Machine Learning Techniques For Prediction Research Articles

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

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

Just-in-Time crash prediction for mobile apps

Just-In-Time (JIT) defect prediction aims to identify defects early, at commit time. Hence, developers can take precautions to avoid defects when the code changes are still fresh in their minds. However, the utility of JIT defect prediction has not been investigated in relation to crashes of mobile apps. We therefore conducted a multi-case study employing both quantitative and qualitative analysis. In the quantitative analysis, we used machine learning techniques for prediction. We collected 113 reliability-related metrics for about 30,000 commits from 14 Android apps and selected 14 important metrics for prediction. We found that both standard JIT metrics and static analysis warnings are important for JIT prediction of mobile app crashes. We further optimized prediction performance, comparing seven state-of-the-art defect prediction techniques with hyperparameter optimization. Our results showed that Random Forest is the best performing model with an AUC-ROC of 0.83. In our qualitative analysis, we manually analysed a sample of 642 commits and identified different types of changes that are common in crash-inducing commits. We explored whether different aspects of changes can be used as metrics in JIT models to improve prediction performance. We found these metrics improve the prediction performance significantly. Hence, we suggest considering static analysis warnings and Android-specific metrics to adapt standard JIT defect prediction models for a mobile context to predict crashes. Finally, we provide recommendations to bridge the gap between research and practice and point to opportunities for future research.

Machine Learning for Water Quality Index Forecasting

This study aims to forecast water quality in the Tumkur district, Karnataka state, India, to increase pollution levels. Various machine learning techniques, including support vector machines, regression trees, linear regression, and neural networks, are employed. The Water Quality Index (WQI) is determined using parameters such as total hardness, pH, alkalinity, turbidity, chloride, dissolved solids, and conductivity. The dataset is split into training and testing sets (80:20) to assess model performance. Support Vector Machines and Linear Regression outperform other models, achieving R2 values of 0.96 and 0.99 for training and testing, respectively. This research underscores the importance of advanced machine learning techniques for accurate water quality prediction, crucial for effective pollution reduction strategies in the region.

Exploring Machine Learning Techniques for Accurate Prediction of Methane Hydrate Formation Temperature in Brine: A Comparative Study

AbstractAccurate estimation of formation conditions plays a pivotal role in effectively managing various processes related to hydrates, including flow assurance, deep-water drilling, and hydrate-based technology development. The formation temperature of methane hydrates in the presence of brine greatly affects the efficacy and accuracy of these processes. This work presents a comprehensive and novel comparative analysis of nine distinct machine learning models for accurate prediction of formation temperatures of methane hydrate. This study investigated the application of major machine learning (ML) algorithms including multiple linear regression (MLR), long short-term memory (LSTM), radial basis function (RBF), support vector machine (SVM), artificial neural network (ANN), gradient boosting regression (GBR), gradient process regression (GPR), random forest (RF), and K-nearest neighbor (KNN). The model accuracy was validated against a large dataset comprising of over 1000 data points with diverse range of salt concentrations. In this regard, model accuracies were compared using several metrics including R2, ARD, and AARD. The experimental results exhibited KNN algorithm to be fast-converging, accurate, and consistent over the entire range of data points with an R2 score of 0.975 and AARD of 0.385%. The results enable efficient and accurate temperature estimation with ML algorithms for multiple hydrate-related processes.

Revolutionizing Traffic Management: Advanced Machine Learning Techniques for Accurate Traffic Flow Prediction

In modern urban¬¬¬¬¬ transportation systems, efficient traffic flow prediction is of paramount importance to optimize traffic management, reduce congestion, and improve the overall commuting experience. This research report examines the application of state-of-the-art machine learning algorithms to accurately predict traffic flow patterns. By leveraging historical traffic data, weather conditions, time of day, and various other relevant features, our proposed model exhibits significant predictive capabilities. We study the effectiveness of various machine learning techniques, such as neural networks, decision trees, and ensemble methods, in capturing the complex dynamics of traffic flows. Through extensive experiments and validation using real datasets, we demonstrate the superiority of our approach compared to traditional methods. Ultimately, this research will contribute to the further development of intelligent transportation systems, paving the way for more efficient and sustainable urban mobility solutions

Translating Test Responses to Images for Test-termination Prediction via Multiple Machine Learning Strategies

Failure diagnosis is a software-based data-driven procedure. Collecting an excessive amount of fail data not only increases the overall test cost but can also potentially reduce diagnostic resolution. Test-termination prediction is thus proposed to dynamically determine which failing test pattern to terminate testing, producing an amount of test data that is sufficient for an accurate diagnosis analysis. In this work, we describe a set of novel methods utilizing advanced machine learning techniques for efficient test-termination prediction. To implement this approach, we first generate images representing failing test responses from failure-log files. These images are then used to train a multi-layer convolutional neural network (CNN) incorporating a residual block. The trained CNN model leverages the images and known diagnostic results to determine the optimal test-termination strategy within the testing process, ensuring efficient and high-quality diagnosis. In addition to the integration of test response-to-image translation, our approach harnesses two cutting-edge learning strategies to enhance fail data and boost performance in subsequent tasks. The first strategy is transfer learning, which utilizes sample-label information from one circuit to guide the decision of whether to continue or stop testing for another circuit lacking labels. The second strategy involves the use of a generative deep model to generate fail data in the form of synthetic images. This technique increases the modeling effectiveness by expanding the volume of training samples. Experimental results conducted on actual failing chips and standard benchmarks validate that our proposed method surpasses existing approaches. Our method creates opportunities to harness the power of recent advances in machine learning for improving test and diagnosis efficiency.

Boosting enabled efficient machine learning technique for accurate prediction of crop yield towards precision agriculture

Due to the limited availability of natural resources, it is essential that agricultural productivity keep pace with population growth. Despite unfavorable weather circumstances, this project's major objective is to boost production. As a consequence of technological advancements in agriculture, precision farming as a way for enhancing crop yields is gaining appeal and becoming more prevalent. When it comes to predicting future data, machine learning employs a number of methods, including the creation of models and the acquisition of prediction rules based on past data. In this manuscript, we examine various techniques to machine learning, as well as an automated agricultural yield projection model based on selecting the most relevant features. For the purpose of selecting features, the Grey Level Co-occurrence Matrix method is utilised. For classification, we make use of the AdaBoost Decision Tree, Artificial Neural Network (ANN), and K-Nearest Neighbour (KNN) algorithms. The data set that was used in this study is simply a compilation of information about a variety of topics, including yield, pesticide use, rainfall, and average temperature. This data collection consists of 33 characteristics or qualities in total. The crops soya beans, maze, potato, rice, paddy, wheat, and sorghum are included in this data collection. This data collection was made possible through the collaboration of the Food and Agriculture Organisation (FAO) and the World Data Bank, both of which make their data available to the public. The AdaBoost decision tree has achieved the highest level of accuracy possible when used to anticipate agricultural yield. Both the accuracy rate and the recall rate are quite high at 99 percent.

Study on the use of different machine learning techniques for prediction of concrete properties from their mixture proportions with their deterministic and robust optimisation

The construction industry relies so heavily on concrete that it's crucial to precisely forecast and optimize the strength and workability of concrete mixtures, while reducing costs as much as possible. For this objective, this study tries to predict and optimize the compressive strength and workability (slump) of concrete by using deterministic and robust optimization approaches, so as to determine the optimum concrete mixture proportions, while minimizing cost. Specifically, strength and slump were predicted based on concrete mixture proportions with five different machine learning techniques—support vector machine (SVM), artificial neural network (ANN), fuzzy inference system (FIS), adaptive fuzzy inference system (ANIS), and genetic expression programming (GEP), based on a dataset comprising two hundred concrete mixtures, which has various levels of key ingredients, including cement, water, fine aggregate, coarse aggregate, and size of coarse aggregate, along with their associated measures of strength and workability. These ingredients were used as input parameters, while compressive strength and slump (representing workability) served as output parameters for each mix proportion. Experimental investigations were conducted on fifteen distinct concrete mixes to validate the performance of the five networks, finding that ANFIS can yield the best results both for training and validation. This study provides valuable insights for predicting concrete properties and optimizing concrete mixture proportions, thus helping to maximize strength and workability while minimizing costs.

Machine Learning Techniques for Gender Recognition and Age Prediction Using Digital Images of Human Dentition

In today's digital age, the utilization of digital medical imagery is on the rise, since the increased computational power that enables the development of more advanced machine learning techniques. We have implemented a system capable of determining the gender and estimating the age of individuals based on digital images of their teeth. Teeth, being a robust and relatively unchanging part of the human body, serve as an ideal source for such analysis. By examining Orthopantomogram images, our system systematically extracts gender and age-related information. Developed using the Python programming language, our application is particularly valuable in forensic contexts, where traditional prediction methods can be time-consuming, often taking days. In contrast, our model delivers results in under a minute. We employed the Random Forest (RF) Classifier algorithm to deploy the prediction model, as it effectively addresses the overfitting issue associated with growing input sample sizes. With this approach, we have achieved an impressive accuracy rate of 94% in gender and age prediction.

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.

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).