Applying Machine Learning Models Research Articles

Global census of the significance of giant mesopelagic protists to the marine carbon and silicon cycles

Thriving in both epipelagic and mesopelagic layers, Rhizaria are biomineralizing protists, mixotrophs or flux-feeders, often reaching gigantic sizes. In situ imaging showed their contribution to oceanic carbon stock, but left their contribution to element cycling unquantified. Here, we compile a global dataset of 167,551 Underwater Vision Profiler 5 Rhizaria images, and apply machine learning models to predict their organic carbon and biogenic silica biomasses in the uppermost 1000 m. We estimate that Rhizaria represent up to 1.7% of mesozooplankton carbon biomass in the top 500 m. Rhizaria biomass, dominated by Phaeodaria, is more than twice as high in the mesopelagic than in the epipelagic layer. Globally, the carbon demand of mesopelagic, flux-feeding Phaeodaria reaches 0.46 Pg C y−1, representing 3.8 to 9.2% of gravitational carbon export. Furthermore, we show that Rhizaria are a unique source of biogenic silica production in the mesopelagic layer, where no other silicifiers are present. Our global census further highlights the importance of Rhizaria for ocean biogeochemistry.

Research on applying machine learning models to predict and assess return on assets (ROA)

Research on applying machine learning models to predict and assess return on assets (ROA)

Predict Unknown Properties of Elements of Periodic Table with Machine Learning

The periodic table of elements includes 92 elements with many unknown properties like melting point, boiling point, heat of vaporization, and molar heat capacity of some specific elements such as Curium, Berkelium, Californium, and Einsteinium. Physicists, chemists, and other scientists have done many successful experiments to predict these mysterious features using the first principal methods. But still many properties have been unclear. In this project we apply Machine Learning models such as linear and logistic regression to predict this unknow values. The known values split to train and test data to find and confirm the model. Then the model will be run over unknown variables.

A Study on Improving Sleep Apnea Diagnoses Using Machine Learning Based on the STOP-BANG Questionnaire

Sleep apnea has emerged as a significant health issue in modern society, with self-diagnosis and effective management becoming increasingly important. Among the most renowned methods for self-diagnosis, the STOP-BANG questionnaire is widely recognized as a simple yet effective tool for diagnosing and assessing the risk of sleep apnea. However, its sensitivity and specificity have limitations, necessitating the need for tools with higher performance. Consequently, this study aimed to enhance the accuracy of sleep apnea diagnoses by integrating machine learning with the STOP-BANG questionnaire. Research through actual cases was conducted based on the data of 262 patients undergoing polysomnography, confirming sleep apnea with a STOP-BANG score of ≥3 and an Apnea–Hypopnea Index (AHI) of ≥5. The accuracy, sensitivity, and specificity were derived by comparing Apnea–Hypopnea Index scores with STOP-BANG scores. When applying machine learning models, four hyperparameter-tuned models were utilized: K-Nearest Neighbor (K-NN), Logistic Regression, Random Forest, and Support Vector Machine (SVM). Among them, the K-NN model with a K value of 11 demonstrated superior performance, achieving a sensitivity of 0.94, specificity of 0.85, and overall accuracy of 0.92. These results highlight the potential of combining traditional STOP-BANG diagnostic tools with machine learning technology, offering new directions for future research in self-diagnosis and the preliminary diagnosis of sleep-related disorders in clinical settings.

Pangenomes at the limits of evolution

Evolutionary pathways can be random or deterministic. In a recent article, Beavan et al. investigate this balance by applying machine learning models to microbial pangenomes. The presence of almost one-third of genes can be reliably inferred, indicating a surprising amount of predictable evolution.

Max Explainability Score–A quantitative metric for explainability evaluation in knowledge graph-based recommendations

Applying machine learning (ML) models within recommender systems (RSs) has proven effective in achieving high recommendation accuracy. However, a prevalent drawback is their inherent lack of explicability. Integrating knowledge graphs (KGs) into RSs enhances interpretability by elucidating the reasoning behind specific recommendations. Explainability evaluation in RSs often relies on subjective metrics assessed using qualitative user feedback. Although they offer a robust foundation for evaluating explainability, they are susceptible to confirmation bias. Quantitative metrics for assessing the explainability of KG-driven RSs are currently unavailable. This study, thus, proposes novel metrics for the quantitative evaluation of explanation quality in KG-based RSs. The proposed metric, Max Explainability Score, is based on four evaluation parameters: the number of rules, the probability of the traversal path, the entropy value and the reward due to the chosen traversal paths. User studies employing these metrics offer enhanced validity and better integration potential with future XAI research.

Estimating millimeter-scale surface roughness of rock outcrops using drone-flyover structure-from-motion (SfM) photogrammetry by applying machine learning model

Estimating millimeter-scale surface roughness of rock outcrops using drone-flyover structure-from-motion (SfM) photogrammetry by applying machine learning model

Novel COVID-19 biomarkers identified through multi-omics data analysis: N-acetyl-4-O-acetylneuraminic acid, N-acetyl-L-alanine, N-acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate.

This study aims to apply machine learning models to identify new biomarkers associated with the early diagnosis and prognosis of SARS-CoV-2 infection.Plasma and serum samples from COVID-19 patients (mild, moderate, and severe), patients with other pneumonia (but with negative COVID-19 RT-PCR), and healthy volunteers (control) from hospitals in four different countries (China, Spain, France, and Italy) were analyzed by GC-MS, LC-MS, and NMR. Machine learning models (PCA and PLS-DA) were developed to predict the diagnosis and prognosis of COVID-19 and identify biomarkers associated with these outcomes.A total of 1410 patient samples were analyzed. The PLS-DA model presented a diagnostic and prognostic accuracy of around 95% of all analyzed data. A total of 23 biomarkers (e.g., spermidine, taurine, L-aspartic, L-glutamic, L-phenylalanine and xanthine, ornithine, and ribothimidine) have been identified as being associated with the diagnosis and prognosis of COVID-19. Additionally, we also identified for the first time five new biomarkers (N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-L-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate) that are also associated with the severity and diagnosis of COVID-19. These five new biomarkers were elevated in severe COVID-19 patients compared to patients with mild disease or healthy volunteers.The PLS-DA model was able to predict the diagnosis and prognosis of COVID-19 around 95%. Additionally, our investigation pinpointed five novel potential biomarkers linked to the diagnosis and prognosis of COVID-19: N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-L-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate. These biomarkers exhibited heightened levels in severe COVID-19 patients compared to those with mild COVID-19 or healthy volunteers.

Survey Paper on A Machine Learning Approach for Multiple Disease Prediction

Machine learning techniques have revolutionized the field of healthcare by enabling accurate and timely disease prediction. The ability to predict multiple diseases simultaneously can significantly improve early diagnosis and treatment, leading to better patient outcomes and reduced healthcare costs. This research paper explores the application of machine learning algorithms in predicting multiple diseases, focusing on their benefits, challenges, and future directions. We present an overview of various machine learning models and data sources commonly used for disease prediction. Additionally, we discuss the importance of feature selection, model evaluation, and the integration of multiple data modalities for enhanced disease prediction. The research findings highlight the potential of machine learning in multi- disease prediction and its potential impact on public health. Once more, I am applying machine learning model to identify that a person is affected with few disease or not. This training model takes a sample data and train itself for predicting disease. Key Words: Disease Prediction, Na ̈ıve Bayesian Networks, Random Forest, Decision Tree, Feature , Selection KNN.

STARdbi: A pipeline and database for insect monitoring based on automated image analysis

Insects are highly abundant and diverse, and play major roles in ecosystem functions. Monitoring of insect populations is key to their sustainable management. However, the labor and expertise needed to identify insects, and the challenges of archiving the wealth of data collected in monitoring programs, often limit these efforts. We describe a pipeline to reduce the barriers associated with curating and mining big data of insect biodiversity. The pipeline, STARdbi, includes capturing flying insects with sticky traps, scanning the traps, storing the trap-images in a public database with a web-based interface, and applying machine learning models to extract information from the images. To illustrate the insights that can be gained from STARdbi, we describe two case studies. One of them involves monitoring of circadian activity patterns of grain pests and of their natural enemies, and the other compares insect abundance, biomass and size distributions between agricultural and semi-natural habitats. We invite the community of insect ecologists to contribute to the STARdbi database, and to use its image analysis tools to address diverse ecological and evolutionary questions.

Evaluation of Machine Learning Methods for Prediction Student Performance

Abstract: Significant findings and useful insights have emerged from the study of using machine learning techniques to predict student performance. used large datasets with a wide variety of demographic, socioeconomic, and academic performance data to conduct in-depth evaluations of several machine learning methods. Our study highlighted the importance of careful data preprocessing, which involves fundamental steps like classifying student performance and doing in-depth exploratory data analysis (EDA). To ensure the validity of our model assessments, we meticulously split the dataset into training and testing subsets. To effectively apply machine learning models, it is necessary to convert categorical data into numerical representations; we did this by using strategic label encoding and one-hot encoding techniques. Metrics on the effectiveness of the recommended machine learning models are presented, with a spotlight on the XG Boost algorithm's remarkable 97% accuracy. The MLP's performance indicators are as follows: 95% accuracy, 98% recall, 95% precision, and 96% F1 score. When compared to these, ADA Boost has a precision of 83%, recall of 90%, F1 score of 86%, and accuracy rate of 81%. XGBoost, on the other hand, claims rates of 98% precision, 98% recall, 98% F1 score, and 97% accuracy, making it stand out from the crowd. Based on these findings, the XG Boost algorithm is the best option among the machine learning models considered for this research.

Dynamic Malware Classification and API Categorisation of Windows Portable Executable Files Using Machine Learning

The rise of malware attacks presents a significant cyber-security challenge, with advanced techniques and offline command-and-control (C2) servers causing disruptions and financial losses. This paper proposes a methodology for dynamic malware analysis and classification using a malware Portable Executable (PE) file from the MalwareBazaar repository. It suggests effective strategies to mitigate the impact of evolving malware threats. For this purpose, a five-level approach for data management and experiments was utilised: (1) generation of a customised dataset by analysing a total of 582 malware and 438 goodware samples from Windows PE files; (2) feature extraction and feature scoring based on Chi2 and Gini importance; (3) empirical evaluation of six state-of-the-art baseline machine learning (ML) models, including Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), XGBoost (XGB), and K-Nearest Neighbour (KNN), with the curated dataset; (4) malware family classification using VirusTotal APIs; and, finally, (5) categorisation of 23 distinct APIs from 266 malware APIs. According to the results, Gini’s method takes a holistic view of feature scoring, considering a wider range of API activities. The RF achieved the highest precision of 0.99, accuracy of 0.96, area under the curve (AUC) of 0.98, and F1-score of 0.96, with a 0.93 true-positive rate (TPR) and 0.0098 false-positive rate (FPR), among all applied ML models. The results show that Trojans (27%) and ransomware (22%) are the most risky among 11 malware families. Windows-based APIs (22%), the file system (12%), and registry manipulation (8.2%) showcased their importance in detecting malicious activity in API categorisation. This paper considers a dual approach for feature reduction and scoring, resulting in an improved F1-score (2%), and the inclusion of AUC and specificity metrics distinguishes it from existing research (Section Comparative Analysis with Existing Approaches). The newly generated dataset is publicly available in the GitHub repository (Data Availability Statement) to facilitate aspirant researchers’ dynamic malware analysis.

SENTIMENT ANALYSIS OF WEB 3.0 ENABLED TWITTER DATASET

In this paper, the focus is on sentiment analysis of web 3.0 enabled twitter dataset. The objective of the project is to explore various methods for performing sentiment analysis on Twitter datasets and implementing these methods on the web3.0 Twitter platform. The project involves collecting Twitter data through blockchain-based applications, preprocessing the data to remove noise, and applying machine learning models for sentiment analysis. Sentiment analysis is simply the extraction of thoughts, ideas, opinions, and emotions from sources such as text, speech, tweets, and databases using natural language processing (NLP) This process involves text segmentation mentally makes it "good," "bad," and "neutral" groups. In addition, it is known by other terms such as objective evaluation, mindfulness mining, and rating extraction. Web 3.0, also known as Web3, represents the third contemplated iteration of the World Wide Web, which aspires to establish a connected, transparent and intelligent online environment Based on the concept of decentralization, blockchain technology and the implementation of token-based economies. The main outcome of the project is to gain insights into the sentiment of users by analyzing WEB3.0 enabled Twitter data. By implementing sentiment analysis techniques on a WEB3.0 enabled Twitter dataset, the project aims to contribute to the field of sentiment analysis and showcase the effectiveness of using WEB3.0 enabled for data collection and analysis. The project aims to provide valuable insights for various methods of sentiment analysis for researchers. Keywords – Sentiment Analysis, Blockchain- Enabled, Twitter data, WEB 3.0, Machine Learning Models, User Sentiment, Research Contribution

Applying Machine Learning models to Diagnosing Migraines with EEG Diverse Algorithms

This study investigates how well time collection analysis may be used by system-studying algorithms to diagnose migraines. Through the use of various algorithms and current statistical resources, such as EEG activity and affected person histories, the mission will develop a predictive model to identify the start of migraine signs and symptoms, allowing for prompt and early management for sufferers. The results will help to compare how the algorithms affect migraine accuracy predictions and how well they forecast migraine presence early enough for preventative interventions. Furthermore, studies may be conducted to examine the model's ability to be employed in real-time patient monitoring and to identify actionable inputs from the algorithms. This work presents novel machine learning algorithms software for time series analysis of functions such as temperature, heart rate, and EEG indications, which can be used to identify migraines. The paper delves into the idea of utilizing machine learning algorithms to identify migraine styles, examines the pre-processing procedures to accurately arrange the indications, and provides the results of a study conducted to evaluate the efficacy of the solution. The observation's results show that the suggested diagnostic framework is capable of accurately identifying and categorizing migraines, enabling medical professionals to recognize the warning indications of migraine and predict when an attack would begin. The examination demonstrates the possibility of devices learning algorithms to correctly and accurately diagnose migraines, but more research is necessary to obtain more detailed information about this situation.

Explainable Early Prediction of Gestational Diabetes Biomarkers by Combining Medical Background and Wearable Devices: A Pilot Study with a Cohort Group in South Africa.

This study aims to explore the potential of Internet of Things (IoT) devices and explainable Artificial Intelligence (AI) techniques in predicting biomarker values associated with GDM when measured 13 - 16 weeks prior to diagnosis. We developed a system that forecasts biomarkers such as LDL, HDL, triglycerides, cholesterol, HbA1c, and results from the Oral Glucose Tolerance Test (OGTT) including fasting glucose, 1-hour, and 2-hour post-load glucose values. These biomarker values are predicted based on sensory measurements collected around week 12 of pregnancy, including continuous glucose levels, short physical movement recordings, and medical background information. To the best of our knowledge, this is the first study to forecast GDM-associated biomarker values 13 to 16 weeks prior to the GDM screening test, using continuous glucose monitoring devices, a wristband for activity detection, and medical background data. We applied machine learning models, specifically Decision Tree and Random Forest Regressors, along with Coupled-Matrix Tensor Factorisation (CMTF) and Elastic Net techniques, examining all possible combinations of these methods across different data modalities. The results demonstrated good performance for most biomarkers. On average, the models achieved Mean Squared Error (MSE) between 0.29 and 0.42 and Mean Absolute Error (MAE) between 0.23 and 0.45 for biomarkers like HDL, LDL, cholesterol, and HbA1c. For the OGTT glucose values, the average MSE ranged from 0.95 to 2.44, and the average MAE ranged from 0.72 to 0.91. Additionally, the utilisation of CMTF with Alternating Least Squares technique yielded slightly better results (0.16 MSE and 0.07 MAE on average) compared to the well-known Elastic Net feature selection technique. While our study was conducted with a limited cohort in South Africa, our findings offer promising indications regarding the potential for predicting biomarker values in pregnant women through the integration of wearable devices and medical background data in the analysis. Nevertheless, further validation on a larger, more diverse cohort is imperative to substantiate these encouraging results.

The Prediction of Low-Rise Building Construction Cost Estimation Using Extreme Learning Machine

This study aims to predict the possibility of low-rise building construction costs by applying machine learning models, and the performance of each model is evaluated and compared with ensemble methods. The artificial neural network (ANN) emerges as the top-performing individual model, attaining an accuracy of 0.891, while multiple linear regression and decision trees follow closely with accuracies of 0.884 and 0.864 respectively. Ensemble methods like maximum voting ensemble (MVE) improve the accuracy beyond individual models with an impressive accuracy rate of 0.924. Meanwhile, the stacking ensemble and averaging ensemble also demonstrate competitive performance with accuracies of 0.883 and 0.871, respectively. These findings can result in more informed decision-making, which is valuable for the real estate industry.

The ripple effect of dataset reuse: Contextualising the data lifecycle for machine learning data sets and social impact

Although there exists a rich literature on data lifecycle, a common framework for data lifecycle depicts reuse as the last stage. However, this framework fails to explain the complex lifecycle of machine learning (ML) data sets, which can have many different afterlives. Data sets for ML can be expanded to supplement previous research, and researchers can concatenate multiple data sets to develop new models. This study discusses ML dataset reuse through the lens of the data–information–knowledge–wisdom pyramid. In social science research, researchers might reuse data to analyse a new research question that is still in the context of the data domain. By contrast, research practices in ML, where researchers layer multiple data sets for training purposes, require us to ask whether the existing data lifecycle model, ending with ‘reuse’, is appropriate for explaining such an iterative and layered lifecycle. This study introduces one case of merging computer vision data set and natural language processing data set and two cases of applying ML models from outside of the ML community (hate speech detection and politeness detection) to justify a framework for a ML dataset lifecycle. Last but not least, this study proposes a ML dataset lifecycle and provides case examples to describe each stage.

Machine learning for sustainable investing: Current applications and overcoming obstacles in ESG analysis

The intersection of Environmental, Social, and Governance (ESG) issues and Machine Learning (ML) has garnered significant attention in recent years as companies and investors increasingly recognize the paramount importance of sustainable and responsible business practices. ML techniques have been actively explored to tackle various ESG-related challenges, including enhancing ESG data quality and availability, developing comprehensive and dynamic ESG risk models, and optimizing ESG portfolios. The overall process of applying ML models in ESG analysis involves data collection, preprocessing, model training and evaluation, and model interpretation. Commonly used ML models in ESG analysis include logistic regression, decision trees, random forests, and support vector machines. However, there are notable obstacles to overcome, such as the lack of standardization and transparency in ESG data, as well as the potential for bias and ethical concerns in ML-based approaches. Further research and collaborative efforts among researchers and practitioners are crucial to fully realize the potential of ML in enhancing ESG analysis while ensuring transparency, ethical use, and alignment with sustainable and responsible investing principles.

Nine (not so simple) steps: a practical guide to using machine learning in microbial ecology.

Due to the complex nature of microbiome data, the field of microbial ecology has many current and potential uses for machine learning (ML) modeling. With the increased use of predictive ML models across many disciplines, including microbial ecology, there is extensive published information on the specific ML algorithms available and how those algorithms have been applied. Thus, our goal is not to summarize the breadth of ML models available or compare their performances. Rather, our goal is to provide more concrete and actionable information to guide microbial ecologists in how to select, run, and interpret ML algorithms to predict the taxa or genes associated with particular sample categories or environmental gradients of interest. Such microbial data often have unique characteristics that require careful consideration of how to apply ML models and how to interpret the associated results. This review is intended for practicing microbial ecologists who may be unfamiliar with some of the intricacies of ML models. We provide examples and discuss common opportunities and pitfalls specific to applying ML models to the types of data sets most frequently collected by microbial ecologists.

Parametric study of anomaly detection models for defect detection in infrared thermography

In the current NDT 4.0 revolution, machine learning and artificial intelligence have emerged as the major enablers for non-destructive testing and evaluation (NDT&E) of industrial components. However, recent developments in active thermal NDT (TNDT) support its use as a practical method for checking a range of industrial components. Additionally, recent post-processing research in TNDT has developed several machine learning models to replace human interaction and offer automatic defect detection. However, the smaller area of the flaws and their related few thermal profiles than the wide sound area, leading to imbalanced datasets, make it difficult to train a supervised deep neural. Recently added to TNDT are anomaly detection models and one-class classifiers, both of which are commonly applied machine learning models to real-world issues. The accuracy and other important metrics in autonomous defect detection are influenced by the hyper-parameters of these models, such as contamination factor, volume of training data, and initialization parameter of the relevant model. The current paper investigates how initialization parameters affect these models’ TNDT capabilities for automated flaw detection. Using quadratic frequency modulated thermal wave imaging (QFMTWI), a carbon fiber-reinforced polymer specimen with variously sized artificially produced back-holes at different depths is examined. A good hyper-parameter for automatic flaw identification is chosen after qualitatively comparing testing accuracy, precision, recall, F-score, and probability.