Supervised Learning Research Articles

Cardiac ultrasomics for acute myocardial infarction risk stratification and prediction of all-cause mortality: a feasibility study

BackgroundCurrent risk stratification tools for acute myocardial infarction (AMI) have limitations, particularly in predicting mortality. This study utilizes cardiac ultrasound radiomics (i.e., ultrasomics) to risk stratify AMI patients when predicting all-cause mortality.ResultsThe study included 197 patients: (a) retrospective internal cohort (n = 155) of non-ST-elevation myocardial infarction (n = 63) and ST-elevation myocardial infarction (n = 92) patients, and (b) external cohort from the multicenter Door-To-Unload in ST-segment–elevation myocardial infarction [DTU-STEMI] Pilot Trial (n = 42). Echocardiography images of apical 2, 3, and 4-chamber were processed through an automated deep-learning pipeline to extract ultrasomic features. Unsupervised machine learning (topological data analysis) generated AMI clusters followed by a supervised classifier to generate individual predicted probabilities. Validation included assessing the incremental value of predicted probabilities over the Global Registry of Acute Coronary Events (GRACE) risk score 2.0 to predict 1-year all-cause mortality in the internal cohort and infarct size in the external cohort. Three phenogroups were identified: Cluster A (high-risk), Cluster B (intermediate-risk), and Cluster C (low-risk). Cluster A patients had decreased LV ejection fraction (P < 0.01) and global longitudinal strain (P = 0.03) and increased mortality at 1-year (log rank P = 0.05). Ultrasomics features alone (C-Index: 0.74 vs. 0.70, P = 0.04) and combined with global longitudinal strain (C-Index: 0.81 vs. 0.70, P < 0.01) increased prediction of mortality beyond the GRACE 2.0 score. In the DTU-STEMI clinical trial, Cluster A was associated with larger infarct size (> 10% LV mass, P < 0.01), compared to remaining clusters.ConclusionsUltrasomics-based phenogroup clustering, augmented by TDA and supervised machine learning, provides a novel approach for AMI risk stratification.

Cheminformatic Identification of Tyrosyl-DNA Phosphodiesterase 1 (Tdp1) Inhibitors: A Comparative Study of SMILES-Based Supervised Machine Learning Models.

Tyrosyl-DNA phosphodiesterase 1 (Tdp1) repairs damages in DNA induced by abortive topoisomerase 1 activity; however, maintenance of genetic integrity may sustain cellular division of neoplastic cells. It follows that Tdp1-targeting chemical inhibitors could synergize well with existing chemotherapy drugs to deny cancer growth; therefore, identification of Tdp1 inhibitors may advance precision medicine in oncology. Current computational research efforts focus primarily on molecular docking simulations, though datasets involving three-dimensional molecular structures are often hard to curate and computationally expensive to store and process. We propose the use of simplified molecular input line entry system (SMILES) chemical representations to train supervised machine learning (ML) models, aiming to predict potential Tdp1 inhibitors. An open-sourced consensus dataset containing the inhibitory activity of numerous chemicals against Tdp1 was obtained from Kaggle. Various ML algorithms were trained, ranging from simple algorithms to ensemble methods and deep neural networks. For algorithms requiring numerical data, SMILES were converted to chemical descriptors using RDKit, an open-sourced Python cheminformatics library. Out of 13 optimized ML models with rigorously tuned hyperparameters, the random forest model gave the best results, yielding a receiver operating characteristics-area under curve of 0.7421, testing accuracy of 0.6815, sensitivity of 0.6444, specificity of 0.7156, precision of 0.6753, and F1 score of 0.6595. Ensemble methods, especially the bootstrap aggregation mechanism adopted by random forest, outperformed other ML algorithms in classifying Tdp1 inhibitors from non-inhibitors using SMILES. The discovery of Tdp1 inhibitors could unlock more treatment regimens for cancer patients, allowing for therapies tailored to the patient's condition.

Machine learning-assisted rapid determination for traditional Chinese Medicine Constitution

The aim of this study was to develop a machine learning-assisted rapid determination methodology for traditional Chinese Medicine Constitution. Based on the Constitution in Chinese Medicine Questionnaire (CCMQ), the most applied diagnostic instrument for assessing individuals’ constitutions, we employed automated supervised machine learning algorithms (i.e., Tree-based Pipeline Optimization Tool; TPOT) on all the possible item combinations for each subscale and an unsupervised machine learning algorithm (i.e., variable clustering; varclus) on the whole scale to select items that can best predict body constitution (BC) classifications or BC scores. By utilizing subsets of items selected based on TPOT and corresponding machine learning algorithms, the accuracies of BC classifications prediction ranged from 0.819 to 0.936, with the root mean square errors of BC scores prediction stabilizing between 6.241 and 9.877. Overall, the results suggested that the automated machine learning algorithms performed better than the varclus algorithm for item selection. Additionally, based on an automated machine learning item selection procedure, we provided the top three ranked item combinations with each possible subscale length, along with their corresponding algorithms for predicting BC classification and severity. This approach could accommodate the needs of different practitioners in traditional Chinese medicine for rapid constitution determination.

A Supervised Machine Learning Model for Dental Crowns Material Design for Manufacturing

A Supervised Machine Learning Model for Dental Crowns Material Design for Manufacturing

67 Precision identification and weight assessment of cattle using supervised machine learning on body surface keypoints

Abstract Computer vision systems (CVS) offer identification solutions for animals with distinct coat patterns, but are less effective for solid-colored herds. In addition, they can be used to measure critical phenotypes, such as body weight (BW). Providing both BW and identification for solid-colored animals can help farmers make decisions. This study aimed to 1) develop an automatic CVS capable of identifying using the Euclidean distance between keypoints located at specific anatomical landmarks (e.g., bony prominences), and 2) predict the BW using features extracted from these keypoints. The keypoint model was trained to identify seven keypoints per cow, located at the left and right hips, left and right pin bones, tail head, sacral, and cervical vertebrae. These locations remain constant as body condition score of a cow changes. The Euclidean distance between these keypoints was used to generate biometric features for each cow. The keypoint detection model was trained using 3,928 images over 900 epochs with a batch size of 14 and validated using 391 images. This trained model was used to predict keypoints on a different set of 41 cows over 5 d, resulting in a total of 6,944 images which correspond to the dataset for cow identification. The Random Forest (RF) algorithm for cow identification was trained with the first 4 d (5,376 images), and the last day served as the test set (1,568 images). For the cow identification, a RF algorithm was employed on the Euclidean distance of the keypoints. The hyperparameters were determined through a grid search on the training set, utilizing 5-fold cross-validation. The hyperparameters ‘mtry’ (which ranged from 1 to 10) and ‘ntree’ (which ranged from 250 to 500, in increments of 50) were optimized. For the second objective, due to the completely new environment, the keypoint model was fine-tuned using transfer learning for 240 epochs with a batch size of 14, utilizing 310 images. The model predicted keypoints in 1,593 images from different beef-on-dairy animals. This dataset was used to train the BW prediction model. The Euclidean distances from the predicted images and sex were used as features. A RF model was trained using a leave-one-animal-out cross-validation approach. The hyperparameters were determined through a grid search on the training set, using 10-fold cross-validation. The ‘mtry’ parameter ranged from 2 to 16, in increments of 2. For animal identification, the model achieved an accuracy, precision, recall, and F1-score of 92.7%, 89.0%, 90.2%, and 92.7%, respectively. To predict BW, the RF model achieved an R² of 0.86 and a root mean squared prediction error of 36.9 kg, representing 6.7% of the observed values average. These results suggest that keypoints located on the dorsal body surface can identify and weigh individual animals, even those lacking distinct coat color patterns.

Assessment of a probabilistic supervised machine learning method to estimate biomass expansion and conversion factors: A case study on cedar and pine trees.

Assessment of a probabilistic supervised machine learning method to estimate biomass expansion and conversion factors: A case study on cedar and pine trees.

Predicting effective thermal conductivity of HGM composite using ML

Data-driven materials research can be used as an effective tool to supplement conventional approaches in designing new materials. Hollow glass microsphere (HGM) composites are widely used for high-temperature thermal insulation applications, and their synthesis is traditionally done by varying parameters of the constituents on a trial-and-error basis. In this work, a prediction tool based on a supervised machine learning (ML) model is developed to predict the effective thermal conductivity (ETC) of an HGM composite by tailoring the composition and parameters of the constituents to reduce the time and cost involved. A comprehensive database containing the various input features is generated from previous experimental investigations conducted on various HGM composites. ML models, namely random forest regression (RF), K-nearest neighbor (KNN), support vector regression (SVR), and artificial neural network (ANN), are used to predict the ETC of HGM composites. Feature importance analysis showed that the matrix material’s thermal conductivity and the composite’s bulk density have the highest impact on the ETC of the HGM composite, followed by porosity and average microsphere size. ANN emerged as the best model to predict HGM composite ETC with the lowest root mean square error and highest R2 value for predictions. Moreover, a systematic approach for key feature selection using ANN shows that adding or omitting additional features beyond an optimal combination degrades the model’s predictive accuracy.

Exploring the role of topological descriptors to predict physicochemical properties of anti-HIV drugs by using supervised machine learning algorithms

In order to explore the role of topological indices for predicting physio-chemical properties of anti-HIV drugs, this research uses python program-based algorithms to compute topological indices as well as machine learning algorithms. Degree-based topological indices are calculated using Python algorithm, providing important information about the structural behavior of drugs that are essential to their anti-HIV effectiveness. Furthermore, machine learning algorithms analyze the physio-chemical properties that correspond to anti-HIV activities, making use of their ability to identify complex trends in large, convoluted datasets. In addition to improving our comprehension of the links between molecular structure and effectiveness, the collaboration between machine learning and QSPR research further highlights the potential of computational approaches in drug discovery. This work reveals the mechanisms underlying anti-HIV effectiveness, which paves the way for the development of more potent anti-HIV drugs. This work reveals the mechanisms underlying anti-HIV efficiency, which paves the way for the development of more potent anti-HIV drugs which demonstrates the invaluable advantages of machine learning in assessing drug properties by clarifying the biological processes underlying anti-HIV behavior, which paves the way for the design and development of more effective anti-HIV drugs.

Enhancing PVT property predictions for black oil reservoirs through the application of supervised machine learning techniques

The study presents an innovative approach utilizing machine learning (ML) techniques to forecast essential characteristics of black oil reservoirs, namely the oil formation volume factor (Bo) and the solution gas-oil ratio (Rs). A suite of four established ML algorithms-Random Forest, Linear Regression, Ada Boost, and Gradient Boost were extensively conditioned using a dataset that includes 297 individual data points and evaluation is done using test data and a blind test of models. The study showcases the practical efficacy of ML in accurately predicting Bo and Rs, even with a relatively modest dataset. The potential of ML models to save time and costs by reducing the dependence on laborious laboratory measurements is underscored, presenting a transformative shift in reservoir engineering practices. Furthermore, the current research extends its impact by comparing ML predictions with various empirical correlations and actual values, demonstrating that ML models, particularly Gradient Boost, consistently outperform traditional correlations and it can be used to predict values in the undersaturated region too. It is observed from the blind test results that for the prediction of Bo Gradient boost and random forest models (R2 0.97, RMSE 0.01) is a preferable choice and for the prediction of Rs the Gradient boost model (R2 0.97, RMSE 22) can be preferred. The adaptability of ML models to integrate new data over time is a significant feature, ensuring continuous improvement in accuracy. This study adds to the expanding reservoir engineering knowledge base, highlighting the practical use of machine learning for accurately predicting crucial reservoir properties. Its significance goes beyond theoretical progress, providing tangible tools for reservoir engineers and geologists to improve decision-making in the management of oil and gas reservoirs.

Drone delivery problem with multi-flight level: Machine learning based solution approach

This study provides a new perspective on the drone delivery problems (DDP) by conceptualizing the vertical space in multiple flight levels. The main advantage of drone delivery is efficiency in utilizing free three-dimension aerial space, enabling numerous travels at multiple flight levels. However, the operational efficiency tradeoff exists according to the flight level, particularly in metropolitan cities with countless skyscrapers. Operation on the upper level requires less detour on horizontal movement, but it needs more time on the vertical movement of drones to reach the upper level. This study introduces a novel DDP by dividing the vertical airspace into multiple flight levels, thereby providing an opportunity to increase overall delivery efficiency based on realistic constraints faced by cities. We formulate this problem into a mathematical model and suggest a new supervised machine learning approach called SPML (Sequential Prediction Machine Learning). The SPML has three phases. In the first phase, customers are sequenced by priority. The second phase uses a supervised machine learning model trained by the data collected from solving the mixed-integer linear programming (MILP) model to assign customers to the depot. The third phase is distributing jobs to drones by using dynamic programming.

The use of Supervised Learning to perform pairwise classification for Record Linkage over real world data

Supervised Learning (SL) is a subset of Machine Learning that uses labeled data to establish the learning process and perform tasks such as classification and regression. Record Linkage (RL) is the process for entity resolution and aims to determine if two records correspond to the same entity in the real world. A challenging aspect of RL involves estimating probabilities and weights for attributes and choosing the algorithm to perform the comparison. Our study aims to address this challenge and contribute in two primary ways: 1) by providing a labeled dataset comprising pairs of records, and 2) by evaluating an alternative classification method using an SL classifier. We generated more than 28.000 pairs, manually reviewed, derived from a massive Record Linkage Process on the Brazilian Public Health System (SUS). From that, we selected 5000 random stratified pairs for evaluation. The data contains demographic attributes such as sex, city, date of birth, zip code, and name. We modeled the data pairwise in a vector of latent space. Our methodology involved comparing Random Forest, LightGBM, XGBoost, and Neural Networks for classification tasks on these pairs, utilizing cross-validation to optimize hyperparameters. Our findings reveal that XGBoost had the highest performance, achieving 96% accuracy. Additionally, we conducted privacy evaluations using bloom filters, yielding an accuracy of 93%. The adoption of SL presents a promising space due to its capability for weight estimation and classification. In future endeavors, integrating these algorithms into a framework for the Record Linkage process could streamline procedures for researchers without compromising performance.

A machine learning model based on CHAT-23 for early screening of autism in Chinese children.

Autism spectrum disorder (ASD) is a neurodevelopmental condition that significantly impacts the mental, emotional, and social development of children. Early screening for ASD typically involves the use of a series of questionnaires. With answers to these questionnaires, healthcare professionals can identify whether a child is at risk for developing ASD and refer them for further evaluation and diagnosis. CHAT-23 is an effective and widely used screening test in China for the early screening of ASD, which contains 23 different kinds of questions. We have collected clinical data from Wuxi, China. All the questions of CHAT-23 are regarded as different kinds of features for building machine learning models. We introduce machine learning methods into ASD screening, using the Max-Relevance and Min-Redundancy (mRMR) feature selection method to analyze the most important questions among all 23 from the collected CHAT-23 questionnaires. Seven mainstream supervised machine learning models were built and experiments were conducted. Among the seven supervised machine learning models evaluated, the best-performing model achieved a sensitivity of 0.909 and a specificity of 0.922 when the number of features was reduced to 9. This demonstrates the model's ability to accurately identify children for ASD with high precision, even with a more concise set of features. Our study focuses on the health of Chinese children, introducing machine learning methods to provide more accurate and effective early screening tests for autism. This approach not only enhances the early detection of ASD but also helps in refining the CHAT-23 questionnaire by identifying the most relevant questions for the diagnosis process.

CattleAssigner: A framework for accurate assignment of individuals to cattle lineages and populations using minimum informative markers

Assigning individual animals to their respective breeds, populations or lineages has immense significance in the evolutionary analyses of global cattle populations besides detecting the underlying genetic variation that may likely have facilitated the adaptation of these breeds to diverse environmental conditions. It is also important in discovering the geographic patterns of genetic variation in cattle populations as well as tracing the geographical origin of breeds, food products, and diseases. Given this, the present study was undertaken to elucidate the minimum number of informative single nucleotide polymorphism (SNP) markers, originally generated using medium-density BovineSNP50 BeadChip across 1823 individuals represnting 73 populations, to assign individual animals to the corresponding lineage/group (African or European or Indicine or admixed) and respective populations within that lineage/group using two well-known supervised machine learning (ML) algorithms namely Random Forest (RF) and Extreme Gradient Boosting (XGBoost). Each of the two ML models were trained with the most informative SNP panels (with sizes of 48, 96, and 192) that were elucidated using two statistical methods i.e., principal component analysis (PCA) and Wright's fixation index (FST), and two ML methods (RF with Gini, and RF with MDA). Three panels with the topmost discriminant SNPs (at 192, 96, and 48 densities) were created for each of the marker preselection approaches. These panels were evaluated, based on their performance vis-à-vis animals’ assignment to respective lineage, population group or population. The results showed that XGBoost achieved the best accuracy of 95% with 192-SNP panel (selected via RF with MDA), followed by RF (93% accuracy) with 192-SNP panel (selected via RF with either Gini or MDA), for animal to lineage assignment. Similarly, RF trained with 48-SNP panel (selected via RF with Gini algorithm) achieved the best accuracy of 97% for assigning animals to African lineage, while it achieved the best accuracy of 89% for assigning animals to admixed populations using 96-SNP panel (selected via PCA). On the other hand, XGBoost achieved the best accuracy of 88% for assigning animals to European breeds using 192-SNP panel (selected via FST method). Furthermore, the results with both RF and XGBoost achieved a poor performance of assigning animals of Indicine lineage to the correct group as the best accuracy for such assignment was 66%, achieved using RF with 192-SNP panel (selected via FST method). In conclusion, the study reports the applicability of statistical and ML approaches for identification of discriminatory SNPs, useful the assignment of individuals to corresponding lineages and to respective populations within lineages besides revealing the efficiency of XGBoost and RF-based ML models in performing such assignments. Both the ML models achieved better performance as compared to statistical ones in assigning the animals to specific lineages while they faired comparably similar to each other for the assignment of individuals to respective populations within respective lineages or population groups.

Deep Clustering for Epileptic Seizure Detection.

The objective of this study is to address the challenges of EEG-based epileptic seizure detection by introducing a novel methodology, Deep Embedded Gaussian Mixture (DEGM). The DEGM method begins with a deep autoencoder (DAE) for embedding the input EEG data, followed by Singular Value Decomposition (SVD) to enhance the representational quality of the embedding while achieving dimensionality reduction. A Gaussian Mixture Model (GMM) is then employed for clustering purposes. Unlike conventional supervised machine learning and deep learning techniques, DEGM leverages deep clustering (DC) algorithms for more effective seizure detection. Empirical results from two real-world epileptic datasets demonstrate the notable performance of DEGM. The method's effectiveness is particularly remarkable given the substantial size of the datasets, showcasing its ability to handle large-scale EEG data efficiently. In conclusion, the DEGM methodology provides a novel and effective approach for EEG-based epileptic seizure detection, addressing key challenges such as data variability and artifact contamination. By combining deep autoencoders, SVD, and GMM, DEGM achieves superior clustering performance compared to existing methods, representing a significant advancement in biomedical research and clinical applications for epilepsy. Its robust performance on large datasets underscores its potential for improving seizure detection accuracy, ultimately contributing to better patient outcomes.

The Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning.

Therapeutic antibodies such as monoclonal antibodies (mAbs), bispecific and multispecific antibodies are pivotal in therapeutic protein development and have transformed disease treatments across various therapeutic areas. The integrity of therapeutic antibodies, however, is compromised by sequence liabilities, notably deamidation, where asparagine (N) and glutamine (Q) residues undergo chemical degradations. Deamidation negatively impacts the efficacy, stability, and safety of diverse classes of antibodies, thus necessitating the critical need for the early and accurate identification of vulnerable sites. In this article, a comprehensive antibody deamidation-specific dataset (n = 2285) of varied modalities was created by using high-throughput automated peptide mapping followed by supervised machine learning to predict the deamidation propensities, as well as the extents, throughout the entire antibody sequences. We propose a novel chimeric deep learning model, integrating protein language model (pLM)-derived embeddings with local sequence information for enhanced deamidation predictions. Remarkably, this model requires only sequence inputs, eliminating the need for laborious feature engineering. Our approach demonstrates state-of-the-art performance, offering a streamlined workflow for high-throughput automated peptide mapping and deamidation prediction, with the potential of broader applicability to other antibody sequence liabilities.

WP2.10 - The Future of Artificial Intelligence (AI) in Surgery

Abstract Aims Artificial intelligence (AI) has revolutionised many industries, especially in the field of medicine. However, its application in medical surgery has been limited. Our study aims to review current literature surrounding AI’s use in surgery and identify potential future application of this new technology. We look at the preoperative, intra-operative, and postoperative phases of a patient’s surgical journey and see how AI can improve each component. Methods A selective literature review was conducted by gathering 19 relevant research papers of interest related to AI in surgery. These studies were collected from PubMed and papers only published from the past 5 years were included. Of the 19 papers 11 were included in the final review. Results This review reveals numerous applications of AI in surgery, primarily showcasing its accuracy in preoperative patient data analysis with high sensitivities of 89%. Secondly intra-operatively AI can provide guidance through supervised machine learning and computer vision with accuracies as high as 95%. Finally, postoperatively AI has been shown to have a high accuracy at preoperative prediction of surgical site readmission rates, diabetes remission and personalised weight loss trajectories. Conclusions The findings of this review demonstrate the transformative ability of AI on various stages of the surgical journey. While demonstrating significant promise, challenges such as ethical considerations and regulatory frameworks need to be addressed to ensure responsible and safe integration. The new advancements shown in AI provide the foundation for future research into the technology.

Smart Hostel Allocation System Using Naïve Bayes Algorithm

This research explores the potential of automated personality classification using supervised machine learning techniques, with a specific focus on the Myers-Briggs Type Indicator (MBTI) for hostel allocation. The problem is the manual and resource-intensive allocation of hostel accommodations in educational institutions, leading to personality mismatches among roommates, which negatively impacts students' academic engagement and well-being. The aim of this research is to improve hostel room allocation by predicting student personality traits, thus minimizing personality mismatches among roommates. A Naïve Bayes classifier was applied to the MBTI personality dataset acquired from Kaggle. Various preprocessing techniques, such as tokenization, stop-word removal, and word stemming, were used to prepare the dataset for feature selection. CountVectorizer and TF-IDF methods were employed to convert text into numerical vectors for machine learning analysis. Additionally, SMOTE was implemented to address class imbalance within the dataset, ensuring better model performance. The Naïve Bayes classifier showed promising results, with scores achieved across all personality traits as 73% precision, 70% accuracy, F1 Score 70% and Recall 70% in predicting MBTI personality traits from input text. Visualizations and performance metrics such as accuracy, precision, recall, and F1-score were used to evaluate the model's efficiency. The research findings suggest that supervised machine learning techniques can effectively predict personality traits from social media data, aiding in improved hostel room allocation and potentially benefiting broader applications in education and computational psychology. This system is recommended for educational institutions and residential complexes seeking efficient roommate pairing.

EEG-based Signatures of Schizophrenia, Depression, and Aberrant Aging: A Supervised Machine Learning Investigation.

Electroencephalography (EEG) is a noninvasive, cost-effective, and robust tool, which directly measures in vivo neuronal mass activity with high temporal resolution. Combined with state-of-the-art machine learning (ML) techniques, EEG recordings could potentially yield in silico biomarkers of severe mental disorders. Pathological and physiological aging processes influence the electrophysiological signatures of schizophrenia (SCZ) and major depressive disorder (MDD). From a single-center cohort (N = 735, 51.6% male) comprising healthy control individuals (HC, N = 245) and inpatients suffering from SCZ (N = 250) or MDD (N = 240), we acquired resting-state 19 channel-EEG recordings. Using repeated nested cross-validation, support vector machine models were trained to (1) classify patients with SCZ or MDD and HC individuals and (2) predict age in HC individuals. The age model was applied to patient groups to calculate Electrophysiological Age Gap Estimation (EphysAGE) as the difference between predicted and chronological age. The links between EphysAGE, diagnosis, and medication were then further explored. The classification models robustly discriminated SCZ from HC (balanced accuracy, BAC = 72.7%, P < .001), MDD from HC (BAC = 67.0%, P < .001), and SCZ from MDD individuals (BAC = 63.2%, P < .001). Notably, central alpha (8-11 Hz) power decrease was the most consistently predictive feature for SCZ and MDD. Higher EphysAGE was associated with an increased likelihood of being misclassified as SCZ in HC and MDD (ρHC = 0.23, P < .001; ρMDD = 0.17, P = .01). ML models can extract electrophysiological signatures of MDD and SCZ for potential clinical use. However, the impact of aging processes on diagnostic separability calls for timely application of such models, possibly in early recognition settings.

Predicting Consumer Behaviour from E-Commerce Platform Product Reviews: A Machine Learning Approach to Sentiment Analysis

Given the recent advancements in web technology, users make use of the online services and offer their feedback thereby creating a vast amount of data available on the internet. To enable improved decision-making, it is essential to study, analyze, and organize this vast amount of user opinion, perspectives, feedback, and suggestions available through online resources. Nevertheless, the majority of e-commerce websites encounter challenges in comprehensively analyzing each individual review owing to the sheer volume of user feedback they receive. The aim of this study is to conduct sentiment analysis of product review from e-commerce platforms using machine learning to predict consumer behaviour so as to make informed decision. The methodology used was a sentiment analysis using models developed in Python with machine learning algorithms (Support Vector Machine and Naive Bayes) in order to compare the performance accuracy. The model was trained and tested on a dataset, and the performance of the two supervised machine learning algorithms for sentiment classification was compared. Accordingly, the results of the experiment showed that the Support Vector Machine technique outperformed Naive Bayes, obtaining accuracy levels of 97%. The study also demonstrates the significance of carrying out further research by exploring different machine-learning techniques and evaluating their performance on local market datasets. Additionally, it was observed that utilizing more data might improve the model's classification and prediction accuracy.

Supervised machine learning for understanding and predicting the status of bistable eukaryotic plankton community in urbanized rivers

Understanding and predicting the ecological status of urbanized rivers is crucial for their restoration and management. However, the complex and nonlinear nature of ecological responses poses a challenge to the development of predictive models. Here, the study investigated and predicted the status of eukaryotic plankton communities in urbanized rivers by coupling environmental DNA metabarcoding, the alternative stable states theory, and supervised machine learning (SML) models. The results revealed two distinct states of eukaryotic plankton communities under similar environmental conditions: one state was characterized by the enrichment of a diverse phytoplankton population and the high relative abundance of protozoa, whereas the alternative state was characterized by abundant phytoplankton and fungi with an associated risk of algal blooms. Turbidity was identified as a key driver based on the SML model and Mantel test. Potential analysis demonstrated that the response pattern of eukaryotic plankton communities to turbidity was thresholds with hysteresis (Threshold1 = 17 NTU, Threshold2 = 24 NTU). A reduction in turbidity induced a regime shift in the eukaryotic plankton community toward an alternative state associated with a risk of algal blooms. In the prediction of ecological status, both SML models showed excellent performance (R2 > 0.80, RMSE < 0.1, Kappa > 0.70). Additionally, SHapley Additive exPlanations analysis identified turbidity, chlorophyll-a, chemical oxygen demand (COD), ammonia nitrogen and green algae's amplicon sequence variants as crucial features for prediction, with turbidity and COD showing a synergistic effect on ecological status. A framework was further proposed to enhance the understanding and prediction of ecological status in urbanized rivers. The obtained results of this study demonstrated the feasibility of using SML models to predict and explain the ecological status of urbanized rivers with alternative stable states. This provides valuable insights for the application of SML models in the restoration and management of urbanized rivers.