of big electronic health record (EHR) data is not practical. The US Department of Veterans Affairs (VA) has the largest integrated healthcare system in the nation, and an estimated 1.5 million patients have ICD codes for HF (HF ICD‐code universe) in their VA EHR. The objective of our study was to develop artificial intelligence (AI) models to phenotype HF in these patients.Methods and resultsThe model development cohort (n = 20 000: training, 16 000; validation 2000; testing, 2000) included 10 000 patients with HF and 10 000 without HF who were matched by age, sex, race, inpatient/outpatient status, hospital, and encounter date (within 60 days). HF status was ascertained by manual chart reviews in VA's External Peer Review Program for HF (EPRP‐HF) and non‐HF status was ascertained by the absence of ICD codes for HF in VA EHR. Two clinicians annotated 1000 random snippets with HF‐related keywords and labelled 436 as HF, which was then used to train and test a natural language processing (NLP) model to classify HF (positive predictive value or PPV, 0.81; sensitivity, 0.77). A machine learning (ML) model using linear support vector machine architecture was trained and tested to classify HF using EPRP‐HF as cases (PPV, 0.86; sensitivity, 0.86). From the ‘HF ICD‐code universe’, we randomly selected 200 patients (gold standard cohort) and two clinicians manually adjudicated HF (gold standard HF) in 145 of those patients by chart reviews. We calculated NLP, ML, and NLP + ML scores and used weighted F scores to derive their optimal threshold values for HF classification, which resulted in PPVs of 0.83, 0.77, and 0.85 and sensitivities of 0.86, 0.88, and 0.83, respectively. HF patients classified by the NLP + ML model were characteristically and prognostically similar to those with gold standard HF. All three models performed better than ICD code approaches: one principal hospital discharge diagnosis code for HF (PPV, 0.97; sensitivity, 0.21) or two primary outpatient encounter diagnosis codes for HF (PPV, 0.88; sensitivity, 0.54).ConclusionsThese findings suggest that NLP and ML models are efficient AI tools to phenotype HF in big EHR data to create contemporary HF registries for clinical studies of effectiveness, quality improvement, and hypothesis generation.

The proliferation of Internet of Things (IoT) devices in personal and household environments has led to a significant increase in security vulnerabilities. These devices, due to their limited computational resources, often struggle to support conventional security solutions, making them prime targets for cyberattacks. This paper explores the potential of lightweight machine learning (ML) models to enhance the security of personal IoT devices. By leveraging the power of AI, lightweight models can offer real-time threat detection and anomaly identification without compromising the device's performance or requiring extensive computational resources. The paper investigates various ML techniques that are well-suited for IoT environments, such as decision trees, k-nearest neighbours (KNN), and support vector machines (SVM), focusing on their ability to detect intrusions, unauthorized access, and other malicious activities while maintaining efficiency. Additionally, the study highlights the trade-offs between model complexity, accuracy, and resource consumption, offering practical insights for deploying ML solutions in resource-constrained IoT systems. Key challenges, including data privacy, model generalization, and the adaptability of models to diverse IoT ecosystems, are addressed. Finally, the paper discusses future directions for the integration of more advanced lightweight models, such as federated learning and edge computing, which could further enhance security capabilities while ensuring minimal impact on IoT device performance. Through this review, the paper advocates for the adoption of lightweight ML models as a feasible and scalable solution to securing personal IoT devices in an increasingly connected world.

Artificial intelligence and data-driven networks will be integral part of 6G systems. In this article, we comprehensively discuss implementation challenges and need for architectural changes in 5G radio access networks for integrating machine learning (ML) solutions. As an example use case, we investigate user equipment (UE) positioning assisted by deep learning (DL) in 5G and beyond networks. As compared to state of the art positioning algorithms used in today's networks, radio signal fingerprinting and machine learning (ML) assisted positioning requires smaller additional feedback overhead; and the positioning estimates are made directly inside the radio access network (RAN), thereby assisting in radio resource management. In this regard, we study ML-assisted positioning methods and evaluate their performance using system level simulations for an outdoor scenario. The study is based on the use of raytracing tool, a 3GPP 5G NR compliant system level simulator and DL framework to estimate positioning accuracy of the UE. We evaluate and compare performance of various DL models and show mean positioning error in the range of 1-1.5 m for a 2-hidden layer DL architecture with appropriate feature-modeling. Building on our performance analysis, we discuss pros and cons of various architectures to implement ML solutions for future networks and draw conclusions on the most suitable architecture.

The explosion in the scale, sophistication, and persistence of cyber threats is a serious challenge to the national security and digital infrastructure of the United States. Traditional systems that rely on rules and signatures to detect cybersecurity threats are no longer sufficient for detecting and mitigating advanced persistent threats, zero-day attacks, and large-scale, coordinated cyber operations. This study examines the importance of next-generation Artificial Intelligence (AI) and Machine Learning (ML) solutions in the battle to enhance the US's cybersecurity capabilities. The main use case of the research is to test how sophisticated AI-based models can improve threat detection, prediction, and response in complex cyber environments. The research examines key AI and ML technologies, including deep learning, reinforcement learning, anomaly detection models, and explainable artificial intelligence (XAI), with an emphasis on their applicability to intrusion detection, malware analysis, and automated incident response systems. Using a structured analytical framework and performance comparisons, the research explores how these techniques outperform traditional Cybersecurity methods in detection accuracy, response time, and adaptability to changing threats. The results demonstrate that, with AI-enabled cybersecurity systems, threat identification is significantly improved in real time, and false-positive rates decrease, improving the operational efficiency of US cyber defense agencies. Furthermore, the incorporation of explainable AI helps address trust, transparency, and ethical issues related to autonomous decision-making in national security applications. The findings highlight the strategic importance of AI-powered cyber defense architectures and add to ongoing research by providing a comprehensive evaluation of next-generation AI and ML solutions as a cornerstone of resilient, proactive U.S. cyber defense strategies.

Being the necessary data of the city-scale seismic damage simulations, structural types of buildings of a city need to be collected. To this end, a prediction method of structural types of buildings based on machine learning (ML) is proposed herein. Specifically, using the training data of 230,683 buildings in Tangshan city, China, a supervised ML solution based on a decision forest model was designed for the prediction. The scale sensitivity and regional applicability of the designed solution are discussed, respectively, and the results show that the supervised ML solution can maintain high accuracy for different scales; however, it is only suitable for cities similar to the sample city. For wide applicability for various cities, a semi-supervised ML solution was designed based on sampling investigation and self-training procedures. The downtowns of Daxing and Tongzhou districts in Beijing were selected as a case study for the designed semi-supervised ML solution. The overall prediction accuracies of structural types for Daxing and Tongzhou downtowns can reach 94.8% and 99.5%, respectively, which are acceptable for seismic damage simulations. Based on the predicted results, the distributions of seismic damage in Daxing and Tongzhou downtown were output. This study provides a smart and efficient method for obtaining structural types for a city-scale seismic damage simulation.

BACKGROUND Atrial fibrillation (AF) is a progressive disease, and its clinical type is classified according to the AF duration: paroxysmal AF, persistent AF (PeAF; AF duration of less than 1 year), and long-standing persistent AF (AF duration of more than 1 year). When considering the indication for catheter ablation, having a long AF duration is considered a risk factor for recurrence, and therefore, the duration of AF is an important factor in determining the treatment strategy for PeAF. OBJECTIVE This study aims to improve the accuracy of the cardiologists’ diagnosis of the AF duration, and the steps to achieve this goal are to develop a predictive model of the AF duration and validate the support performance of the prediction model. METHODS The study included 272 patients with PeAF (aged 20-90 years), with data obtained between January 1, 2015, and December 31, 2023. Of those, 189 (69.5%) were included in the study, excluding 83 (30.5%) who met the exclusion criteria. Of the 189 patients included, 145 (76.7%) were used as training data to build the machine learning (ML) model and 44 (23.3%) were used as test data for predictive ability of the ML model. Using a questionnaire, 10 cardiologists (group A) evaluated whether the test data (44 patients) included AF of more than a 1-year duration (phase 1). Next, the same questionnaire was performed again after providing the ML model’s answer (phase 2). Subsequently, another 10 cardiologists (group B) were shown the test results of group A, were made aware of the limitations of their own diagnostic abilities, and were then administered the same 2-stage test as group A. RESULTS The prediction results with the ML model using the test data provided 81.8% accuracy (72% sensitivity and 89% specificity). The mean percentage of correct answers in group A was 63.9% (SD 9.6%) for phase 1 and improved to 71.6% (SD 9.3%) for phase 2 (<i>P</i>=.01). The mean percentage of correct answers in group B was 59.8% (SD 5.3%) for phase 1 and improved to 68.2% (SD 5.9%) for phase 2 (<i>P</i>=.007). The mean percentage of answers that differed from the ML model’s prediction for phase 2 (percentage of answers where cardiologists did not trust the ML model and believed their own determination) was 17.3% (SD 10.3%) in group A and 20.9% (SD 5%) in group B and was not significantly different (<i>P</i>=.85). CONCLUSIONS ML models predicting AF duration improved the diagnostic ability of cardiologists. However, cardiologists did not entirely rely on the ML model’s prediction, even if they were aware of their diagnostic capability limitations.

As deployment of Internet of Things (IoT) devices gain momentum, there is an increased interest in implementing machine learning (ML) algorithms on IoT devices. Most of the existing ML solutions, however, rely on a central server to execute data-intensive ML models, because most devices in IoT systems do not have sufficient storage and computing resources. This paper presents a distributed Artificial Neural Networks (ANN) architecture, called PCANN, which allows execution of a complex image recognition task on a collection of resource-constrained IoT devices. Our solution separates a single ML model into multiple small modules that are executed by the distributed IoT devices. The solution effectively reduces storage and computing requirements for individual devices to store and process ML model. We design multiple PCANN models and utilize the models for human posture recognition as the case study. The experimental results show that the distributed PCANN architecture achieves comparable accuracy as the classical ANN model, while the average size of each PCANN module is largely reduced.

Finding the root causes of network performance anomalies is critical to satisfy the quality of service requirements. In this paper, we introduce machine learning (ML) models to process TCP socket statistics to pinpoint underlying reasons of performance issues such as packet loss and jitter. More importantly, we introduce a novel feature engineering method to transform network-dependent metrics (e.g., total packet count and round trip time) in training datasets into network-independent forms to be able to transfer the models to new network settings without requiring to retrain them. Experimental results in various network settings show that the proposed feature engineering approach improves the performance of the models in previously unseen network settings from around 60% to nearly 90%. We believe ability to transfer ML models across networks will pave the way for wide adoption of ML solutions in production networks where collecting labeled data is not possible.

Assessment center (AC) exercises such as role‐plays have established themselves as valuable approaches for obtaining insights into interpersonal behavior, but they are often considered the “Rolls Royce” of personnel assessment due to their high costs. The observation and rating process comprises a substantial part of these costs. In an exploratory case study, we capitalize on recent advances in natural language processing (NLP) by developing NLP‐based machine learning (ML) models to investigate the possibility of automatically scoring AC exercises. First, we compared the convergent‐related validity and contamination with word count of ML scores based on models that used different NLP methods to operationalize verbal behavior. Second, for the model that maximized convergence while minimizing contamination with word count (i.e., a model that used both n‐grams and Universal Sentence Encoder embeddings as predictors), we investigated the criterion‐related validity of its scores. Third, we examined how the interrater reliability of the AC role‐play scores affects ML model convergence. To do so, we applied seven NLP methods to 96 assessees' transcriptions and trained 10 sets of ML models across 18 speeded AC role‐plays to automatically score assessee performance. Results suggest that ML scores recovered most of the original variance in the overall assessment ratings, and replacing one or more human assessors with ML scores maintained criterion‐related validity. Additionally, ML models seemed to exhibit higher convergence when assessors consistently detected and utilized observable behaviors to make ratings (i.e., when interrater reliability was higher). Finally, we provide a step‐by‐step guide for practitioners seeking to implement ML scoring in ACs.

With the advent of smart grid concept, Internet of Things (IoT) and the deployment of smart meters, the cyber-attack threats on power networks have increased due to the use of communication systems that can be accessed by adversaries. Attackers will have the ability to manipulate the outcomes of smart meters which in turn influence the core application of Energy Management System (EMS): State Estimation (SE). Bad data analytic tools may fail to detect some attacks into measurements. Meanwhile, Machine Learning (ML) solutions have been proposed for detecting False Data Injection (FDI) attacks. However, there is a lack of ML time-series solutions presented in the state-of-the-art that is yet to be less complex. In signal processing, time-series solutions does not only consider the signal, but also the statistics of the signal over time. Therefore, in this paper, a machine learning for time-series solutions is presented as an application to model the measurements of the power grid that are used in SE. The presented model takes into account adaptive linear and non-linear filters: Finite Impulse Response (FIR), and Infinite Impulse Response (IIR). The presented models are implemented and performed on the IEEE-118 bus system. The results indicate the advantage of applying those filters over the state-of-the-art machine learning solutions.

The evaluation of load-settlement behavior of piles is very crucial in meeting the serviceability criteria for pile analysis and design. The most reliable approach for estimating this behavior can be achieved by conducting pile load tests. However, due to the considerable expense and time requirement of such in-situ testing, the load-transfer methods have been used routinely in practice. In this paper, an alternative tree-based machine learning (ML) modeling is explored to predict the load-settlement behavior of axially loaded single piles from cone penetration test (CPT) data. Two variants of tree-based ML models, the random forest (RF) and gradient boosted tree (GBT), are developed in this study to estimate the load-settlement behavior of piles from CPT data (corrected cone tip resistance, qt, and sleeve friction, fs). A database of load-settlement curves of 64 static pile load tests and the corresponding CPT test data were compiled and used for the development of these ML models. The developed RF and GBT models are evaluated based on several statistical criteria. The load-settlement curves for six PLTs predicted using the developed RF and GBT models were compared with the measured data and the load-settlement curves predicted using the conventional load-transfer methods. The results demonstrated the great potential of tree-based ML (RF, GBT) models for predicting the load-settlement behavior of axially loaded piles from CPT data. The comparison clearly shows that the ML models outperformed the conventional load-transfer methods. Amongst the two ML models, the results show that the GBT model outperformed the RF model.