Advanced Machine Learning Techniques Research Articles

Next-gen security in IIoT: integrating intrusion detection systems with machine learning for industry 4.0 resilience

In the dynamic landscape of Industry 4.0, characterized by the integration of smart technologies and the industrial internet of things (IIoT), ensuring robust security measures is imperative. This paper explores advanced security solutions tailored for the IIoT, focusing on the integration of intrusion detection systems (IDS) with advanced machine learning (ML) and deep learning (DL) techniques. In this paper, we present a novel intrusion detection model to fortify to fortify Industry 4.0 systems against evolving cyber threats by leveraging ML an DL algorithms for dynamic adaptation. To evaluate the performances and effectiveness of our proposed model, we use the improved Coburg intrusion detection data sets (CIDDS) and BoT-IoT datasets, showcasing notable performance attributes with an exceptional 99.99% accuracy, high recall, and precision scores. The model demonstrates computational efficiency, with rapid learning and detection phases. This research contributes to advancing next-gen security solutions for Industry 4.0, offering a promising approach to tackle contemporary cyber.

Athlete Performance Prediction Using Random Forest

Abstract: Fitness and health applications are increasingly being integrated into athletes' training routines, providing new opportunities for personalized performance optimization. This research paper investigates the utilization of smartwatch metrics and machine learning algorithms for predicting athlete performance, focusing on metrics such as power. Through a comprehensive analysis of collected data and employing advanced machine learning techniques, the study aims to provide insights into the predictive capabilities of smartwatch data in the realm of sports science. The introduction of a user-friendly athlete interface further enhances the accessibility and usability of this innovative technology. Smartwatch metrics combined with machine learning algorithms offer a potent toolset for predicting athlete performance. Smartwatches collect vast amounts of data on an athlete's biometrics, such as heart rate, activity levels, and more. Machine learning algorithms analyse this data to uncover patterns, correlations, and trends that are often imperceptible to human observation. By training models on historical data from athletes and their performances, these algorithms can make predictions about future performance, injury risk, optimal training schedules, and even suggest personalized strategies for improvement. This fusion of technology enables coaches and athletes to make data-driven decisions, optimize training regimens, and enhance overall performance while minimizing the risk of injury.

Machine Learning Insights: Exploring Key Factors Influencing Sale-to-List Ratio—Insights from SVM Classification and Recursive Feature Selection in the US Real Estate Market

The US real estate market is a complex ecosystem influenced by multiple factors, making it critical for stakeholders to understand its dynamics. This study uses Zillow Econ (monthly) data from January 2018 to October 2023 across 100 major regions gathered through Metropolitan Statistical Area (MSA) and advanced machine learning techniques, including radial kernel Support Vector Machines (SVMs), used to predict the sale-to-list ratio, a key metric that indicates the market health and competitiveness of the US real estate. Recursive Feature Elimination (RFE) is used to identify influential variables that provide insight into market dynamics. Results show that SVM achieves approximately 85% accuracy, with temporal indicators such as Days to Pending and Days to Close, pricing dynamics such as Listing Price Cut and Share of Listings with Price Cut, and rental market conditions captured by the Zillow Observed Rent Index (ZORI) emerging as critical factors influencing the sale-to-list ratio. The comparison between SVM alphas and RFE highlights the importance of time, price, and rental market indicators in understanding market trends. This study underscores the interplay between these variables and provides actionable insights for stakeholders. By contextualizing the findings within the existing literature, this study emphasizes the importance of considering multiple factors in housing market analysis. Recommendations include using pricing dynamics and rental market conditions to inform pricing strategies and negotiation tactics. This study adds to the body of knowledge in real estate research and provides a foundation for informed decision-making in the ever-evolving real estate landscape.

Multi-agent system architecture for winter road maintenance: a real Spanish case study

AbstractRoad safety remains a critical issue in contemporary society, where the sudden deterioration of road conditions due to weather-related natural phenomena poses significant risks. These abrupt changes can lead to severe safety hazards on the roads, making real-time monitoring and control essential for maintaining road safety. In this context, technological advancements, especially in sensor networks and intelligent systems, play a fundamental role in efficiently managing these challenges. This study introduces an innovative approach that leverages a sophisticated sensor platform coupled with a multi-agent system. This integration facilitates the collection, processing, and analysis of data to preemptively determine the appropriate chemical treatments for roads during severe winter conditions. By employing advanced data analysis and machine learning techniques within a multi-agent framework, the system can predict and respond to adverse weather effects swiftly and with a high degree of accuracy. The proposed system has undergone rigorous testing in a real-world environment, which has verified its operational effectiveness. The results from the deployment of the multi-agent architecture and its predictive capabilities are encouraging, suggesting that this approach could significantly enhance road safety in extreme weather conditions. Furthermore, the proposed architecture allows the system to evolve and scale over time. This paper details the design and implementation of the system, discusses the results of its field tests, and explores potential improvements.

Improving Cardiovascular Disease Forecasting with Machine Learning and Electronic Medical Record Data Characteristics Within a Local Healthcare Network

The PCE Risk Calculator, developed by the ACC/AHA, is frequently utilized in the United States for the purpose of averting the onset of Atherosclerotic cardiovascular disease (ASCVD) via first-line defense strategies. However, this calculator may not accurately estimate risk for certain populations, potentially leading to either under- or over-estimation of risk. We have created calculator for ASCVD risk specific to a population by leveraging advanced Machine Learning (ML) techniques and Electronic Medical Record (EMR) data. Our study involved comparing predictive accuracy of our calculator with PCE calculator. Between January 1, 2009, and April 30, 2020, data was gathered from 101,110 distinct EMRs of patients who were actively receiving treatment. Patient datasets underwent machine learning techniques containing Longitudinal (LT) and Cross-Sectional (CS) features, or solely CS features, derived from laboratory values and vital statistics. The models' effectiveness was assessed using fresh price metric (Screened Cases Percentage @Sensitivity level). In terms of prediction accuracy, every ML model that was tested performed better than the PCE risk calculator. Area Under Curve (AUC) score of 0.902 was obtained by Random Forest (RF) ML technique when CS and LT characteristics were combined (RF-LTC). Our machine learning model only needed to screen 43% of patients in order to identify 90% of positive ASCVD cases, in contrast to the PCE risk calculator, which required screening 69% of patients. Prediction models created using ML techniques reduce the amount number of tests necessary to forecast ASCVD and increase the accuracy of ASCVD prediction when compared to using PCE calculator alone. The combination of LT and CS features in these ML models leads to a significant enhancement in comparing the ASCVD prediction to utilizing CS features exclusively.

Predicting ground surface deformation induced from CO2 plume movement using machine learning

Carbon capture and storage (CCS), which involves injecting carbon dioxide (CO2) into subsurface, is an increasingly popular process for mitigating human caused greenhouse gas emissions. In order to ensure the safety and efficacy of CCS implementation, it is necessary to possess a comprehensive understanding of the complex behaviour of CO2 plumes within geological formations and their potential impact on ground surface deformation. Therefore, conducting research and analysis on these critical aspects is of vital importance. This research provides a methodology to anticipate ground surface deformations, which result from the motion of CO2 plumes utilising an advanced machine learning (ML) technique. The ML surrogate model has been developed using conditional Generative Adversarial Networks (cGAN). The dataset used for the model training and testing comprises ground surface measurements (tiltmeters), reservoir properties, as well as pressure/volume data. The model has been trained and tested using a set of samples created using a forward finite element model. Results show that the surrogate model is capable of predicting reasonably accurate results while running much faster than the forward model.

MERA: Meta-Learning Based Runtime Adaptation for Industrial Wireless Sensor-Actuator Networks

IEEE 802.15.4-based industrial wireless sensor-actuator networks (WSANs) have been widely deployed to connect sensors, actuators, and controllers in industrial facilities. Configuring an industrial WSAN to meet the application-specified quality of service (QoS) requirements is a complex process, which involves theoretical computation, simulation, and field testing, among other tasks. Since industrial wireless networks become increasingly hierarchical, heterogeneous, and complex, many research efforts have been made to apply wireless simulations and advanced machine learning techniques for network configuration. Unfortunately, our study shows that the network configuration model generated by the state-of-the-art method decays quickly over time. To address this issue, we develop a ME ta-learning based R untime A daptation (MERA) method that efficiently adapts network configuration models for industrial WSANs at runtime. Under MERA, the parameters of the network configuration model are explicitly trained such that a small number of optimization steps with only a few new measurements will produce good generalization performance after the network condition changes. We also develop a data sampling method to reduce the measurements required by MERA at runtime without sacrificing its performance. Experimental results show that MERA achieves higher prediction accuracy with less physical measurements, less computation time, and longer adaptation intervals compared to a state-of-the-art baseline.

Analyzing the correlation between body composition variables and cellular phase angle via computerized bioelectrical impedance analysis (BIA)

Background: Various diseases and pathological conditions, as well as inflammatory processes, either in isolation or in combination with advanced age, can lead to significant alterations in body composition. This study investigates the correlation between various body composition metrics and the cellular Phase Angle (PhA) through Computerized Bioelectrical Impedance Analysis (BIA) in a diverse age group of individuals ranging from 18 to 80 years. Objective: Our aim was to explore the interplay between body composition variables such as Body Mass Index, Lean Body Mass, Skeletal Muscle Mass, and their potential influence on the cellular integrity as indicated by PhA. Methods: Utilizing a dataset of 199 participants, we employed both linear and advanced machine learning models, including Random Forest regression, to analyze the predictive relationships and variable importance within our body composition metrics. Results: Initial analyses revealed strong correlations between mass-related measures and suggested complex relationships with the PhA, an indicator of cellular health and membrane integrity. The Random Forest model significantly outperformed simple linear regression in predicting PhA, emphasizing the nonlinear nature of these relationships and the importance of a comprehensive approach in body composition assessment. Conclusion: Our findings highlight the nuanced interactions between body composition variables and their collective impact on cellular health as measured by PhA. This study underscores the potential of utilizing PhA alongside traditional metrics for a more nuanced understanding of body composition and its implications for health. Future research should continue to leverage advanced statistical and machine learning techniques to further elucidate these complex relationships, with implications for nutritional, sports medicine, and biomedical fields. Initial analyses revealed strong correlations between mass-related measures and suggested complex relationships with the Phase Angle, an indicator of cellular health and membrane integrity. The Random Forest model significantly outperformed simple linear regression in predicting PhA, emphasizing the nonlinear nature of these relationships and the importance of a comprehensive approach in body composition assessment. Our findings highlight the nuanced interactions between body composition variables and their collective impact on cellular health as measured by PhA. This study underscores the potential of utilizing PhA alongside traditional metrics for a more nuanced understanding of body composition and its implications for health. Future research should continue to leverage advanced statistical and machine learning techniques to further elucidate these complex relationships, with implications for nutritional, sports medicine, and biomedical fields.

Reducing delivery insurance costs through risk score model for food delivery company

In this paper, we propose a novel pricing model for delivery insurance in a food delivery company in Latin America, with the aim of reducing the high costs associated with the premium paid to the insurer. To achieve this goal, a thorough analysis was conducted to estimate the probability of losses based on delivery routes, transportation modes, and delivery drivers’ profiles. A large amount of data was collected and used as a database, and various statistical models and machine learning techniques were employed to construct a comprehensive risk profile and perform risk classification. Based on the risk classification and the estimated probability associated with it, a new pricing model for delivery insurance was developed using advanced mathematical algorithms and machine learning techniques. This new pricing model took into account the pattern of loss occurrence and high and low-risk behaviors, resulting in a significant reduction of insurance costs for both the contracting company and the insurer. The proposed pricing model also allowed for greater flexibility in insurance contracting, making it more accessible and appealing to delivery drivers. The use of estimated loss probabilities and a risk score for the pricing of delivery insurance proved to be a highly effective and efficient alternative for reducing the high costs associated with insurance, while also improving the profitability and competitiveness of the food delivery company in Latin America.

Experimental investigation and machine learning modeling of diethylenetriaminepentaacetic acid agents in sandstone rock wettability alteration: Implications for enhanced oil recovery processes

Chelating agents have garnered increasing attention due to their unique capabilities in addressing challenges associated with chemical enhanced oil recovery (CEOR) materials. This research investigates the transformative potential of diethylenetriaminepentaacetic acid (DTPA) chelating agent in altering sandstone rock wettability. Wettability and zeta potential measurements were used to assess the impact of crucial factors including DTPA concentration, salinity, and potential determining ions (PDIs) on contact angle and rock surface charges. The findings revealed that there is an optimal level for DTPA concentration and salinity that can shift rock wettability from an oil-wet to strongly water-wet state. Moreover, while PDIs in their natural concentrations had no significant impact on DTPA performance, a threefold increase in their concentration was found to adversely affect DTPA efficacy. After conducting rock wettability tests, two advanced machine learning (ML) techniques, namely Random Forest (RF) and Boosted Regression Tree (BRT), were employed to assess rock/oil contact angle based on the aforementioned factors. Data were collected from 240 experimental datasets of contact angles. The findings indicated that both models performed well, but BRT exhibited exceptional performance. Sensitivity analysis, conducted using the Jackknife method, revealed the order of importance for parameters affecting wettability as follows: PDIs > salinity > time > DTPA concentration.

A Comparative Analysis of Advanced Machine Learning Techniques for River Streamflow Time-Series Forecasting

A Comparative Analysis of Advanced Machine Learning Techniques for River Streamflow Time-Series Forecasting

Innovative integrated workflow for data-driven production forecasting and well completion optimization: A Montney Formation case study

With the use of advanced data analytics and machine learning (ML) techniques, the oil and gas industry has transformed significantly in recent years. These innovations have opened new opportunities in reservoir engineering, allowing engineers to leverage data for better decision-making and well completion optimization. A key challenge in hydrocarbon exploration and production is forecasting gas production rates from unconventional sources. This is a crucial factor that affects how resources are allocated and decisions are made, requiring the application of novel techniques to enhance hydrocarbon production. Here, a novel and comprehensive automated workflow is introduced covering data collection, data preparation, feature selection, ML model development, and operational parameter optimization. The workflow uses various ML and optimization methods, including a modified Differential Evolution (MDE) algorithm, to improve gas production forecasting and optimize well completion parameters. We applied the proposed workflow to 3144 horizontal gas wells in the Montney Formation, British Columbia (BC), Canada, to assess its performance. The results showed the advantages of incorporating the initial 24 months of cumulative gas production data alongside well and completion data. Also, following the fine-tuning of hyperparameters for the ML algorithms, we identified the most effective model for forecasting cumulative gas production within the Montney Formation as a two-layer Artificial Neural Network (ANN) model. Moreover, using the MDE algorithm, the optimization results revealed the possibility of increasing gas production by more than 10% for about 47% of the wells. This research makes a significant contribution to the field of unconventional gas exploration by introducing an automated workflow that simplifies data processing, model development, and decision-making processes. It also offers valuable insights for optimizing unconventional gas production in the Montney Formation and similar reservoirs, as the energy industry moves towards more sustainable and efficient practices.

Predicting Indian GDP with Machine Learning: A Comparison of Regression Models

In recent years, the application of machine learning algorithms in economic forecasting has gained significant traction. This study focuses on predicting the Gross Domestic Product (GDP) of India by harnessing advanced machine learning techniques. We assembled a comprehensive dataset incorporating time series analysis and inflation rates from diverse sources. Our investigation involved a comparative analysis employing both linear and polynomial regression methods to discern the most accurate predictive model. Our findings underscore the superiority of the polynomial regression model, which excelled in capturing non-linear relationships between independent variables and GDP. Specifically, the polynomial regression model achieved an impressive prediction accuracy rate of surpassing the accuracy achieved by the linear regression model. This study underscores the critical role of advanced machine learning algorithms in economic forecasting. By emphasizing the significance of high-quality datasets and the application of techniques like polynomial regression, we illustrate how these factors can substantially enhance the precision of economic forecasts. These insights hold substantial implications for policy makers and businesses alike, aiding informed decision-making and effective economic policy development. This study serves as a valuable reference for future research, highlighting the importance of advanced machine learning methods and robust data sources in economic forecasting.

Unveiling the intricate connection between per- and polyfluoroalkyl substances and prostate hyperplasia

The presence of perfluoroalkyl and polyfluoroalkyl substances (PFAS) in various everyday products has raised concerns about their potential impact on prostate health. This study aimed to investigate the effects of different types of PFAS on prostate health, including PFDeA, PFOA, PFOS, PFHxS, and PFNA. To assess the relationship between PFAS exposure and prostate injury, machine learning algorithms were employed to analyze prostate-specific antigen (PSA) metrics. The analysis revealed a linear and positive dose-dependent association between PFOS and the ratio of free PSA to total PSA (f/tPSA). Non-linear dose-response relationships were observed between the other four types of PFAS and the f/tPSA ratio. Additionally, the analysis showed a positive association between the mixture of PFAS and prostate hyperplasia, with PFNA having the highest impact followed by PFOS. These findings suggest that elevated serum levels of PFDeA, PFOA, PFOS, and PFNA are linked to prostate hyperplasia. Therefore, this study utilized advanced machine learning techniques to uncover potential hazardous effects of PFAS exposure on prostate health, specifically the positive association between PFAS and prostate hyperplasia.

Prediction of seismic-induced bending moment and lateral displacement in closed and open-ended pipe piles: A genetic programming approach

Ensuring the reliability of pipe pile designs under earthquake loading necessitates an accurate determination of lateral displacement and bending moment, typically achieved through complex numerical modeling to address the intricacies of soil-pile interaction. Despite recent advancements in machine learning techniques, there is a persistent need to establish data-driven models that can predict these parameters without using numerical simulations due to the difficulties in conducting correct numerical simulations and the need for constitutive modelling parameters that are not readily available. This research presents novel lateral displacement and bending moment predictive models for closed and open-ended pipe piles, employing a Genetic Programming (GP) approach. Utilizing a soil dataset extracted from existing literature, comprising 392 data points for both pile types embedded in cohesionless soil and subjected to earthquake loading, the study intentionally limited input parameters to three features to enhance model simplicity: Standard Penetration Test (SPT) corrected blow count (N60), Peak Ground Acceleration (PGA), and pile slenderness ratio (L/D). Model performance was assessed via coefficient of determination (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), with R2 values ranging from 0.95 to 0.99 for the training set, and from 0.92 to 0.98 for the testing set, which indicate of high accuracy of prediction. Finally, the study concludes with a sensitivity analysis, evaluating the influence of each input parameter across different pile types.

Artificial Intelligence-Driven Model for Gold Price Prediction

Artificial Intelligence-Driven Model for Gold Price Prediction

Machine learning insights into HIV outbreak predictions in Sub-Saharan Africa

Predicting and preventing HIV outbreaks in Sub-Saharan Africa, a region disproportionately affected by the epidemic remains a significant challenge. This review explores the effectiveness and challenges of using machine learning (ML) for forecasting HIV spread in high-risk areas. ML models have shown promise in identifying patterns and trends in HIV data, enabling more accurate predictions and targeted interventions. ML insights into HIV outbreak predictions leverage various data sources, including demographic, epidemiological, and behavioural data. By analysing these data, ML algorithms can identify high-risk populations and geographical areas susceptible to HIV transmission. This information is crucial for public health authorities to allocate resources efficiently and implement preventive measures effectively. Despite the potential benefits, several challenges exist in using ML for HIV outbreak predictions. These include data quality issues, such as incomplete or inaccurate data, which can affect the reliability of predictions. Additionally, the complexity of HIV transmission dynamics and the need for real-time data pose challenges for ML models. To address these challenges, researchers and practitioners are exploring innovative approaches, such as integrating multiple data sources and using advanced ML techniques. Collaborations between researchers, public health officials, and technology experts are also crucial for developing robust ML models for HIV outbreak predictions. In conclusion, while ML offers valuable insights into HIV outbreak predictions in Sub-Saharan Africa, addressing challenges such as data quality and model complexity is essential for its effective use. By overcoming these challenges, ML has the potential to significantly improve HIV prevention efforts and ultimately reduce the burden of the epidemic in the region. Keywords: Machine Learning, AI, HIV Outbreaks: Predictions, Insights.

Advancing cuffless blood pressure estimation: A PPG-based multi-task learning model for enhanced feature extraction and fusion

Cuffless continuous blood pressure (BP) monitoring is essential for personalized health management. Although existing cuffless BP estimation applies advanced machine learning techniques and integrates PPG signals, it is deficient in feature extraction and fusion. In addition, it is inefficient to train the model separately for different tasks. In this study, an advanced multi-domain and local–global feature parallel multi-task learning network (MDLG-MTLNet) is introduced. The MDLG-MTLNet was designed with three key aspects: first, temporal and multi-scale spatial features were extracted from PPG signals and their derivatives via a multi-scale spatial and temporal feature block; interaction of features from different domains was facilitated by the introduction of a local–global attention module that captured and efficiently fused local–global information; and lastly, intrinsic correlation between systolic (SBP) and diastolic blood pressure (DBP) was taken into account via a multi-task learning network to improve the overall performance of the model. On the MIMIC-II dataset, the MAEs of MDLG-MTLNet for DBP, SBP, and MBP were 2.64 mmHg, 1.57 mmHg, and 2.02 mmHg, respectively. These errors were superior to those of the existing methods, meeting the AAMI criteria, and earning an A grade according to the BHS protocol. The experimental results confirm the effectiveness of our proposed model in feature extraction and fusion.

Sign Speak: Recogninzing Sign Language with Machine Learning

Sign language serves as a critical means of communication for individuals with hearing impairments, enabling them to integrate into society effectively and express themselves. However, interpreting and recognizing sign language gestures present unique challenges due to the dynamic nature of gestures and spatial dependencies inherent in sign language communication. As a response, the SignSpeak project employs advanced machine learning techniques to address these challenges and enhance accessibility for the deaf and hard of hearing community. The project leverages a diverse dataset sourced from Kaggle, comprising images of sign language gestures captured in various contexts. The integration of advanced algorithms, such as 3D Convolutional Neural Networks (CNNs), and Gated Recurrent Units (GRUs), enables SignSpeak to recognize and interpret sign language gestures accurately and in real-time. This integration allows the model to capture both spatial and temporal features inherent in sign language, thus enabling more robust and accurate recognition. The project encompasses several critical stages, including data preprocessing, model development, training, and evaluation. Data preprocessing involves converting the image data into a suitable format and applying augmentation techniques to enhance the diversity and robustness of the dataset. Model development entails designing a deep learning architecture that combines CNNs and GRUs to effectively capture spatial and temporal dependencies in sign language gestures. Training the model involves optimizing parameters and hyperparameters to achieve optimal performance. Evaluation metrics such as accuracy, F1 score, and recall are utilized to assess the model's performance on both training and validation datasets. The trained model is then tested on a separate test dataset to evaluate its real- world performance and generalization ability. Experimental results demonstrate the efficacy of the SignSpeak approach in accurately recognizing and interpreting sign language gestures. The model achieves high accuracy scores, demonstrating its potential to enhance accessibility and inclusion for individuals with hearing impairments. By providing real-time translation of sign language into text or speech, SignSpeak contributes to breaking down communication barriers and promoting equal participation for all members of society.

Artificial intelligence-based prediction models of bio-treated sand strength for sustainable and green infrastructure applications

As a sustainable, environmentally friendly, and green solution, biomineralization/bio-treatment process has recently emerged for enhancing the strength characteristics of loose sands by inducing cementation material commonly referred to as microbially induced calcite/cementation precipitation (MICP). To date, existing research on the unconfined compression strength of MICP-treated sands (UCSmicp) relies heavily on experimental data, with predictions primarily based on linear or curvilinear models that consider calcite (CaCO3) as the main influencing factor. However, there is limited exploration into the application of artificial intelligence (AI) and machine learning (ML) methods for UCSmicp prediction involving multiple influencing factors and their validation on unseen laboratory data. Therefore, this study intends to propose new prediction models of UCSmicp using four advanced ML techniques: Gaussian Process Regression (GPR), Adaptive Neuro Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), and Gene Expression Programming (GEP). The models incorporate various factors affecting UCSmicp, including sand uniformity coefficient (Cu), initial void ratio (e), mean grain size (D50), optical density at 600 mm wavelength (OD600), urea concentration (U), calcium concentration (Ca), and calcite content (C). Performance evaluation of models involved statistical assessments, error analysis, cross validation on laboratory data, and sensitivity and monotonicity studies. Results indicate that the proposed models exhibit exceptional precision across training, testing, and validation data in predicting UCSmicp, with ANN demonstrating the highest accuracy, followed by GPR, ANFIS, and GEP. Sensitivity analysis identifies CaCO3 as the main influencing factor on UCSmicp, while monotonicity analysis confirms alignment with physical processes. This work advocates for MICP as an eco-friendly solution and highlights the efficacy of advanced ML techniques in fostering sustainable infrastructure.