Machine Learning Techniques For Prediction Research Articles

Predictive machine learning in optimizing the performance of electric vehicle batteries: Techniques, challenges, and solutions

AbstractThis research paper explores the importance of optimizing the performance of electric vehicle (EV) batteries to align with the rapid growth in EV usage. It uses predictive machine learning (ML) techniques to achieve this optimization. The paper covers various ML methods like supervised, unsupervised, and deep learning (DL) and ways to measure their effectiveness. Significant battery performance factors, such as state of charge (SoC), state of health (SoH), state of function (SoF), and remaining useful life (RUL), are discussed, along with methods to collect and prepare data for accurate predictions. The paper introduces an operation research model for optimizing the performance of EV Batteries. It also looks at challenges unique to battery systems and ways to overcome them. The study showcases ML models' ability to predict battery behavior for real‐time monitoring, efficient energy use, and proactive maintenance. The paper categorizes different applications and case studies, providing valuable insights and forward‐looking perspectives for researchers, practitioners, and policymakers involved in improving EV battery performance through predictive ML.This article is categorized under: Technologies > Classification Fundamental Concepts of Data and Knowledge > Explainable AI Technologies > Machine Learning

Comparing machine learning techniques for software requirements risk prediction

Software requirements are the most critical phase focused on documenting, eliciting, and maintaining the stakeholders’ requirements. Risk identification and analysis are preemptive actions designed to anticipate and prepare for potential issues. Usually, this classification of risks is done manually, a practice that the personal judgment of the risk analyst or the project manager might influence. Machine learning (ML) techniques were proposed to predict the risk level in software requirements. The techniques used were logistic regression (LR), multilayer perceptron (MLP) neural network, support vector machine (SVM), decision tree (DT), naive bayes, and random forest (RF). Each model was trained and tested using cross-validation with k-folds, each with its respective parameters, to provide optimal results. Finally, they were compared based on precision, accuracy, and recall metrics. Statistical tests were performed to determine if there were significant differences between the different ML techniques used to classify risks. The results concluded that the DT and RF are the techniques that best predict the risk level in software requirements.

Performance prediction and analysis of engineered cementitious composites based on machine learning

This study presents the implementation of machine learning (ML) techniques for mechanical properties prediction and analysis of polyethylene fiber-reinforced ECC (PE-ECC). A comprehensive database including different mechanical properties of PE-ECC was first constructed, with total 50 compressive strengths, 123 tensile strengths and 123 tensile strain capacities being assembled. Grey relational analysis was used to investigate the sensitivity of the critical parameters of PE-ECC’s mechanical properties. The evaluation results showed that the supplementary cementitious materials-to-binder ratio, water-to-binder ratio, sand-to-binder ratio, and fiber reinforcing index have significant effects on the mechanical properties of PE-ECC. Three representative ML techniques were utilized and demonstrated good predictive performance. A parametric study was further undertaken to quantify the effects of the selected parameters on the mechanical properties of PE-ECC based on the ML models. This study aims to help researchers and engineers estimate material properties of PE-ECC more effectively and provide supports for ECC design.

Implementing machine learning techniques for continuous emotion prediction from uniformly segmented voice recordings.

Emotional recognition from audio recordings is a rapidly advancing field, with significant implications for artificial intelligence and human-computer interaction. This study introduces a novel method for detecting emotions from short, 1.5 s audio samples, aiming to improve accuracy and efficiency in emotion recognition technologies. We utilized 1,510 unique audio samples from two databases in German and English to train our models. We extracted various features for emotion prediction, employing Deep Neural Networks (DNN) for general feature analysis, Convolutional Neural Networks (CNN) for spectrogram analysis, and a hybrid model combining both approaches (C-DNN). The study addressed challenges associated with dataset heterogeneity, language differences, and the complexities of audio sample trimming. Our models demonstrated accuracy significantly surpassing random guessing, aligning closely with human evaluative benchmarks. This indicates the effectiveness of our approach in recognizing emotional states from brief audio clips. Despite the challenges of integrating diverse datasets and managing short audio samples, our findings suggest considerable potential for this methodology in real-time emotion detection from continuous speech. This could contribute to improving the emotional intelligence of AI and its applications in various areas.

Credit Risk Prediction Using Explainable AI

Despite advancements in machine-learning prediction techniques, the majority of lenders continue to rely on conventional methods for predicting credit defaults, largely due to their lack of transparency and explainability. This reluctance to embrace newer approaches persists as there is a compelling need for credit default prediction models to be explainable. This study introduces credit default prediction models employing several tree-based ensemble methods, with the most effective model, XGBoost, being further utilized to enhance explainability. We implement SHapley Additive exPlanations (SHAP) in ML-based credit scoring models using data from the US-based P2P Lending Platform, Lending Club. Detailed discussions on the results, along with explanations using SHAP values, are also provided. The model explainability generated by Shapely values enables its applicability to a broad spectrum of industry applications.

Machine Learning Prediction Techniques in the Optimization of Diagnostic Laboratories’ Network Operations

The article presents an outline of the concept of a prototype system allowing for the optimization of inventory management in a diagnostic laboratory on the basis of patients results. The effectiveness of laboratory diagnostics depends largely on the appropriate management of resources and the quality of tests. A functional quality management system is an integral element of every diagnostic laboratory, ensuring reliability and appropriate work standards. This system includes maintaining correct and reliable analytical test results as well as the optimal use of the laboratory equipment’s processing capacity and the appropriate organization of the supply chain—both analytical material and reagents. It is extremely important to avoid situations in which tests cannot be performed due to a lack of reagents, the overloading of analyzers, or improper calibration. Therefore, the accurate prediction of the number of orders is crucial to optimize the laboratory’s operations, both in the short term—for the next few hours and minutes—and in the longer term, even monthly, which will allow for the appropriate planning of reagent stock. As part of the research presented in this article, machine learning methods were used to implement the above functionalities, which allowed for the development of a prototype of a laboratory optimization system using patient test results as a basis.

Functional group analysis and machine learning techniques for MIE prediction

The successful prediction of minimum ignition energies (MIEs) for 55 flammable organic molecules has been accomplished through group contribution and machine learning methods. The applied techniques include least squares regression, Huber regression, and kernel ridge regression, with the Marrero/Gani method applied to determine structurally dependent descriptors to uniquely characterize each molecule. These descriptors were used as predictors for the aforementioned regressions techniques to develop four predictive models. The rudimentary least squares regression resulted in a modest R2 of 0.939 on the at-large data set, but overpredicted the MIEs of several compounds. An improved least squares regression featured a lower R2 of 0.840, but with virtually no overprediction. Outlier analysis was conducted with the Huber and kernel ridge techniques, and these models exhibited reduced outlier influence and considered the non-linear relationship between predictors and MIEs. These improved algorithms also used L2 regularization to reduce sensitivity of MIE predictions on statistically insignificant descriptors. Resulting R2 values for models developed using the Huber and kernel ridge techniques came out to be 0.878 and 0.991, respectively, when applied to the at-large data set, and featured little overprediction. Thus, it is concluded that simple group contribution methods, optimized by Huber and kernel ridge techniques, are potential modeling alternatives for simple and accurate prediction of organic molecule MIEs.

Machine Learning Techniques for Fatal Accident Prediction

Abstract Ensuring public safety on our roads is a top priority, and the prevalence of road accidents is a major concern. Fortunately, advances in machine learning allow us to use data to predict and prevent such incidents. Our study delves into the development and implementation of machine learning techniques for predicting road accidents, using rich datasets from Catalonia and Toronto Fatal Collision. Our comprehensive research reveals that ensemble learning methods outperform other models in most prediction tasks, while Decision Tree and K-NN exhibit poor performance. Additionally, our findings highlight the complexity involved in predicting various aspects of crashes, as the Stacking Regressor shows variability in its performance across different target variables. Overall, our study provides valuable insights that can significantly contribute to ongoing efforts to reduce accidents and their consequences by enabling more accurate predictions.

Explainable machine learning in outcome prediction of high-grade aneurysmal subarachnoid hemorrhage.

Accurate prognostic prediction in patients with high-grade aneruysmal subarachnoid hemorrhage (aSAH) is essential for personalized treatment. In this study, we developed an interpretable prognostic machine learning model for high-grade aSAH patients using SHapley Additive exPlanations (SHAP). A prospective registry cohort of high-grade aSAH patients was collected in one single-center hospital. The endpoint in our study is a 12-month follow-up outcome. The dataset was divided into training and validation sets in a 7:3 ratio. Machine learning algorithms, including Logistic regression model (LR), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), were employed to develop a prognostic prediction model for high-grade aSAH. The optimal model was selected for SHAP analysis. Among the 421 patients, 204 (48.5%) exhibited poor prognosis. The RF model demonstrated superior performance compared to LR (AUC = 0.850, 95% CI: 0.783-0.918), SVM (AUC = 0.862, 95% CI: 0.799-0.926), and XGBoost (AUC = 0.850, 95% CI: 0.783-0.917) with an AUC of 0.867 (95% CI: 0.806-0 .929). Primary prognostic features identified through SHAP analysis included higher World Federation of Neurosurgical Societies (WFNS) grade, higher modified Fisher score (mFS) and advanced age, were found to be associated with 12-month unfavorable outcome, while the treatment of coiling embolization for aSAH drove the prediction towards favorable prognosis. Additionally, the SHAP force plot visualized individual prognosis predictions. This study demonstrated the potential of machine learning techniques in prognostic prediction for high-grade aSAH patients. The features identified through SHAP analysis enhance model interpretability and provide guidance for clinical decision-making.

A Comprehensive Review on Machine Learning Techniques for Protein Family Prediction.

Proteomics is a field dedicated to the analysis of proteins in cells, tissues, and organisms, aiming to gain insights into their structures, functions, and interactions. A crucial aspect within proteomics is protein family prediction, which involves identifying evolutionary relationships between proteins by examining similarities in their sequences or structures. This approach holds great potential for applications such as drug discovery and functional annotation of genomes. However, current methods for protein family prediction have certain limitations, including limited accuracy, high false positive rates, and challenges in handling large datasets. Some methods also rely on homologous sequences or protein structures, which introduce biases and restrict their applicability to specific protein families or structures. To overcome these limitations, researchers have turned to machine learning (ML) approaches that can identify connections between protein features and simplify complex high-dimensional datasets. This paper presents a comprehensive survey of articles that employ various ML techniques for predicting protein families. The primary objective is to explore and improve ML techniques specifically for protein family prediction, thus advancing future research in the field. Through qualitative and quantitative analyses of ML techniques, it is evident that multiple methods utilizing a range of classifiers have been applied for protein family prediction. However, there has been limited focus on developing novel classifiers for protein family classification, highlighting the urgent need for improved approaches in this area. By addressing these challenges, this research aims to enhance the accuracy and effectiveness of protein family prediction, ultimately facilitating advancements in proteomics and its diverse applications.

A Systematic Review on the use of Machine Learning and Deep Learning Techniques for Crime Prediction from Social Media

The goal of crime prediction is to help law enforcement authorities to prevent crimes before they happen by identifying and reducing potential future crimes. In present days, crime cases are rapidly occur so it is a challenging task to accurately predict and classify the future crimes, as criminal patterns are adaptable and constantly evolve, making it a challenging task. Sentiment analysis is essential for deciphering the feelings conveyed in text since social media has grown into a popular venue for individuals to communicate their thoughts, feelings, views, and comments. This analysis is particularly valuable for making informed decisions in business, politics and government agencies especially to identify crimes. However, it faces the difficulties like lexical diversity and dataset imbalance. In order to make better decisions and maybe lower the risk of predicting the suspects in a crime, early crime prediction has lately made use of Artificial Intelligence (AI) models like Deep Learning (DL) and Machine Learning (ML). When applied to crime data, ML and DL models may assist pinpoint possible crime hotspots and foretell when crimes may occur. Crime prediction and categorization using social media posts is covered in depth in this paper, which offers a comprehensive overview of several ML and DL frameworks. Initially, different ML and DL based crime prediction models designed by many researchers are examined in brief. The next step is to do a comparison research to learn about the shortcomings of those frameworks and provide an alternative method for effectively predicting crimes based on social media postings.

Empirical validation of machine learning techniques for heterogeneous cross-project change prediction and within-project change prediction

Software change prediction plays key role for maintaining software quality. Identification of change prone parts of a software in early stages helps in optimization of resources and amount of effort required for the maintenance of software. The change prediction model can be built by using the same project for training and testing, which is termed as within-project change prediction. Sometimes, the available data is not sufficient for both training and testing, in such cases, it is beneficial to use other projects for testing. Usage of two different projects for training and testing for identification of changes is termed as Cross-Project Change Prediction (CPCP). In CPCP, the data distribution of features differentiates. The scalability of prediction models can be increased by using the two different projects for training and testing with different features, which is termed as Heterogeneous Cross-Project Change Prediction (HCPCP). Thus, the usability of such prediction models increases for future projects with unseen data. In this study, the feasibility of HCPCP and WPCP analyzed for open-source projects. HCPCP used feature-type Transfer Learning (TL) in this study. The analysis of HCPCP and CPCP is accomplished for open-source projects to enhance software quality such as maintainability, reliability, and robustness. Change prediction model developed using various Machine Learning (ML) techniques in this study. Performance of change prediction model analyzed using Area Under the Curve (AUC).

Comparative analysis of machine learning techniques for cryptocurrency price prediction

The emergence of cryptocurrencies has revolutionized the concept of digital currencies and attracted significant attention from financial markets. Predicting the price dynamics of cryptocurrencies is crucial but challenging due to their highly volatile and non-linear nature. This study compares the performance of various models in predicting cryptocurrency prices using three datasets: Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH). The models analyzed include Moving Average (MA), Logistic Regression (LR), Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM). The objective is to uncover underlying patterns in cryptocurrency price movements and identify the most accurate and reliable approach for predicting future prices. Through the analysis, it could be observed that MA, LR, and ARIMA models struggle to capture the actual trend accurately. In contrast, LSTM and CNN-LSTM models demonstrate strong fit to the actual price trend, with CNN-LSTM exhibiting a higher level of granularity in its predictions. Results suggest that deep learning architectures, particularly CNN-LSTM, show promise in capturing the complex dynamics of cryptocurrency prices. These findings contribute to the development of improved methodologies for cryptocurrency price prediction.

A Comparative Study of Machine Learning Techniques for College Student Success Prediction

The study aims to compare the performance of various machine learning models for student persistence prediction. The research starts with a historical review of student retention studies and the evolution of predictive models in the field. It highlights the importance of predicting student persistence for educational institutions and individuals. It then describes a dataset from ResearchGate, consisting of anonymized undergraduate student data collected between 2008 and 2018, with 37 features and 4,424 records. Ten machine learning algorithms are considered, with two popular machine learning algorithms, Logistic Regression, and Random Forest classification, being compared in more detail for their performance in predicting student persistence. Evaluation metrics such as prediction accuracy, precision, recall, and F1-score are used. Results show that the Random Forest model outperforms Logistic Regression in predicting student outcomes, particularly when using the synthetic minority oversampling technique (SMOTE) to address the class imbalance. Overall, this study contributes to student retention research and provides insights for developing targeted support measures to enhance student success in higher education.

Advanced model based machine learning technique for early stage prediction of ankylosing spondylitis under timely analysis with featured textures

Advanced model based machine learning technique for early stage prediction of ankylosing spondylitis under timely analysis with featured textures

Identification of Most Preferable Machine Learning Technique for Prediction of Bank Loan Defaulters

Identification of Most Preferable Machine Learning Technique for Prediction of Bank Loan Defaulters

Stock Market Prediction using Machine Learning

Stock market prediction has been a subject of significant interest and research for both financial analysts and machine learning practitioners. This abstract presents a concise overview of the key aspects and approaches in the realm of stock market prediction. The unpredictable and dynamic nature of financial markets poses a challenge for accurate forecasting. However, advancements in machine learning techniques, availability of large-scale financial data, and computational power have led computational to the development of sophisticated prediction models. In this endeavour, we investigate the application of various machine learning algorithms, including regression, time series models and support vector machine, to forecast stock prices. The research focuses on data preprocessing, feature engineering, and model evaluation to enhance prediction accuracy. Using a diverse dataset Evaluation metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) are utilized to measure model performance. While acknowledging the inherent uncertainty of financial markets, this research contributes to the broader dialogue on data-driven decision-making in investment and finance. The outcomes of this study offer insights into the strengths and limitations of machine learning techniques in stock price prediction.

Evaluation of machine learning techniques for heart disease prediction using multi-criteria decision making

Globally, heart disease is the primary cause of death. Early detection of this disease enables cardiologists to make more accurate judgments regarding the health of their patients. Due to machine learning’s ability to identify patterns in data, its use in the medical industry has increased. Many heart disease prediction models have been developed by various researchers utilizing machine learning techniques (MLTs). The performance of MLTs on heart disease prediction may vary for different accuracy measures. Thus, the choice of the appropriate machine-learning technique for heart disease prediction is a challenging task. This paper proposes a multi-criteria decision-making (MCDM)-based method to evaluate the MLTs for heart disease prediction considering various performance measures taken into account altogether. The proposed approach uses the concept of a combined compromise solution (CoCoSo)- an MCDM method. For validation of the proposed approach, an experimental study was conducted to evaluate the performance of fifteen machine learning techniques for predicting heart disease over three heart disease datasets considering six performance measures taken into account altogether. Results show that the logistic regression and support vector machine are recommended as the most suitable MLTs for heart disease prediction modeling with respect to six performance measures considered simultaneously.

Calculating Pairwise Similarity of Polymer Ensembles via Earth Mover's Distance.

Synthetic polymers, in contrast to small molecules and deterministic biomacromolecules, are typically ensembles composed of polymer chains with varying numbers, lengths, sequences, chemistry, and topologies. While numerous approaches exist for measuring pairwise similarity among small molecules and sequence-defined biomacromolecules, accurately determining the pairwise similarity between two polymer ensembles remains challenging. This work proposes the earth mover's distance (EMD) metric to calculate the pairwise similarity score between two polymer ensembles. EMD offers a greater resolution of chemical differences between polymer ensembles than the averaging method and provides a quantitative numeric value representing the pairwise similarity between polymer ensembles in alignment with chemical intuition. The EMD approach for assessing polymer similarity enhances the development of accurate chemical search algorithms within polymer databases and can improve machine learning techniques for polymer design, optimization, and property prediction.

Machine learning-assisted search for novel coagulants: When machine learning can be efficient even if data availability is low.

Design of new drugs is a challenging process: a candidate molecule should satisfy multiple conditions to act properly and make the least side-effect-perfect candidates selectively attach to and influence only targets, leaving off-targets intact. The amount of experimental data about various properties of molecules constantly grows, promoting data-driven approaches. However, the applicability of typical predictive machine learning techniques can be substantially limited by a lack of experimental data about a particular target. For example, there are many known Thrombin inhibitors (acting as anticoagulants), but a very limited number of known Protein C inhibitors (coagulants). In this study, we present our approach to suggest new inhibitor candidates by building an effective representation of chemical space. For this aim, we developed a deep learning model-autoencoder, trained on a large set of molecules in the SMILES format to map the chemical space. Further, we applied different sampling strategies to generate novel coagulant candidates. Symmetrically, we tested our approach on anticoagulant candidates, where we were able to predict their inhibition towards Thrombin. We also compare our approach with MegaMolBART-another deep learning generative model, but exploiting similar principles of navigation in a chemical space.