Features Of Dataset Research Articles

Machine-Learning Predictions of Cochlear Implant Functional Outcomes: A Systematic Review.

Cochlear implant (CI) user functional outcomes are challenging to predict because of the variability in individual anatomy, neural health, CI device characteristics, and linguistic and listening experience. Machine learning (ML) techniques are uniquely poised for this predictive challenge because they can analyze nonlinear interactions using large amounts of multidimensional data. The objective of this article is to systematically review the literature regarding ML models that predict functional CI outcomes, defined as sound perception and production. We analyze the potential strengths and weaknesses of various ML models, identify important features for favorable outcomes, and suggest potential future directions of ML applications for CI-related clinical and research purposes. We conducted a systematic literature search with Web of Science, Scopus, MEDLINE, EMBASE, CENTRAL, and CINAHL from the date of inception through September 2024. We included studies with ML models predicting a CI functional outcome, defined as those pertaining to sound perception and production, and excluded simulation studies and those involving patients without CIs. Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we extracted participant population, CI characteristics, ML model, and performance data. Sixteen studies examining 5058 pediatric and adult CI users (range: 4 to 2489) were included from an initial 1442 publications. Studies predicted heterogeneous outcome measures pertaining to sound production (5 studies), sound perception (12 studies), and language (2 studies). ML models use a variety of prediction features, including demographic, audiological, imaging, and subjective measures. Some studies highlighted predictors beyond traditional CI audiometric outcomes, such as anatomical and imaging characteristics (e.g., vestibulocochlear nerve area, brain regions unaffected by auditory deprivation), health system factors (e.g., wait time to referral), and patient-reported measures (e.g., dizziness and tinnitus questionnaires). Used ML models were tree-based, kernel-based, instance-based, probabilistic, or neural networks, with validation and test methods most commonly being k-fold cross-validation and train-test split. Various statistical measures were used to evaluate model performance, however, for studies reporting accuracy, the best-performing models for each study ranged from 71.0% to 98.83%. ML models demonstrate high predictive performance and illuminate factors that contribute to CI user functional outcomes. While many models showed favorable evaluation statistics, the majority were not adequately reported with regard to dataset characteristics, model creation, and validation. Furthermore, the extent of overfitting in these models is unclear and will likely result in poor generalization to new data. This suggests the need for more robust validation procedures and standardization in reporting, with the ultimate hope that the iterative improvement of these models will allow for their adoption as a future clinical tool.

Research on Mass Prediction of Maize Kernel Based on Machine Vision and Machine Learning Algorithm

The yield assessment process during maize harvesting is a necessary means to ensure farmers’ economic benefits and stable agricultural production. Predicting the mass of maize kernels is an important condition for yield detection. This study proposes a maize kernel mass prediction model based on machine vision and machine learning algorithms to determine whether the kernels are broken. By extracting the geometric features of maize kernels, a phenotypic feature dataset of maize kernels was constructed. Subsequently, popular machine learning algorithms were used to establish regression models for maize kernel mass, achieving quantitative prediction of maize kernel mass. The results indicate that the PLSR (Partial Least Squares Regression) and RF (Random Forest) algorithms are suitable for constructing mass prediction models for broken and unbroken kernels, respectively. The models established by the two algorithms achieved R-values of 0.941 and 0.925, respectively. Field trial results show that there is a strong linear relationship between the predicted maize kernel mass using the constructed model and the actual kernel mass. Therefore, this method can serve as an accurate, objective, and efficient detection method for maize yield.

Manufacturing Feature Recognition With a Sparse Voxel-Based Convolutional Neural Network

Abstract Automated manufacturing feature recognition is a crucial link between computer-aided design and manufacturing, facilitating process selection and other downstream tasks in computer-aided process planning. While various methods such as graph-based, rule-based, and neural networks have been proposed for automatic feature recognition, they suffer from poor scalability or computational inefficiency. Recently, voxel-based convolutional neural networks have shown promise in solving these challenges but incur a tradeoff between computational cost and feature resolution. This paper investigates a computationally efficient sparse voxel-based convolutional neural network for manufacturing feature recognition, specifically, an octree-based sparse voxel convolutional neural network. This model is trained on a large-scale manufacturing feature dataset, and its performance is compared to a voxel-based feature recognition model (FeatureNet). The results indicate that the octree-based model yields higher feature recognition accuracy (99.5% on the test dataset) with 44% lower graphics processing unit (GPU) memory consumption than a voxel-based model of comparable resolution. In addition, increasing the resolution of the octree-based model enables recognition of finer manufacturing features. These results indicate that a sparse voxel-based convolutional neural network is a computationally efficient deep learning model for manufacturing feature recognition to enable process planning automation. Moreover, the sparse voxel-based neural network demonstrated comparable performance to a boundary representation-based feature recognition neural network, achieving similar accuracy in single-feature recognition without having access to the exact 3D shape descriptors.

Instar identification and weight prediction of Ostrinia furnacalis (Guenée) larvae using machine learning.

The Asian corn borer, Ostrinia furnacalis (Guenée), emerges as a significant threat to maize cultivation, inflicting substantial damage upon the crops. Particularly, its larval stage represents a critical point characterised by significant economic consequences on maize yield. To manage the infestation of this pest effectively, timely and precise identification of its larval stages is required. Currently, the absence of techniques capable of addressing this urgent need poses a formidable challenge to agricultural practitioners. To mitigate this issue, the current study aims to establish models conducive to the identification of larval stages. Furthermore, this study aims to devise predictive models for estimating larval weights, thereby enhancing the precision and efficacy of pest management strategies. For this, 9 classification and 11 regression models were established using four feature datasets based on the following features geometry, colour, and texture. Effectiveness of the models was determined by comparing metrics such as accuracy, precision, recall, F1-score, coefficient of determination, root mean squared error, mean absolute error, and mean absolute percentage error. Furthermore, Shapley Additive exPlanations analysis was employed to analyse the importance of features. Our results revealed that for instar identification, the DecisionTreeClassifier model exhibited the best performance with an accuracy of 84%. For larval weight, the SupportVectorRegressor model performed best with R2 of 0.9742. Overall, these findings present a novel and accurate approach to identify instar and predict the weight of O. furnacalis larvae, offering valuable insights for the implementation of management strategies against this key pest.

A data-informed knowledge discovery framework to predict fatigue properties of additively manufactured Ti–6Al–4V, IN718 and AlSi10Mg alloys using fatigue databases

Abstract With the increasing interest in utilizing additive manufacturing (AM) for fatigue-critical applications, reliable fatigue analysis tools are increasingly necessary. Unlike traditional manufacturing processes, AM involves a greater number of process parameters and manufacturing variables which adds complexity to the development of mechanistic fatigue models. Data-driven models, to some extent, have shown predictive capabilities for the fatigue properties of AM alloys. However, no study has extensively investigated fatigue properties across different AM alloys with the aim of developing predictive models for fatigue databases. A data-informed knowledge discovery framework is developed for exploring diverse AM fatigue datasets in the literature. Characteristics of the AM fatigue datasets are first analyzed within this framework to uncover data-driven insights for machine learning modeling. Machine learning techniques are then used to solve classification and regression problems using fatigue ratio as the property of interest. k-nearest neighbors and support vector machine classifiers are used to classify process parameters and manufacturing variables to determine the class of fatigue ratio they belong to based on a pre-defined threshold. Similarly, a Gaussian process regression (GPR) model is used to predict the fatigue ratio of AM alloys with a 95% prediction interval. The Shapley Additive exPlanations (SHAP) method is used to explain the predictions of the GPR model. This framework is validated using Ti–6Al–4V, IN718, and AlSi10Mg datasets from the literature. The proposed framework efficiently maps process parameters, post-processing sequences, and AM types to the fatigue ratio, thereby providing an efficient surrogate modeling methodology for estimating fatigue properties from available datasets.

Long-Term Forecasting of Solar Irradiation in Riyadh, Saudi Arabia, Using Machine Learning Techniques

Forecasting of time series data presents some challenges because the data’s nature is complex and therefore difficult to accurately forecast. This study presents the design and development of a novel forecasting system that integrates efficient data processing techniques with advanced machine learning algorithms to improve time series forecasting across the sustainability domain. Specifically, this study focuses on solar irradiation forecasting in Riyadh, Saudi Arabia. Efficient and accurate forecasts of solar irradiation are important for optimizing power production and its smooth integration into the utility grid. This advancement supports Saudi Arabia in Vision 2030, which aims to generate and utilize renewable energy sources to drive sustainable development. Therefore, the proposed forecasting system has been developed to the parameters characteristic of the Riyadh region environment, including high solar intensity, dust storms, and unpredictable weather conditions. After the cleaning and filtering process, the filtered dataset was pre-processed using the standardization method. Then, the Discrete Wavelet Transform (DWT) technique has been applied to extract the features of the pre-processed data. Next, the extracted features of the solar dataset have been split into three subsets: train, test, and forecast. Finally, two different machine learning techniques have been utilized for the forecasting process: Support Vector Machine (SVM) and Gaussian Process (GP) techniques. The proposed forecasting system has been evaluated across different time horizons: one-day, five-day, ten-day, and fifteen-day ahead. Comprehensive evaluation metrics were calculated including accuracy, stability, and generalizability measures. The study outcomes present the proposed forecasting system which provides a more robust and adaptable solution for time-series long-term forecasting and complex patterns of solar irradiation in Riyadh, Saudi Arabia.

Accurate prediction of nucleic acid binding proteins using protein language model

Abstract Motivation Nucleic acid binding proteins (NABPs) play critical roles in various and essential biological processes. Many machine learning-based methods have been developed to predict different types of NABPs. However, most of these studies have limited applications in predicting the types of NABPs for any given protein with unknown functions, due to several factors such as dataset construction, prediction scope and features used for training and testing. In addition, single-stranded DNA binding proteins (SSBs) have not been extensively investigated for identifying novel SSBs from proteins with unknown functions. Results To improve prediction accuracy of different types of NABPs for any given protein, we developed hierarchical and multi-class models with machine learning-based methods and a feature extracted from protein language model ESM2. Our results show that by combining the feature from ESM2 and machine learning methods, we can achieve high prediction accuracy up to 95% for each stage in the hierarchical approach, and 85% for overall prediction accuracy from the multi-class approach. More importantly, besides the much improved prediction of other types of NABPs, the models can be used to accurately predict single-stranded DNA binding proteins, which is underexplored.

Curating global datasets of structural linguistic features for independence

The increasing availability of cross-linguistic databases dedicated to documenting morphosyntactic, lexical and phonological features has proliferated the use of such data for studies on language evolution and human history. However, most of these databases were not designed to ensure independence of features, such that it is not valid to jointly use all their features in large-scale statistical analyses assuming independence of inputs. Here, we curate published data from five large linguistic databases to generate two global-scale cross-linguistic datasets: GBI (from the Grambank dataset), and TLI (using inputs from the World Atlas of Language Structures, AUTOTYP, PHOIBLE and Lexibank). The datasets minimize logical dependencies of features and forms of strong statistical dependencies that go beyond phylogenetic and geographical signal. They are also made available in densified form, reducing the proportion of missing data. We document our curation principles and workflows to ensure reusability of this framework with other inputs or thresholds of independence. Our curation steps on both datasets reveal robust and comparable global patterns of structural linguistic diversity.

Reliably quantifying the severity of social symptoms in children with autism using ASDSpeech

Several studies have demonstrated that the severity of social communication problems, a core symptom of Autism Spectrum Disorder (ASD), is correlated with specific speech characteristics of ASD individuals. This suggests that it may be possible to develop speech analysis algorithms that can quantify ASD symptom severity from speech recordings in a direct and objective manner. Here we demonstrate the utility of a new open-source AI algorithm, ASDSpeech, which can analyze speech recordings of ASD children and reliably quantify their social communication difficulties across multiple developmental timepoints. The algorithm was trained and tested on the largest ASD speech dataset available to date, which contained 99,193 vocalizations from 197 ASD children recorded in 258 Autism Diagnostic Observation Schedule, Second edition (ADOS-2) assessments. ASDSpeech was trained with acoustic and conversational features extracted from the speech recordings of 136 children, who participated in a single ADOS-2 assessment, and tested with independent recordings of 61 additional children who completed two ADOS-2 assessments, separated by 1–2 years. Estimated total ADOS-2 scores in the test set were significantly correlated with actual scores when examining either the first (r(59) = 0.544, P < 0.0001) or second (r(59) = 0.605, P < 0.0001) assessment. Separate estimation of social communication and restricted and repetitive behavior symptoms revealed that ASDSpeech was particularly accurate at estimating social communication symptoms (i.e., ADOS-2 social affect scores). These results demonstrate the potential utility of ASDSpeech for enhancing basic and clinical ASD research as well as clinical management. We openly share both algorithm and speech feature dataset for use and further development by the community.

Monitoring the Maize Canopy Chlorophyll Content Using Discrete Wavelet Transform Combined with RGB Feature Fusion

To evaluate the accuracy of Discrete Wavelet Transform (DWT) in monitoring the chlorophyll (CHL) content of maize canopies based on RGB images, a field experiment was conducted in 2023. Images of maize canopies during the jointing, tasseling, and grouting stages were captured using unmanned aerial vehicle (UAV) remote sensing to extract color, texture, and wavelet features and to construct a color and texture feature dataset and a fusion of wavelet, color, and texture feature datasets. Backpropagation neural network (BP), Stacked Ensemble Learning (SEL), and Gradient Boosting Decision Tree (GBDT) models were employed to develop CHL monitoring models for the maize canopy. The performance of these models was evaluated by comparing their predictions with measured CHL data. The results indicate that the dataset integrating wavelet features achieved higher monitoring accuracy compared to the color and texture feature dataset. Specifically, for the integrated dataset, the BP model achieved an R2 value of 0.728, an RMSE of 3.911, and an NRMSE of 15.24%; the SEL model achieved an R2 value of 0.792, an RMSE of 3.319, and an NRMSE of 15.34%; and the GBDT model achieved an R2 value of 0.756, an RMSE of 3.730, and an NRMSE of 15.45%. Among these, the SEL model exhibited the highest monitoring accuracy. This study provides a fast and reliable method for monitoring maize growth in field conditions. Future research could incorporate cross-validation with hyperspectral and thermal infrared sensors to further enhance model reliability and expand its applicability.

Icelandic translation, adaptation and validation of the Parental Burnout Assessment (PBA-IS)

Background As parental burnout is increasingly recognised for its severe impact on parents and children, identifying factors that exacerbate or alleviate this condition is crucial. Reliable assessment tools in clinical settings are essential to detect those at risk of or experiencing burnout, enabling timely intervention. Aims/objectives This study aims to adapt the Parental Burnout Assessment for use in Iceland and evaluate its psychometric properties while exploring how personal and socio-demographic factors influence parental burnout. Materials and methods A sample of 1,110 parents participated. Descriptive statistics analysed the main dataset characteristics, and confirmatory factor analysis evaluated the psychometric properties of the adapted version. Results Satisfactory structural validity and internal consistency (α 0.96) of the PBA-IS was demonstrated. Factors influencing parental burnout included marital status, number of children, perceived support, and personal causation. Conclusions The PBA-IS is a valid and reliable translated tool for assessing parental burnout in Iceland. Personal causation, a key concept in occupational therapy, appears pivotal in parental burnout. Occupational therapists can provide holistic support to help parents effectively manage stress. Significance The PBA-IS enables parental burnout to be identified in Icelandic clinical settings, supporting early interventions that reduce stress, promote mental health, and enhance well-being.

Exploring Metaheuristic Optimization Algorithms in the Context of Textual Cyberharassment: A Systematic Review

ABSTRACTThe digital landscape and rapid advancement of Information and Communication Technology have significantly increased social interactions, but it has also led to a rise in harmful behaviours such as offensive language, cyberbullying, and HS. Addressing online harassment is critical due to its severe consequences. This study offers a comprehensive evaluation of existing studies that employed metaheuristic optimization algorithms for detecting textual harassment content across social media platforms, highlighting their strengths and limitations. Using the PRISMA methodology, we reviewed and analysed 271 research papers, ultimately narrowing down the selection to 36 papers based on specific inclusion and exclusion criteria. By analysing key factors such as optimization techniques, feature engineering strategies, and dataset characteristics, we identify crucial trends and challenges in the field. Finally, we offer practical recommendations to improve the accuracy of predictive models, including adopting hybrid approaches, enhancing multilingual capabilities, and expanding models to operate effectively across various social media platforms.

ADMET evaluation in drug discovery: 21. Application and industrial validation of machine learning algorithms for Caco-2 permeability prediction

The Caco-2 cell model has been widely used to assess the intestinal permeability of drug candidates in vitro, owing to its morphological and functional similarity to human enterocytes. While Caco-2 cell assay is considered safe and cost-effective, it is also characterized by being time-consuming. Therefore, computational models that achieve high accuracies in predicting Caco-2 permeability are crucial for enhancing the efficiency of oral drug development. In this study, we conducted an in-depth analysis of the characteristics of an augmented Caco-2 permeability dataset, and evaluated a diverse range of machine learning algorithms in combination with different molecular representations. The results indicated that XGBoost generally provided better predictions than comparable models for the test sets. In addition, we investigated the transferability of machine learning models trained on publicly available data to internal pharmaceutical industry datasets. Our findings, based on the Shanghai Qilu’s in-house dataset, showed that the boosting models retained a degree of predictive efficacy when applied to industry data. Furthermore, Y-randomization test and applicability domain analysis were employed to assess the robustness and generalizability of these models. Matched Molecular Pair Analysis (MMPA) was utilized to extract chemical transformation rules. We believe that the model developed in this study could represent a reliable tool for assessing Caco-2 permeability during early-stage drug discovery and the chemical transformation rules derived here could provide insights for optimizing Caco-2 permeability.Scientific contributionA comprehensive validation of various machine learning algorithms combined with diverse molecular representations on a large dataset for predicting Caco-2 permeability was reported. The transferability of machine learning models trained on publicly available data to internal pharmaceutical industry datasets was also investigated. Matched molecular pair analysis was carried out to provide reasonable suggestions for researchers to improve the Caco-2 permeability of compounds.Graphical

Systematic review of computational techniques, dataset utilization, and feature extraction in electrocardiographic imaging.

This study aimed to analyze computational techniques in ECG imaging (ECGI) reconstruction, focusing on dataset identification, problem-solving, and feature extraction. We employed a PRISMA approach to review studies from Scopus and Web of Science, applying Cochrane principles to assess risk of bias. The selection was limited to English peer-reviewed papers published from 2010 to 2023, excluding studies that lacked computational technique descriptions. From 99 reviewed papers, trends show a preference for traditional methods like the boundary element and Tikhonov methods, alongside a rising use of advanced technologies including hybrid techniques and deep learning. These advancements have enhanced cardiac diagnosis and treatment precision. Our findings underscore the need for robust data utilization and innovative computational integration in ECGI, highlighting promising areas for future research and advances. This shift toward tailored cardiac care suggests significant progress in diagnostic and treatment methods.

A comprehensive study on the interplay between dataset characteristics and oversampling methods

Addressing class imbalance in oversampling domain using machine learning methods requires careful selection of techniques and classifiers for optimal outcomes. While the importance of technique choice is well recognized, research on how dataset characteristics affect classification results remained limited. This study fills this gap by analyzing 16 datasets, categorized by financial relevance, temporal relevance, minority rate, minority sample count, and feature count. The effectiveness of various oversampling techniques is systematically evaluated and ranked using F1 and AUC, providing a structured framework for assessing the suitability of these techniques across diverse datasets. The evaluation involved 15 classifiers, resulting in 75 models combining four oversampling techniques and a baseline classifier. A ranking mechanism identified five top-performing models, emphasizing that classifier performance is influenced by the choice of the oversampling method, depending on dataset type. Notably, the traditional Synthetic Minority Oversampling Technique (SMOTE) outperformed the approaches based on Generative Adversarial Network (GAN) across different classifiers and datasets. Among classifiers, random forest proved to be the most robust across all dataset types, surpassing boosting-based classifiers. Overall, this study provides valuable insights into selecting the optimal oversampling methods and classifiers for specific dataset characteristics, offering a framework for addressing class imbalance in various contexts.

Fostering employment match? Graduates’ internships and early professional experiences between individuals’ choices and institutional constraints

PurposeIn Italy, internships were introduced in higher education to ease graduates’ entry into the labor market, addressing youth unemployment and overeducation attributed to low job-specific skills. We investigate whether internships during and after the degree program improve employment quality, specifically in terms of coherence between the job obtained and the educational-level achieved.Design/methodology/approachWe employ both a longitudinal approach and Propensity Score Matching (PSM) to examine the associations between curricular and extracurricular internships, and the number of job episodes after graduation, with the risk of overeducation among master’s graduates.FindingsThe findings suggest that curricular internships, if at all, do not affect the likelihood of obtaining a matching qualified job. Extracurricular internships are instead associated with a higher risk of overeducation. Additionally, the risk of overeducation does not decrease with more job episodes and even rises in the South, where opportunities are scarcer. We conclude that internships may help secure employment but at the cost of overeducation for graduates.Originality/valueThis study fills a gap by analyzing the impact of both curricular and extracurricular internships on employment quality in terms of occupational coherence. In doing so, it assesses policies on graduates' professional experiences over the last 20 years. By employing both longitudinal analysis and PSM quasi-experimental models, it aims to address selection bias related to unobserved characteristics in observational datasets and panel attrition. Theoretically, it advances by analyzing the determinants of overeducation while accounting for territorial differences in labor demand, and better assessing the contrasting predictions of human capital vs credentialist theories.

Constructing a metadata knowledge graph as an atlas for demystifying AI pipeline optimization.

The emergence of advanced artificial intelligence (AI) models has driven the development of frameworks and approaches that focus on automating model training and hyperparameter tuning of end-to-end AI pipelines. However, other crucial stages of these pipelines such as dataset selection, feature engineering, and model optimization for deployment have received less attention. Improving efficiency of end-to-end AI pipelines requires metadata of past executions of AI pipelines and all their stages. Regenerating metadata history by re-executing existing AI pipelines is computationally challenging and impractical. To address this issue, we propose to source AI pipeline metadata from open-source platforms such as Papers-with-Code, OpenML, and Hugging Face. However, integrating and unifying the varying terminologies and data formats from these diverse sources is a challenge. In this study, we present a solution by introducing Common Metadata Ontology (CMO) which is used to construct an extensive AI Pipeline Metadata Knowledge Graph (AIMKG) consisting of 1.6 million pipelines. Through semantic enhancements, the pipeline metadata in AIMKG is also enriched for downstream tasks such as search and recommendation of AI pipelines. We perform quantitative and qualitative evaluations on AIMKG to search and recommend relevant pipelines to user query. For quantitative evaluation, we propose a custom aggregation model that outperforms other baselines by achieving a retrieval accuracy (R@1) of 76.3%. Our qualitative analysis shows that AIMKG-based recommender retrieved relevant pipelines in 78% of test cases compared to the state-of-the-art MLSchema-based recommender which retrieved relevant responses in 51% of the cases. AIMKG serves as an atlas for navigating the evolving AI landscape, providing practitioners with a comprehensive factsheet for their applications. It guides AI pipeline optimization, offers insights and recommendations for improving AI pipelines, and serves as a foundation for data mining and analysis of evolving AI workflows.

Movement Disorders and Smart Wrist Devices: A Comprehensive Study.

In the medical field, there are several very different movement disorders, such as tremors, Parkinson's disease, or Huntington's disease. A wide range of motor and non-motor symptoms characterizes them. It is evident that in the modern era, the use of smart wrist devices, such as smartwatches, wristbands, and smart bracelets is spreading among all categories of people. This diffusion is justified by the limited costs, ease of use, and less invasiveness (and consequently greater acceptability) than other types of sensors used for health status monitoring. This systematic review aims to synthesize research studies using smart wrist devices for a specific class of movement disorders. Following PRISMA-S guidelines, 130 studies were selected and analyzed. For each selected study, information is provided relating to the smartwatch/wristband/bracelet model used (whether it is commercial or not), the number of end-users involved in the experimentation stage, and finally the characteristics of the benchmark dataset possibly used for testing. Moreover, some articles also reported the type of raw data extracted from the smart wrist device, the implemented designed algorithmic pipeline, and the data classification methodology. It turned out that most of the studies have been published in the last ten years, showing a growing interest in the scientific community. The selected articles mainly investigate the relationship between smart wrist devices and Parkinson's disease. Epilepsy and seizure detection are also research topics of interest, while there are few papers analyzing gait disorders, Huntington's Disease, ataxia, or Tourette Syndrome. However, the results of this review highlight the difficulties still present in the use of the smartwatch/wristband/bracelet for the identified categories of movement disorders, despite the advantages these technologies could bring in the dissemination of low-cost solutions usable directly within living environments and without the need for caregivers or medical personnel.

Cp3-bench: a tool for benchmarking symbolic regression algorithms demonstrated with cosmology

We introduce cp3-bench, a tool for comparing/benching symbolic regression algorithms, which we make publicly available at https://github.com/CP3-Origins/cp3-bench. In its current format, cp3-bench includes 12 different symbolic regression algorithms which can be automatically installed as part of cp3-bench. The philosophy behind cp3-bench is that is should be as user-friendly as possible, available in a ready-to-use format, and allow for easy additions of new algorithms and datasets. Our hope is that users of symbolic regression algorithms can use cp3-bench to easily install and compare/bench an array of symbolic regression algorithms to better decide which algorithms to use for their specific tasks at hand.To introduce and motivate the use of cp3-bench we present a small benchmark of 12 symbolic regression algorithms applied to 28 datasets representing six different cosmological and astroparticle physics setups. Overall, we find that most of the benched algorithms do rather poorly in the benchmark and suggest possible ways to proceed with developing algorithms that will be better at identifying ground truth expressions for cosmological and astroparticle physics datasets. Our demonstration benchmark specifically studies the significance of dimensionality of the feature space and precision of datasets. We find both to be highly important for symbolic regression tasks to be successful. On the other hand, we find no indication that inter-dependence of features in datasets is particularly important, meaning that it is not in general a hindrance for symbolic regression algorithms if datasets e.g. contain both z and H(z) as features. Lastly, we note that we find no indication that performance of algorithms on standardized datasets are good indicators of performance on particular cosmological and astrophysical datasets. This suggests that it is not necessarily prudent to choose symbolic regression algorithms based on their performance on standardized data. Instead, a more robust approach is to consider a variety of algorithms, chosen based on the particular task at hand that one wishes to apply symbolic regression to.

Comprehensive Analysis of Stock Price Dynamics Using Ensemble Machine Learning Models for Enhanced Prediction Accuracy

Stock price prediction is an important component of the financial analysis because the results influence the increase in economic growth and investment. This work aims to develop an ensemble SL technique that consists of mainly PCA, PSO, and SVM to achieve better prediction. Hence, through PCA, large numbers of stocked data dimensions are compressed without compromising on the crucial feature of data set. The problem of parameter selection for non-linear datasets is handled by using a bio-inspired optimization technique known as PSO in order to optimize the SVM hyperparameters. As the core accurate predictor model, the SVM employs the Radial Basis Function to provide the substantial regression capacity for sophisticated financial data sets. The ensemble framework was used with actual stock price data and the information set into training and testing sets. The acknowledgement of probable manifold values indicated that the proposed approach is more accurate than conventional approaches, with an accuracy rate of 95.5 %, when benchmarked using RMSE or MAE. In particular, the forecasts of stock prices by integrating PCA for feature reduction and PSO for parameter tuning with SVM regression is a notable improvement. The proposed methodology can be easily applied to scale for financial analytics since it manages to solve for the issues of noisy and non-linear high dimensional data.