Implementing Machine Learning Research Articles

Implementation of Machine Learning in Predicting Pin-Bearing Strength of Aged and Nonaged Pultruded GFRP Composites

Implementation of Machine Learning in Predicting Pin-Bearing Strength of Aged and Nonaged Pultruded GFRP Composites

Improving Privacy and Security: Artificial Intelligence\'s Ability to Detect and Stop Threats with IBM SAAS SIEM

Abstract: Artificial intelligence has become a vital part of cyber security because of its capacity to assess security threats instantly and respond appropriately. AI currently has a greater influence on identifying and thwarting assaults that maintain companies' technological edge. AI's function in cybersecurity focuses primarily on detection and mitigation. Artificial intelligence uses predictive algorithms and sophisticated data analysis to find trends and irregularities in transmitted data and usage that may indicate a potential cyberattack. This enables security staff to react swiftly and proactively to possible assaults. Artificial Intelligence may be employed to stop assaults by using predictive modeling. The findings suggest that further research and development on the integration of machine learning and artificial intelligence into security platforms has a lot of promise. Among the most intriguing applications found are security for networks, detection of malware, and detection of intrusions and response. The poll indicates that 35% of firms intend to implement machine learning and artificial intelligence throughout existing cyber networks, while 45% of companies have already done so.

Decoding the commutable first hyperpolarisability of keto–enol tautomer using DFT and AI-based soft computing method

ABSTRACT Non-linear optical (NLO) parameter of a series of donor–acceptor substituted N-salicylidene-4-benzenesulfonylaniline (SBSA) derivatives with a contrast of first hyperpolarisability has been investigated using density functional-based theory. The derivative of SBSA with both the donor and acceptor substitution (NMe2-SBSA-NO2) exhibits the highest first hyperpolarisability ( ∼ 3 × 103 a.u in gas phase) value. Frequency and solvent polarity responsive behaviours of first hyperpolarisability are utilised to demonstrate the variation of NLO as switchable electrical parameter. Each of the studied compounds displays very high NLO response at frequency corresponding to the absorption maxima of their electronic state. Likewise, the increase of solvent polarity induces a substantial increase of first hyperpolarisability. The computation of NLO responsive first hyperpolarisability by varying the frequency and solvent dielectric constant within a wide domain is very tedious and time-consuming. To overcome the lacuna, we implemented machine learning tools such as artificial neural networks (ANNs), fuzzy logic (FL) and adaptive neuro-fuzzy inference system (ANFIS) to predict the computation data of the NMe2-SBSA-NO2 and moderately good results were obtained using ANFIS as soft computing tool.

Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing Data and Six Machine Learning Methods

It is crucial to monitor algal blooms in freshwater reservoirs through an examination of chlorophyll-a (Chla) concentrations, as they indicate the trophic condition of these waterbodies. Traditional monitoring methods, however, are expensive and time-consuming. Addressing this hindrance, we conducted a comprehensive investigation using several machine learning models for Chla modeling. To this end, we used in situ collected water sample data and remote sensing data from the Sentinel-2 satellite, including spectral bands and indices, for large-scale coverage. This approach allowed us to conduct a comprehensive analysis and characterization of the Chla concentrations across 149 freshwater reservoirs in Ceará, a semi-arid region of Brazil. The implemented machine learning models included k-nearest neighbors, random forest, extreme gradient boosting, the least absolute shrinkage, and the group method of data handling (GMDH); in particular, the GMDH approach has not been previously explored in this context. The forward stepwise approach was used to determine the best subset of input parameters. Using a 70/30 split for the training and testing datasets, the best-performing model was the GMDH model, achieving an R2 of 0.91, an MAPE of 102.34%, and an RMSE of 20.4 μg/L, which were values consistent with the ones found in the literature. Nevertheless, the predicted Chla concentration values were most sensitive to the red, green, and near-infrared bands.

Incorporating a deep‐learning client outcome prediction tool as feedback in supported internet‐delivered cognitive behavioural therapy for depression and anxiety: A randomised controlled trial within routine clinical practice

AbstractIntroductionMachine learning techniques have been leveraged to predict client psychological treatment outcomes. Few studies, however, have tested whether providing such model predictions as feedback to therapists improves client outcomes. This randomised controlled trial examined (1) the effects of implementing therapist feedback via a deep‐learning model (DLM) tool that predicts client treatment response (i.e., reliable improvement on the Patient Health Questionnaire‐9 [PHQ‐9] or Generalized Anxiety Disorder‐7 [GAD‐7]) to internet‐delivered cognitive behavioural therapy (iCBT) in routine clinical care and (2) therapist acceptability of this prediction tool.MethodsFifty‐one therapists were randomly assigned to access the DLM tool (vs. treatment as usual [TAU]) and oversaw the care of 2394 clients who completed repeated PHQ‐9 and GAD‐7 assessments.ResultsMultilevel growth curve models revealed no overall differences between the DLM tool vs. TAU conditions in client clinical outcomes. However, clients of therapists with the DLM tool used more tools, completed more activities and visited more platform pages. In subgroup analyses, clients predicted to be ‘not‐on‐track’ were statistically significantly more likely to have reliable improvement on the PHQ‐9 in the DLM vs. TAU group. Therapists with access to the DLM tool reported that it was acceptable for use, they had positive attitudes towards it, and reported it prompted greater examination and discussion of clients, particularly those predicted not to improve.ConclusionAltogether, the DLM tool was acceptable for therapists, and clients engaged more with the platform, with clinical benefits specific to reliable improvement on the PHQ‐9 for not‐on‐track clients. Future applications and considerations for implementing machine learning predictions as feedback tools within iCBT are discussed.

CareerBoost: Revolutionizing the Job Search with Resume Enhancement and Tailored Recommendations

The Resume Enhancer and Job Recommendation System is designed to meet the unique challenges faced by job seekers in today's dynamic job market. Leveraging cutting-edge natural language processing (NLP) techniques, the present system provides a tailored solution to streamline the job search process. The present Resume Enhancer component utilizes advanced NLP algorithms to analyse resumes and job descriptions, generating comprehensive eligibility scores and targeted skill recommendations. This ensures that candidates' resumes are optimized to effectively showcase their qualifications and expertise to potential employers. The present Job Recommendation feature delivers personalized job listings tailored to each user's selected roles or career aspirations. The authors implemented machine learning algorithms such as the Random Forest Classifier and K-means Clustering, the system matches candidate preferences and qualifications with relevant job opportunities, increasing the likelihood of finding the perfect fit. Overall, the Resume Enhancer and Job Recommendation System serves as a valuable tool for job seekers, empowering them to navigate the complexities of the modern job market with confidence. With its user-centric approach and advanced technology, the present system enhances employability and facilitates career growth for individuals at every stage of their professional journey.

Transforming Cybersecurity into Critical Energy Infrastructure: A Study on the Effectiveness of Artificial Intelligence

This work explores the integration and effectiveness of artificial intelligence in improving the security of critical energy infrastructure, highlighting its potential to transform cybersecurity practices in the sector. The ability of artificial intelligence solutions to detect and respond to cyber threats in critical energy infrastructure environments was evaluated through a methodology that combines empirical analysis and artificial intelligence modeling. The results indicate a significant increase in the threat detection rate, reaching 98%, and a reduction in incident response time by more than 70%, demonstrating the effectiveness of artificial intelligence in identifying and mitigating cyber risks quickly and accurately. In addition, implementing machine learning algorithms has allowed for the early prediction of failures and cyber-attacks, significantly improving proactivity and security management in energy infrastructure. This study highlights the importance of integrating artificial intelligence into energy infrastructure security strategies, proposing a paradigmatic change in cybersecurity management that increases operational efficiency and strengthens the resilience and sustainability of the energy sector against cyber threats.

The Impact of Smart Technologies on the Medicine 4.0 Transformation in the Context of the Technological Revolution

The article examines the impact of smart technologies on the transformation of healthcare 4.0 in the context of the technological revolution, which is defined by the introduction of telemedicine and mHealth, sensors and digital device technologies that have truly evolved in 4.0 and expanded, thanks to new technologies that have made wireless interaction between devices possible. The methodology includes the use of such methods as computer science, axiological, system analysis and synthesis, Agile method, as well as general philosophical methods, which allowed us to analyze the contradictory nature of the impact of smart technologies on the transformation of medicine 4.0. The article analyzes telemedicine and mHealth, which provide remote access and management of medical services. The directions of artificial intelligence and machine learning implementation that are changing the healthcare sector are identified. The place and role of the 3D printing industry for medical applications and nanotechnology are investigated. The essence of the Internet of Things (IoT) as a global network and technologies of interconnected devices and their application in the medical sector is clarified.

Magnetic resonance imaging-based prediction models for differentiating intraspinal schwannomas from meningiomas: classification and regression tree and random forest analysis.

Due to the variations in surgical approaches and prognosis between intraspinal schwannomas and meningiomas, it is crucial to accurately differentiate between the two prior to surgery. Currently, there is limited research exploring the implementation of machine learning (ML) methods for distinguishing between these two types of tumors. This study aimed to establish a classification and regression tree (CART) model and a random forest (RF) model for distinguishing schwannomas from meningiomas. We retrospectively collected 88 schwannomas (52 males and 36 females) and 51 meningiomas (10 males and 41 females) who underwent magnetic resonance imaging (MRI) examinations prior to the surgery. Simple clinical data and MRI imaging features, including age, sex, tumor location and size, T1-weighted images (T1WI) and T2-weighted images (T2WI) signal characteristics, degree and pattern of enhancement, dural tail sign, ginkgo leaf sign, and intervertebral foramen widening (IFW), were reviewed. Finally, a CART model and RF model were established based on the aforementioned features to evaluate their effectiveness in differentiating between the two types of tumors. Meanwhile, we also compared the performance of the ML models to the radiologists. The receiver operating characteristic (ROC) curve, accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were used to evaluate the models and clinicians' discrimination performance. Our investigation reveals significant variations in ten out of 11 variables in the training group and five out of 11 variables in the test group when comparing schwannomas and meningiomas (P<0.05). Ultimately, the CART model incorporated five variables: enhancement pattern, the presence of IFW, tumor location, maximum diameter, and T2WI signal intensity (SI). The RF model combined all 11 variables. The CART model, RF model, radiologist 1, and radiologist 2 achieved an area under the curve (AUC) of 0.890, 0.956, 0.681, and 0.723 in the training group, and 0.838, 0.922, 0.580, and 0.659 in the test group, respectively. The RF prediction model exhibits more exceptional performance than an experienced radiologist in discriminating intraspinal schwannomas from meningiomas. The RF model seems to be better in discriminating the two tumors than the CART model.

Motion Signal-based Recognition of Human Activity from Video Stream Dataset Using Deep Learning Approach

Background: Human physical activity recognition is challenging in various research eras, such as healthcare, surveillance, senior monitoring, athletics, and rehabilitation. The use of various sensors has attracted outstanding research attention due to the implementation of machine learning and deep learning approaches. Aim: This paper proposes a unique deep learning framework based on motion signals to recognize human activity to handle these constraints and challenges through deep learning (e.g., Enhance CNN, LR, RF, DT, KNN, and SVM) approaches. Method: This research article uses the BML (Biological Motion Library) dataset gathered from thirty volunteers with four various activities to analyze the performance metrics. It compares the evaluated results with existing results, which are found by machine learning and deep learning methods to identify human activity. Result: This framework was successfully investigated with the help of laboratory metrics with convolutional neural networks (CNN) and achieved 89.0% accuracy compared to machine learning methods. Conclusion: The novel work of this research is to increase classification accuracy with a lower error rate and faster execution. Moreover, it introduces a novel approach to human activity recognition in the BML dataset using the CNN with Adam optimizer approach.

Data-Centric Machine Learning in Nursing: A Concept Clarification.

The ubiquity of electronic health records and health information exchanges has generated abundant administrative and clinical healthcare data. The vastness of this rich dataset presents an opportunity for emerging technologies (eg, artificial intelligence and machine learning) to assist clinicians and healthcare administrators with decision-making, predictive analytics, and more. Multiple studies have cited various applications for artificial intelligence and machine learning in nursing. However, what is unknown in the nursing discipline is that while greater than 90% of machine-learning implementations use a model-centric strategy, a fundamental change is occurring. Because of the limitations of this approach, the industry is beginning to pivot toward data-centric artificial intelligence. Nurses should be aware of the differences, including how each approach affects their engagement in designing human-intelligent-like technologies and their data usage, especially regarding electronic health records. Using the Norris Concept Clarification method, this article elucidates the data-centric machine learning concept for nursing. This is accomplished by (1) exploring the concept's origins in the data and computer science disciplines; (2) differentiating data- versus model-centric machine learning approaches, including introducing the machine-learning operation life cycle and process; and (3) explaining the advantages of the data-centric phenomenon, especially concerning nurses' engagement in technological design and proper data usage.

Feasibility of Principal Component Analysis for Multi-Class Earthquake Prediction Machine Learning Model Utilizing Geomagnetic Field Data

Geomagnetic field data have been found to contain earthquake (EQ) precursory signals; however, analyzing this high-resolution, imbalanced data presents challenges when implementing machine learning (ML). This study explored feasibility of principal component analyses (PCA) for reducing the dimensionality of global geomagnetic field data to improve the accuracy of EQ predictive models. Multi-class ML models capable of predicting EQ intensity in terms of the Mercalli Intensity Scale were developed. Ensemble and Support Vector Machine (SVM) models, known for their robustness and capabilities in handling complex relationships, were trained, while a Synthetic Minority Oversampling Technique (SMOTE) was employed to address the imbalanced EQ data. Both models were trained on PCA-extracted features from the balanced dataset, resulting in reasonable model performance. The ensemble model outperformed the SVM model in various aspects, including accuracy (77.50% vs. 75.88%), specificity (96.79% vs. 96.55%), F1-score (77.05% vs. 76.16%), and Matthew Correlation Coefficient (73.88% vs. 73.11%). These findings suggest the potential of a PCA-based ML model for more reliable EQ prediction.

Stock Market Analysis Using Machine Learning

The stock market is a dynamic and complex financial system influenced by numerous factors, making accurate forecasting a challenging task. Traditional methods often fall short in capturing the intricate patterns within financial data. This research paper explores the application of machine learning techniques for stock market forecasting, aiming to provide a comprehensive overview of the current state of the field. The paper reviews relevant literature, discusses popular machine learning algorithms, and assesses their effectiveness in predicting stock market trends. Additionally, real-world applications and challenges in implementing machine learning for stock market forecasting are explored.

Implementing Machine Learning Algorithms for Predictive Network Maintenance in 5G and Beyond Networks

With the evolution of fifth generation (5G) network technologies, network maintenance strategies have become increasingly complex, necessitating the use of predictive analysis enabled by Machine Learning (ML) algorithms. This paper emphasizes exploring how ML algorithms can further enhance predictive maintenance in 5G and future networks. It reviews the current literature on this interdisciplinary topic, identifying key ML models such as Decision Trees, Neural Networks, and Support Vector Machines, and discussing their benefits and limitations. Special attention is given to the methodologies in applying these models, handling of data stages, and the training process. Major challenges in implementing ML in the context of network maintenance, such as data privacy, data gathering, model training, and generalizability, are discussed. Furthermore, the research aims to go beyond predicting maintenance needs to introduce a proactive approach in improving overall network performance and pre-empting potential issues based on ML predictions. The paper also discusses possible future trends including advancements in ML algorithms, Automated Machine Learning (AutoML), Explainable AI, and others. The objective is to provide a comprehensive understanding of the current ML-based predictive maintenance field and outline possibilities for future research. The study finds that the application of ML algorithms continues to show promise in transforming the landscape of network management by improving predictive maintenance and proactive performance enhancement strategies. It remains a challenging yet important area in the context of 5G networks.

Machine Learning for Quality Control in Traditional Textile Manufacturing

This research is centered on the practical implementation of machine learning and computer vision technologies to enhance production quality control within the traditional textile industry. The traditional textile sector, known for labor-intensive practices, has slowly adapted to digital transformation. We present a practical case study from Bandung, Indonesia, to validate the effectiveness of our approach in real-world textile manufacturing. By emphasizing machine learning and computer vision, this research narrows the gap between traditional textile practices and digitalization, offering tailored solutions for manufacturers seeking to excel in today's rapidly changing global market. The findings provide valuable insights into the challenges and opportunities of using machine learning and computer vision for production quality control in traditional textile manufacturing. The machine learning models in the study showed good accuracy, ranging from 75% to 100% under various lighting conditions in real-world textile manufacturing environments, confirming their suitability for practical quality control applications.

Machining technologies to compute the psychological-financial index

Subject. This article deals with the issues of measuring the emotions of financial decision-makers based on machine learning technologies in finance. Objectives. The article aims to develop a methodology for measuring emotions in the financial sector, algorithms and software based on artificial intelligence for its implementation in finance. Methods. For the study, we used the methods for marking up text data, index method, machine learning methods, computer technologies for natural language processing, methods for assessing emotions with their own rating scale, induction and deduction, and statistical methods for processing observation results. Results. The article reveals the content of methods for comprehensive measurement of the emotions of financial decision-makers. Software tools were created to implement machine learning technologies when computing an emotion measurement index. Conclusions and Relevance. A psycho-financial index based on automatic calculation using new machine learning technologies provides fundamental analysts of financial markets with an analytical tool that brings the forecast result closer to real conditions, when the emerging combinations of traditional and psychological indicators in the markets provide a new interpretation of current events. This improves the quality of analytical work and improves its results. The result can be used in the work of a fundamental analyst of financial markets. The use of new indicators complements and expands the classic analytical tools and improves the quality of market forecasts.

Enhancing rock fragmentation assessment in mine blasting through machine learning algorithms: a practical approach

The optimization of blasting operations greatly benefits from the prediction of rock fragmentation. The main factors that affect fragmentation are rock mass characteristics, blast geometry, and explosive properties. This paper is a step towards the implementation of machine learning and deep learning algorithms for predicting the extent of fragmentation (in percentage) in open pit mining. While various parameters can affect rock fragmentation, this study considers ten among them (i.e., spacing, drill hole diameter, burden, average bench height, powder factor, number of holes, charge per delay, uniaxial compressive strength, specific drilling, and stemming) to train and test the models. However, due to a weak correlation with rock fragmentation, drill diameter, average bench height, compressive strength, stemming, and charge per delay are eliminated to reduce model complexity. A total of 219 data sets having five input features including the number of holes, spacing, burden, specific drilling, and powder factor are used to develop the models. Machine learning models (random forest regression, support vector regression, and XG boost), as well as a deep learning model (neural network regression), are applied to develop a practical way that can optimize the prediction of fragmentation. This study employs performance measures such as R-squared, RMSE, MSE, MAPE, and MAE. The optimization of the model revealed promising results, indicating that the architecture 5-64-32-16-1 exhibits strong performance. Specifically, the model achieved mean squared error (MSE) values of 41.32 and 28.59 on the training and test datasets, respectively. The R2 value for both training and test is 0.83. RFR is also performing well compared to SVR and XG boost with MSE values of 12.37 and 9.89 on training and testing data, respectively.in both sets, the R2 value is 94%. Based on permutation importance and shapely plot values, it is observed that the powder factor has the highest impact, while the burden has the lowest impact on fragmentation.

Steam Condensation Heat Transfer in the Presence of Noncondensable Gases (NCGs) in Nuclear Power Plants (NPPs): A Comprehensive Review of Fundamentals, Current Status, and Prospects for Future Research

Efficient steam condensation is crucial for safe nuclear power plant (NPP) operations, preventing pressure buildup, overheating, and the release of radioactive materials. However, the presence of noncondensable gases (NCGs), such as air, nitrogen, hydrogen, or helium, can hinder the condensation process by creating a thermal resistance layer that impedes steam diffusion and condensation on the system’s surface. Maximizing the efficiency of steam condensation requires a thorough grasp of the fundamental processes, theories, advancements, and technical hurdles. Therefore, this work thoroughly addresses these needs, with a particular emphasis on addressing the challenges posed by NCGs by dividing the work into four thematic areas. The first theme relates to a comprehensive examination of pure steam condensation phenomena, which includes an exploration of familiar condensation scenarios and various film condensation types. The second theme examines condensation in the presence of NCGs, their mixture properties, and related theories and modelling of heat and mass transfer. The third theme investigates condensation in NPP by exploring passive cooling systems and condensation phenomena under both natural and forced convection conditions during nuclear accidents, the origin of NCGs in NPP and their transportation aspects. This is followed by experimental work related to condensation scenarios and scale. Finally, the last theme looks upon the recent advancements in computational fluid dynamics (CFD) modelling of wall condensation, system analysis codes coupling with CFD, and the implementation of machine learning (ML) for predicting the condensation HTC. By bridging the gap between fundamental knowledge and practical applications, the four thematic areas presented in this work are aimed at providing a comprehensive foundation for researchers and experts in the field of steam condensation when NCGs are involved. The ultimate objective is to bolster the safety and efficacy of NPP operations by understanding the heat and mass transfer mechanisms while mitigating the risk of catastrophic events.

Using Machine Learning for Chronic Disease Diagnosis and Prediction

Machine learning had a strong effect on day-to-day life processes, and in the future, it's going to continue influencing this field. A key where machine learning illustrates promise in healthcare, especially in diagnosing and predicting chronic diseases, conditions that last for at least a year. This paper examines various methods through which machine learning is used in healthcare, consider how these models might be improved to perform specialized tasks, like detecting cancer from individual cells, as they are used to analyse medical images. The paper also explores the inner workings of machine learning and enlightens on some of its current limitations. The article addresses concerns such as model bias and the significant expenses associated with implementing machine learning solutions

Progression from Prediabetes to Diabetes in a Diverse U.S. Population: A Machine Learning Model.

Objective: To date, there are no widely implemented machine learning (ML) models that predict progression from prediabetes to diabetes. Addressing this knowledge gap would aid in identifying at-risk patients within this heterogeneous population who may benefit from targeted treatment and management in order to preserve glucose metabolism and prevent adverse outcomes. The objective of this study was to utilize readily available laboratory data to train and test the performance of ML-based predictive risk models for progression from prediabetes to diabetes. Methods: The study population was composed of laboratory information services data procured from a large U.S. outpatient laboratory network. The retrospective dataset was composed of 15,029 adults over a 5-year period with initial hemoglobin A1C (A1C) values between 5.0% and 6.4%. ML models were developed using random forest survival methods. The ground truth outcome was progression to A1C values indicative of diabetes (i.e., ≥6.5%) within 5 years. Results: The prediabetes risk classifier model accurately predicted A1C ≥6.5% within 5 years and achieved an area under the receiver-operator characteristic curve of 0.87. The most important predictors of progression from prediabetes to diabetes were initial A1C, initial serum glucose, A1C slope, serum glucose slope, initial HDL, HDL slope, age, and sex. Conclusions: Leveraging readily obtainable laboratory data, our ML risk classifier accurately predicts elevation in A1C associated with progression from prediabetes to diabetes. Although prospective studies are warranted, the results support the clinical utility of the model to improve timely recognition, risk stratification, and optimal management for patients with prediabetes.