Applying Machine Learning Approaches Research Articles

Campus Recruitment Prediction

For businesses and students alike, campus recruitment is an important occasion. While businesses aim to draw in the best employees, students eagerly anticipate beginning their professional careers. Salary prediction is a crucial component of college recruitment, when employers ascertain the wage ranges, they would offer prospective employees. Many criteria, including the candidate's qualifications, experience, and education, as well as the company's budget and industry norms, play a role in predicting the salary for campus recruitment. In this project, we'll apply machine learning approaches to forecast college recruitment salaries based on candidate historical data and salaries that match to those positions. In this project, we develop a predictive model for college recruitment by analysing the dataset that has been provided. Data processing and exploratory data analysis (EDA) are our initial steps. After that, we build a Flask web application that uses the trained predictive model to be deployed and lets users anticipate things based on input.

Drone-based monitoring of intertidal blue mussel beds in the Wadden Sea – comparison of a threshold and two machine learning approaches

In the Wadden Sea, intertidal mussel beds of the blue mussel (Mytilus edulis) and the Pacific oyster (Magallana gigas) form distinct epibenthic communities and local hotspots of high biomass and biodiversity. To detect and evaluate natural and anthropogenic processes, a ground-based monitoring program started over 25 years ago in the German Wadden Sea. In this study, we describe the potential of drones and machine learning approaches for a remote sensing-based integration into an existing monitoring program of intertidal mussel beds. A fixed wing drone was used to cover an area of up to 39ha in a single flight, with an overall time saving potential of 50%. Applying machine learning approaches, a detailed extraction of the intertidal blue mussel bed coverage with an overall accuracy (OA) up to 95.6% was reached, applying a Support Vector Machine (SVM). The application of a multispectral sensor improved the classification performance. Compared to ground-based monitoring, the drone-based method provided significantly more information on the area extension, coverage, and associated algae of the mussel beds. The results show that drones can provide a non-invasive way to survey large and difficult to access areas providing detailed maps of mussel beds and their internal structures.

Enhancing Network Security: Leveraging Machine Learning for Intrusion Detection

In order to precisely identify and categorise different kinds of network attacks, this study focuses on applying machine learning approaches for network intrusion detection. Data collection, preprocessing, feature scaling, model definition, feature selection, and assessment metrics are all part of the methodology. Different machine learning models, including Decision Tree Classifier and Random Forest Classifier, are considered, along with the use of all features or some part of features for each attack category. Evaluation is performed using K-fold cross-validation, with metrics such as accuracy, precision, recall, and F1-score analysed. Results indicate the efficiency of Random Forest Classifier in handling high-dimensional datasets and improving detection accuracy, making it a superior choice for network intrusion detection tasks.

Reinforcement Learning and Data-Generation for Syntax-Guided Synthesis

Program synthesis is the task of automatically generating code based on a specification. In Syntax-Guided Synthesis (SyGuS) this specification is a combination of a syntactic template and a logical formula, and the result is guaranteed to satisfy both. We present a reinforcement-learning guided algorithm for SyGuS which uses Monte-Carlo Tree Search (MCTS) to search the space of candidate solutions. Our algorithm learns policy and value functions which, combined with the upper confidence bound for trees, allow it to balance exploration and exploitation. A common challenge in applying machine learning approaches to syntax-guided synthesis is the scarcity of training data. To address this, we present a method for automatically generating training data for SyGuS based on anti-unification of existing first-order satisfiability problems, which we use to train our MCTS policy. We implement and evaluate this setup and demonstrate that learned policy and value improve the synthesis performance over a baseline by over 26 percentage points in the training and testing sets. Our tool outperforms state-of-the-art tool cvc5 on the training set and performs comparably in terms of the total number of problems solved on the testing set (solving 23% of the benchmarks on which cvc5 fails). We make our data set publicly available, to enable further application of machine learning methods to the SyGuS problem.

Machine learning approaches delimit cryptic taxa in a previously intractable species complex

Cryptic species are not diagnosable via morphological criteria, but can be detected through analysis of DNA sequences. A number of methods have been developed for identifying species based on genetic data; however, these methods are prone to over-splitting taxa with extreme population structure, such as dispersal-limited organisms. Machine learning methodologies have the potential to overcome this challenge. Here, we apply such approaches, using a large dataset generated through hybrid target enrichment of ultraconserved elements (UCEs). Our study taxon is the Aoraki denticulata species complex, a lineage of extremely low-dispersal arachnids endemic to the South Island of Aotearoa New Zealand. This group of mite harvesters has been the subject of previous species delimitation studies using smaller datasets generated through Sanger sequencing and analytical approaches that rely on multispecies coalescent models and barcoding gap discovery. Those analyses yielded a number of putative cryptic species that seems unrealistic and extreme, based on what we know about species’ geographic ranges and genetic diversity in non-cryptic mite harvesters. We find that machine learning approaches, on the other hand, identify cryptic species with geographic ranges that are similar to those seen in other morphologically diagnosable mite harvesters in Aotearoa New Zealand’s South Island. We performed both unsupervised and supervised machine learning analyses, the latter with training data drawn either from animals broadly (vagile and non-vagile) or from a custom training dataset from dispersal-limited harvesters. We conclude that applying machine learning approaches to the analysis of UCE-derived genetic data is an effective method for delimiting species in complexes of low-vagility cryptic species, and that the incorporation of training data from biologically relevant analogues can be critically informative.

Deep Learning Approach for Classification of Tweets in Detecting Cyber Truculent

Identification and Classification of truculent tweets is a serious problem. Studies have shown how truculent tweets on Twitter have caused anxiety and in extreme cases death of victims. Various researches have applied Machine Learning approaches while others Deep Learning in classifying toxic sentences. Impressive results have been obtained using Deep Learning approaches, the focus of the research is to develop an extended model for deep learning classification using cyber tweets. The Model developed in this research work used labelled dataset (twitter_parsed_dataset.csv), Maximum Entropy was used to extract keywords and entities. One-Dimensional Convolutional Neural Network (1d-CNN) was used to detect truculent in tweets. The experimental result from the developed model consider four preprocess datasets for classification, the Unigram, Bigram, Trigram and N-gram. The result that was obtained for Accuracy 96.1%, Precision 93.6%, Recall 73.7% and F1-Score 83.8% for different test during classification. The result obtained from the developed model for accuracy, 96.1% was compared with related work Banerjee’s accuracy of 93.97%. The developed model can be used for auto detection of truculent messages in cyberspace.

Resident space objects classification by machine learning techniques

In recent years the number of resident space objects in near-Earth space has been increasing drastically, which can lead to a “cascade collisions” effect and other catastrophic consequences. Therefore, it is crucial to monitor resident space objects, maintain and update catalogues on time, including the identification of unknown space objects to estimate their location and potential orbit. It is necessary to perform resident space object identification quickly and accurately to avoid possible satellite collisions resulting in large clouds of uncontrollable space debris fragments. That is why the task of resident space object preliminary classification by TLEs is stated and solved by applying machine learning approaches to achieve a higher classification quality and speed. This issue is a highly imbalanced classification problem, which narrows and specifies the used models. Applying the proposed resident space object classification model to space surveillance systems can decrease the time required for the object identification significantly.

Factors Associated with Unplanned Hospital Readmission after Discharge: A Descriptive and Predictive Study Using Electronic Health Record Data

Hospital readmission involves the unplanned emergency admission of patients within 30 days from discharge after the previous admission. According to the Canadian Health Institute (CIHI), 1 in 11 patients were readmitted within 30 days of leaving the hospital in 2021. In the USA, nearly 20% of Medicare patients were readmitted after discharge, where the average cost of readmission was approximately USD 15,000, as reported by the Agency for Healthcare Research and Quality (AHQR) in 2018. To tackle this issue, we first conducted a descriptive analysis study to understand the risk factors associated with hospital readmission, and then we applied machine learning approaches to predict hospital readmission by using patients’ demographic and clinical data extracted from the Electronic Health Record of the MIMIC-III clinical database. The results showed that the number of previous admissions during the last 12 months, hyperosmolar imbalance and comorbidity index were the top three significant factors for hospital readmission. The predictive model achieved a performance of 95.6% AP and an AUC = 97.3% using the Gradient Boosting algorithm trained on all features.

Applying Machine Learning Approach to Explore Childhood Circumstances and Self-Rated Health in Old Age - China and the US, 2020-2021.

Childhood circumstances impact senior health, prompting the introduction of machine learning methods to assess their individual and collective contributions to senior health. Using health and retirement study (HRS) and China Health and Retirement Longitudinal Study (CHARLS), we analyzed 2,434 American and 5,612 Chinese participants aged 60 and above. Conditional inference trees and forests were employed to estimate the influence of childhood circumstances on self-rated health (SRH). The conventional method estimated higher inequality of opportunity (IOP) values in both China (0.039, accounting for 22.67% of the total Gini coefficient 0.172) and the US (0.067, accounting for 35.08% of the total Gini coefficient 0.191). In contrast, the conditional inference tree yielded lower estimates (China: 0.022, accounting for 12.79% of 0.172; US: 0.044, accounting for 23.04% of 0.191), as did the forest (China: 0.035, accounting for 20.35% of 0.172; US: 0.054, accounting for 28.27% of 0.191). Childhood health, financial status, and regional differences were key determinants of senior health. The conditional inference forest consistently outperformed others in predictive accuracy, as demonstrated by lower out-of-sample mean squared error (MSE). The findings emphasize the need for early-life interventions to promote health equity in aging populations. Machine learning showcases the potential in identifying contributing factors.

Systematic Analysis of the Literature Addressing the Use of Machine Learning Techniques in Transportation—A Methodology and Its Application

Advances in the field of machine learning (ML) have been reflected in the intensity of research studies exploiting these techniques for a better understanding of existing phenomena, and for predicting future ones, as a mean for promoting a more efficient and sustainable transportation system. The present study aims to understand the trends of utilizing diverse ML approaches to tackle issues within sub-domains of transportation and to identify underutilized potentials among them. This paper presents a methodology for the bi-dimensional classification of a large corpus of scientific articles. The articles are classified into six transport-related sub-domains, based on the definition of the Israeli Smart Transport Research Center, whose aim is a transportation system with zero externalities, and the ML techniques used in each of them is identified. A fuzzy KNN model is implemented for the multi-classification of articles into the transportation sub-domains and an ontology-based reasoning for identifying the share of each applied ML approach is employed. The application of these methodologies to a corpus of 1718 articles revealed, among other findings, an increasing share of artificial neural networks and deep learning techniques from 2018 until 2022, particularly in the traffic management sub-domain. A significant contribution of the development of these automatic methodologies is the ability to reuse them for ongoing exploration of trends regarding the use of ML techniques for transportation sub-domains.

A DNA Methylation Signature Predicts Clinical Outcome in Pediatric AML Patients

Pediatric Acute Myeloid Leukemia (AML) is a heterogenous disease with a dismal outcome. Recently, transcriptomic-based leukemic stemness scores have highlighted the importance of DNA methyltransferase 3B in pediatric AML prognosis, which invites a deeper investigation of leukemic stem cells through the scope of their DNA methylome. Other studies have shown the prognostic impact of genome-wide methylation burden (PMID: 29928480, 33080932). As the World Health Organization has recognized methylation analysis as the most apt technology for determining lineage and cell population origins of tumors (PMID: 34921008), it is befitting to evaluate the DNA methylome as a prognostic tool in hematopoietic malignancies. Thus, we hypothesize that a DNA methylation-based signature has the potential to enhance the prognostic precision in pediatric AML. In this study, we applied machine learning approaches to DNA methylation data from 924 pediatric AML patients (only including data from samples with diagnostic bone marrow specimens) from three Children's Oncology Group clinical trials: AAML1031, AAML0531, and AAML03P1, available through GSE190931, GSE124413 and GDC_TARGET-AML, respectively. Clinical endpoints available were defined as :i) Minimal residual disease after induction 1 (MRD1): positive MRD1 if ≥ 1 leukemic cell per 1,000 mononuclear bone marrow cells (≥ 0.1%) determined by flow cytometry; ii) event-free survival (EFS) defined as the time from study enrollment to induction failure, relapse, secondary malignancy, death, or study withdrawal for any reason, with event-free patients censored on last follow-up; iii) overall survival (OS) defined as the time from study enrollment to death, with living patients censored on the date of last follow-up. Processing of the raw data followed best practices in the literature using SeSAMe (PMID: 30085201, 27924034). To independently validate the findings derived from the discovery cohort, we processed in parallel meDNA array data from bone marrow specimens at diagnosis from 201 AML patients treated on the multi-site clinical trials AML02 and AML08 (NCT00136084 and NCT00703820). As step 1, we performed Epigenome-Wide Association Study with Cox Proportional Hazards Regression (Cox-PH EWAS) to identify CpGs with methylation levels most predictive of OS, controlling for risk group categories. At p-value threshold of 1.0e-6, 167 CpGs were associated with OS, which were chosen as candidate CpGs for step 2 analysis using LASSO-based Cox-PH model, with 1,000 iterations of 10-fold cross-validation. 35 CpGs were consistently selected as non-zero coefficients in at least 95% of the models and were used to define the CpG based signature. Patients were further categorized into either Low MethylScoreAML Px (n=563, 60%) or High MethylScoreAML Px (n=361, 40%) groups using recursive partitioning statistics. In the discovery cohort, patients with High MethylScoreAML Px demonstrated a markedly poorer probability of OS (HR 3.91, p<0.0001) and EFS (HR 2.61, <0.0001) in comparison to the low MethylScore. These results were further tested in the validation cohort (n=201) for OS (HR 2.45, p=0.0006) and EFS (HR 2.15, p=0.0004). Notably, in subgroup analyses for both discovery and validation, MethylScoreAML Px remained statistically significant in multivariable models for EFS and OS with adjustments for MRD1 status, risk group, FLT3 status, white blood cell count at diagnosis, and patient age, indicating it as a robust, independent model supplementing currently known prognostic factors. In conclusion, this study unveils MethylScoreAML Px, a methylation-based prognostic risk score that robustly predicts clinical outcome in pediatric AML patients. To our knowledge, we are the first group to describe a Cox-PH-EWAS analysis in pediatric AML, as well as the first to devise and validate a prognostic risk score in >1000 patients from multiple pediatric-only trials. Future directions of research are focused on testing MethylScore in fresh peripheral blood samples using long read, real-time Oxford Nanopore sequencing technology, and comparing it with other risk scores.

Applying Machine Learning Approach to Identifying Channels in MIMO Networks for Communications in 5G-Enabled Sustainable Smart Cities

Applying Machine Learning Approach to Identifying Channels in MIMO Networks for Communications in 5G-Enabled Sustainable Smart Cities

Socio-Environmental Determinants of Mental and Behavioral Disorders in Youth: A Machine Learning Approach.

Growing evidence indicates that extreme environmental conditions in summer months have an adverse impact on mental and behavioral disorders (MBD), but there is limited research looking at youth populations. The objective of this study was to apply machine learning approaches to identify key variables that predict MBD-related emergency room (ER) visits in youths in select North Carolina cities among adolescent populations. Daily MBD-related ER visits, which totaled over 42,000 records, were paired with daily environmental conditions, as well as sociodemographic variables to determine if certain conditions lead to higher vulnerability to exacerbated mental health disorders. Four machine learning models (i.e., generalized linear model, generalized additive model, extreme gradient boosting, random forest) were used to assess the predictive performance of multiple environmental and sociodemographic variables on MBD-related ER visits for all cities. The best-performing machine learning model was then applied to each of the six individual cities. As a subanalysis, a distributed lag nonlinear model was used to confirm results. In the all cities scenario, sociodemographic variables contributed the greatest to the overall MBD prediction. In the individual cities scenario, four cities had a 24-hr difference in the maximum temperature, and two of the cities had a 24-hr difference in the minimum temperature, maximum temperature, or Normalized Difference Vegetation Index as a leading predictor of MBD ER visits. Results can inform the use of machine learning models for predicting MBD during high-temperature events and identify variables that affect youth MBD responses during these events.

Estimation of urban AQI based on interpretable machine learning.

Air pollution is an increasingly serious problem. Accurate and efficient prediction of air quality can effectively prevent air pollution and improve the quality of human life. The air quality index (AQI) is a dimensionless tool to describe air quality quantitatively. In this study, the machine learning (ML) method was used to estimate AQI for Shijiazhuang, China, as the research object, and pollutants and meteorological factors as data models. Specifically, eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Random Forest (RF) models were used. The experimental results show that XGBoost model captures the AQI variation trend well, and the R2 of XGBoost model is 0.929, which is 0.3% and 2.3% higher than the R2 of RF model and LightGBM model, respectively. In addition, through the SHAP-based model interpretation method, the study reveals the key factors of AQI variation, that is PM2.5 and PM10, play positive roles in the variation of AQI and AQI is less sensitive to meteorological factors. Finally, Beijing, Shanghai, Xi'an, and Guangzhou were selected to test the model's validity, and the model performance remained good. Our study shows that applying ML approach to air quality prediction is beneficial for efficiently assessing cities' future air quality.

Multinomial classification of NLRP3 inhibitory compounds based on large scale machine learning approaches.

The role of NLRP3 inflammasome in innate immunity is newly recognized. The NLRP3 protein is a family of nucleotide-binding and oligomerization domain-like receptors as well as a pyrin domain-containing protein. It has been shown that NLRP3 may contribute to the development and progression of a variety of diseases, such as multiple sclerosis, metabolic disorders, inflammatory bowel disease, and other auto-immune and auto-inflammatory conditions. The use of machine learning methods in pharmaceutical research has been widespread for several decades. An important objective of this study is to apply machine learning approaches for the multinomial classification of NLRP3 inhibitors. However, data imbalances can affect machine learning. Therefore, a synthetic minority oversampling technique (SMOTE) has been developed to increase the sensitivity of classifiers to minority groups. The QSAR modelling was performed using 154 molecules retrieved from the ChEMBL database (version 29). The accuracy of the multiclass classification top six models was found to fall within ranges of 0.99 to 0.86, and log loss ranges of 0.2 to 2.3, respectively. The results showed that the receiver operating characteristic curve (ROC) plot values significantly improved when tuning parameters were adjusted and imbalanced data was handled. Moreover, the results demonstrated that SMOTE offers a significant advantage in handling imbalanced datasets as well as substantial improvements in overall accuracy of machine learning models. The top models were then used to predict data from unseen datasets. In summary, these QSAR classification models exhibited robust statistical results and were interpretable, which strongly supported their use for rapid screening of NLRP3 inhibitors.

Exploring nonlinear dynamics and network structures in Kuramoto systems using machine learning approaches.

Recent advances in machine learning (ML) have facilitated its application to a wide range of systems, from complex to quantum. Reservoir computing algorithms have proven particularly effective for studying nonlinear dynamical systems that exhibit collective behaviors, such as synchronizations and chaotic phenomena, some of which still remain unclear. Here, we apply ML approaches to the Kuramoto model to address several intriguing problems, including identifying the transition point and criticality of a hybrid synchronization transition, predicting future chaotic behaviors, and understanding network structures from chaotic patterns. Our proposed method also has further implications, such as inferring the structure of neural networks from electroencephalogram signals. This study, finally, highlights the potential of ML approaches for advancing our understanding of complex systems.

Effect of maturity stages on the quality indices of wood apple (Feronia limonia) and modeling of its kinetics by applying machine learning approaches

In the present investigation, an inexpensive and non-destructive method was tested for the appropriate maturity classification of wood apple (Feronia limonia). The investigation was conducted to establish the pronounced effect of maturity stages on the growth kinetics, physico-chemical properties, and other quality indices of wood apple. A systematic trend was observed for all the properties namely sphericity, bulk density (g/cm3), true density (g/cm3), pH, total soluble solids TSS (°Brix), titratable acidity (%) and TSS/TA ratio, etc. of the fruit. In contrast, regular changes were also observed in the color properties at various maturity stages of the wood apple. The maturity kinetics was formulated by applying recurrent neural network (RNN) in compliance with K means cluster algorithm. RNN modeling was applied by considering color property (redness value) as input and six maturity indices as the output of the formulated structure. The RNN architecture, 1-6-6 showed the best results for forecasting the wood apple maturity based on color features. Further, based on the results of the K means cluster algorithm, the maturity stages were classified into three main categories, illustrated in the form of a simplified color chart. Hence, this investigation can be useful for proper control and identification of wood apple maturity during the processing.

Applied machine learning in hematopathology.

An increasing number of machine learning applications are being developed and applied to digital pathology, including hematopathology. The goal of these modern computerized tools is often to support diagnostic workflows by extracting and summarizing information from multiple data sources, including digital images of human tissue. Hematopathology is inherently multimodal and can serve as an ideal case study for machine learning applications. However, hematopathology also poses unique challenges compared to other pathology subspecialities when applying machine learning approaches. By modeling the pathologist workflow and thinking process, machine learning algorithms may be designed to address practical and tangible problems in hematopathology. In this article, we discuss the current trends in machine learning in hematopathology. We review currently available machine learning enabled medical devices supporting hematopathology workflows. We then explore current machine learning research trends of the field with a focus on bone marrow cytology and histopathology, and how adoption of new machine learning tools may be enabled through the transition to digital pathology.

Molecular classification and identification of an aggressive signature in low-grade B-cell lymphomas.

Non-follicular low-grade B-cell lymphomas (LGBCL) are biologically diverse entities that share clinical and histologic features that make definitive pathologic categorization challenging. While most patients with LGBCL have an indolent course, some experience aggressive disease, highlighting additional heterogeneity across these subtypes. To investigate the potential for shared biology across subtypes, we performed RNA sequencing and applied machine learning approaches that identified five clusters of patients that grouped independently of subtype. One cluster was characterized by inferior outcome, upregulation of cell cycle genes, and increased tumor immune cell content. Integration of whole exome sequencing identified novel LGBCL mutations and enrichment of TNFAIP3 and BCL2 alterations in the poor survival cluster. Building on this, we further refined a transcriptomic signature associated with early clinical failure in two independent cohorts. Taken together, this study identifies unique clusters of LGBCL defined by novel gene expression signatures and immune profiles associated with outcome across diagnostic subtypes.

Investigation and prediction of users' sentiment toward food delivery apps applying machine learning approaches

PurposeThe goal of the current research is to use different machine learning (ML) approaches to examine and predict customer reviews of food delivery apps (FDAs).Design/methodology/approachUsing Google Play Scraper, data from five food delivery service providers were collected from the Google Play store. Following cleaning the reviews, the filtered texts were classified as having negative, positive, or neutral sentiments, which were then scored using two unsupervised sentiment algorithms (AFINN and Valence Aware Dictionary for sentiment Reasoning (VADER)). Furthermore, the authors employed four ML approaches to categorize each review of FDAs into the respective sentiment class.FindingsAccording to the study's findings, the majority of customer reviews of FDAs were positive. This research also revealed that, while all of the methods (decision tree, linear support vector machine, random forest classifier and logistic regression) can appropriately classify the reviews into a sentiment category, support vector machines (SVM) beats the others in terms of model accuracy. The authors' study also showed that logistic regression provided the highest recall, F1 score and lowest Root Mean Square Error (RMSE) among the four ML models.Practical implicationsThe findings aid FDAs in determining customer review behavior. The study's findings could help food apps developers better understand how customers feel about the developers' products and services. The food apps developer can learn how to use ML techniques to better understand the users' behavior.Originality/valueThe current study uses ML methodologies to investigate and predict consumer attitude regarding FDAs.