Diverse Machine Learning Algorithms Research Articles

Content-based Recommender System Using Cosine Similarity

Abstract: In the quest for a more tailored cinematic experience, this paper presents a Movie Recommender System that harnesses the power of Cosine Similarity within a machine learning framework. The system’s cornerstone is its ability to discern nuanced user preferences and suggest films that resonate on a personal level. Employing Python, the research delineates a methodology that encompasses data collection, preprocessing, and feature extraction from a comprehensive dataset of movies. The crux of the system lies in its application of the cosine similarity algorithm, which calculates the affinity between movies and users based on shared characteristics such as genre, director, plot, title and cast. This approach is rigorously evaluated using metrics like precision, recall, and accuracy, ensuring the recommender’s reliability. The experimental design bifurcates the dataset into training and testing subsets, fortifying the system’s robustness. The paper’s findings illuminate the system’s efficacy in delivering precise and individualized recommendations, thereby augmenting the user’s movie selection process. It underscores the potential of the system to revolutionize content discovery and consumption in the entertainment domain. The conclusion encapsulates the project’s ambition to refine the recommender system further by integrating diverse machine learning algorithms and scaling its capabilities.

Detection of Various Botnet Attacks Using Machine Learning Techniques

With the rapid growth in the quantity of Internet of Things (IoT) devices linked with the network, there exists a concurrent rise in network attacks, including overwhelming and service disruption incidents. The increasing prevalence of network attacks, such as overwhelming and service denial, poses a threat to IoT devices, leading to network disruptions and service disruption. Detecting these attacks is challenging due to the diverse array of heterogeneous devices in the IoT environment, making traditional rule-based security solutions less effective. Developing optimal security models for diverse device types is challenging. Machine learning (ML) offers an alternative approach, enabling the creation of effective security models by leveraging empirical data specific to each device. We utilize machine learning techniques for the detection of Internet of Things (IoT) attacks. Our focus is on botnet attacks directed at variety of IoT devices. We undertake the development of machine learning-based models tailored to each specific category of device for enhanced security. We utilize the N-BaIoT dataset, which incorporates injected botnet attacks (specifically Gafgyt and Mirai) across diverse IoT device types, including Doorbell, Baby Monitor, Security Camera, and Webcam. We develop models for detecting botnets for each IoT device by utilizing diverse machine learning algorithms. Following model development, we assess the utility of the models with a strong detection F1-score through classification analysis. The novelty of this work lies in crafting a Machine Learning-based framework designed to identify IoT botnet attacks, followed by successful detection of such attacks across diverse IoT devices utilizing this framework. Among the most widely used machine learning algorithms on the NBaIoT dataset, Decision Trees, Random Forests, and K-Nearest Neighbors (KNN) demonstrate superior performance.

Enhancing cervical cancer detection and robust classification through a fusion of deep learning models

Cervical cancer, the second most prevalent cancer affecting women, arises from abnormal cell growth in the cervix, a crucial anatomical structure within the uterus. The significance of early detection cannot be overstated, prompting the use of various screening methods such as Pap smears, colposcopy, and Human Papillomavirus (HPV) testing to identify potential risks and initiate timely intervention. These screening procedures encompass visual inspections, Pap smears, colposcopies, biopsies, and HPV-DNA testing, each demanding the specialized knowledge and skills of experienced physicians and pathologists due to the inherently subjective nature of cancer diagnosis. In response to the imperative for efficient and intelligent screening, this article introduces a groundbreaking methodology that leverages pre-trained deep neural network models, including Alexnet, Resnet-101, Resnet-152, and InceptionV3, for feature extraction. The fine-tuning of these models is accompanied by the integration of diverse machine learning algorithms, with ResNet152 showcasing exceptional performance, achieving an impressive accuracy rate of 98.08%. It is noteworthy that the SIPaKMeD dataset, publicly accessible and utilized in this study, contributes to the transparency and reproducibility of our findings. The proposed hybrid methodology combines aspects of DL and ML for cervical cancer classification. Most intricate and complicated features from images can be extracted through DL. Further various ML algorithms can be implemented on extracted features. This innovative approach not only holds promise for significantly improving cervical cancer detection but also underscores the transformative potential of intelligent automation within the realm of medical diagnostics, paving the way for more accurate and timely interventions.

Unveiling G-protein coupled receptor kinase-5 inhibitors for chronic degenerative diseases: Multilayered prioritization employing explainable machine learning-driven multi-class QSAR, ligand-based pharmacophore and free energy-inspired molecular simulation

GRK5 holds a pivotal role in cellular signaling pathways, with its overexpression in cardiomyocytes, neuronal cells, and tumor cells strongly associated with various chronic degenerative diseases, which highlights the urgent need for potential inhibitors. In this study, multiclass classification-based QSAR models were developed using diverse machine learning algorithms. These models were built from curated compounds with experimentally derived GRK5 inhibitory activity. Additionally, a pharmacophore model was constructed using active compounds from the dataset. Among the models, the SVM-based approach proved most effective and was initially used to screen DrugBank compounds within the applicability domain. Compounds showing significant GRK5 inhibitory potential underwent evaluation for key pharmacophoric features. Prospective compounds were subjected to molecular docking to assess binding affinity towards GRK5's key active site amino acid residues. Stability at the binding site was analyzed through 200 ns molecular dynamics simulations. MM-GBSA analysis quantified individual free energy components contributing to the total binding energy with respect to binding site residues. Metadynamics analysis, including PCA, FEL, and PDF, provided crucial insights into conformational changes of both apo and holo forms of GRK5 at defined energy states. The study identifies DB02844 (S-Adenosyl-1,8-Diamino-3-Thiooctane) and DB13155 (Esculin) as promising GRK5 inhibitors, warranting further in vitro and in vivo validation studies.

Intelligent Lighting Control Systems for Energy Savings in Hospital Buildings Using Artificial Neural Networks

Lighting control systems are essential in modern building automation and smart homes, efficiently managing illumination to enhance energy conservation and user comfort. This project tackles energy consumption challenges in hospital buildings by introducing Intelligent Lighting Control Systems (ILCS) that take natural light and occupancy into account, driven by Artificial Neural Networks (ANN) and diverse machine learning algorithms. In our study, we collected sensor data, processed it, and designed a lighting control system employing a feedforward neural network and various machine learning algorithms. Surprisingly, our research found that a linear regression algorithm surpassed the ANN-based system in this context. We implemented a prototype, tested it on hardware, and obtained the expected results. This research marks progress towards optimizing energy use in hospital buildings and contributing to sustainability endeavors. By combining ILCS and machine learning, it offers a promising approach for more efficient and eco-friendly lighting systems

Exploring post-COVID-19 health effects and features with advanced machine learning techniques

COVID-19 is an infectious respiratory disease that has had a significant impact, resulting in a range of outcomes including recovery, continued health issues, and the loss of life. Among those who have recovered, many experience negative health effects, particularly influenced by demographic factors such as gender and age, as well as physiological and neurological factors like sleep patterns, emotional states, anxiety, and memory. This research aims to explore various health factors affecting different demographic profiles and establish significant correlations among physiological and neurological factors in the post-COVID-19 state. To achieve these objectives, we have identified the post-COVID-19 health factors and based on these factors survey data were collected from COVID-recovered patients in Bangladesh. Employing diverse machine learning algorithms, we utilised the best prediction model for post-COVID-19 factors. Initial findings from statistical analysis were further validated using Chi-square to demonstrate significant relationships among these elements. Additionally, Pearson’s coefficient was utilized to indicate positive or negative associations among various physiological and neurological factors in the post-COVID-19 state. Finally, we determined the most effective machine learning model and identified key features using analytical methods such as the Gini Index, Feature Coefficients, Information Gain, and SHAP Value Assessment. And found that the Decision Tree model excelled in identifying crucial features while predicting the extent of post-COVID-19 impact.

Delving into Machine Learning's Influence on Disease Diagnosis and Prediction

Introduction/ Background Medical diagnoses have increasingly depended on digitized images obtained through cutting-edge technology. These algorithms offer a promising avenue to transform diagnostic processes in healthcare, with their application scope continually widening due to ongoing advancements. This paper explores machine learning's role in clinical analysis and prediction, examining various studies that apply these techniques in clinical diagnosis, focusing on their use in analyzing images and providing accurate diagnoses. Materials and Methods This study employs a comparative analysis approach, utilizing diverse machine learning algorithms like SVM, K-nearest neighbors, Random Forests, and Decision Trees to analyze digitized medical images and patient records. We extracted data from several medical databases, ensuring a varied and comprehensive dataset. We also evaluated the impact of different data characteristics on the algorithms' effectiveness, aiming to understand the variability in their diagnostic precision across various conditions. Results The results indicate that machine learning algorithms, particularly SVM, K-nearest neighbors, Random Forests, and Decision Trees, demonstrate significant accuracy in diagnosing diseases from digitized images and medical records. SVM and Random Forests showed particularly high effectiveness in clinical diagnosis, suggesting their robustness across different medical conditions and datasets. These findings underscore the potential of machine learning to enhance diagnostic precision and predict illnesses early, aligning with the growing trend of technology-driven medical diagnostics. Discussion The findings reinforce the pivotal role of machine learning in transforming medical diagnostics. The variability in algorithm performance highlights the necessity for tailored approaches, considering dataset specifics and the medical condition being diagnosed. This study underscores the potential of machine learning to enhance diagnostic accuracy, yet it also emphasizes the need for continuous refinement and understanding of the underlying factors affecting algorithm performance. Future research should focus on optimizing these algorithms within diverse clinical settings to fully harness their diagnostic capabilities. Conclusion This study highlights the transformative potential of machine learning in medical diagnostics, demonstrating how various algorithms can effectively analyze digitized images and patient records to diagnose diseases. While the performance of these algorithms varies based on dataset characteristics, the overall high accuracy underscores machine learning's promise in healthcare. As the field continues to evolve, machine learning is poised to become an integral part of clinical diagnosis, enhancing the accuracy and efficiency of medical evaluations and treatments.

Evaluation of traditional and machine learning approaches for modeling volatile fatty acid concentrations in anaerobic digestion of sludge: potential and challenges.

This study evaluates models for predicting volatile fatty acid (VFA) concentrations in sludge processing, ranging from classical statistical methods (Gaussian and Surge) to diverse machine learning algorithms (MLAs) such as Decision Tree, XGBoost, CatBoost, LightGBM, Multiple linear regression (MLR), Support vector regression (SVR), AdaBoost, and GradientBoosting. Anaerobic bio-methane potential tests were carried out using domestic wastewater treatment primary and secondary sludge. The tests were monitored over 40days for variations in pH and VFA concentrations under different experimental conditions. The data observed was compared to predictions from the Gaussian and Surge models, and the MLAs. Based on correlation analysis using basic statistics and regression, the Gaussian model appears to be a consistent performer, with high R2 values and low RMSE, favoring precision in forecasting VFA concentrations. The Surge model, on the other hand, albeit having a high R2, has high prediction errors, especially in dynamic VFA concentration settings. Among the MLAs, Decision Tree and XGBoost excel at predicting complicated patterns, albeit with overfitting issues. This study provides insights underlining the need for context-specific considerations when selecting models for accurate VFA forecasts. Real-time data monitoring and collaborative data sharing are required to improve the reliability of VFA prediction models in AD processes, opening the way for breakthroughs in environmental sustainability and bioprocessing applications.

Optimizing hydrogen evolution prediction: A unified approach using random forests, lightGBM, and Bagging Regressor ensemble model

Hydrogen, as a clean and versatile energy carrier, plays a pivotal role in addressing global energy challenges and transitioning towards sustainable energy systems. This study explores the convergence of machine learning (ML) for photocatalytic hydrogen evolution from sucrose solution using perovskite-type catalysts, namely LaFeO3 (LFO) and graphene-supported LaFeO3 (GLFO). This study pioneers the practical application of ML techniques, including Random Forests, LightGBM, and Bagging Regressor, to predict hydrogen yields in the presence of these photocatalysts. LFO and GLFO underwent a thorough characterization study to validate their successful preparation. Noteworthy, the highest hydrogen yield from the sucrose model solution was achieved using GLFO as 3.52 mmol/gcat. The optimum reaction conditions were experimentally found to be pH = 5.25, 0.15 g/L of catalyst amount, and 7.5 mM of HPC (hydrogen peroxide concentration). A pivotal contribution of this research lies in the practical application of ML models, culminating in the development of an ensemble model. This collaborative approach not only achieved an overall R2 of 0.92 but also demonstrated exceptional precision, as reflected in remarkably low error metrics. The mean squared logarithmic error (MSLE) was 0.0032, and the mean absolute error (MAE) was 0.049, underscoring the effectiveness of integrating diverse ML algorithms. This study advances both the understanding of photocatalytic hydrogen evolution and the practical implementation of ML in predicting intricate chemical reactions.

Machine Learning-Based Prediction Methods for Home Burglary Crimes

In today’s rapidly evolving society, as technology continues to advance, various new forms and methods of crime emerge incessantly. It becomes particularly crucial to accurately predict future criminal behaviors. This paper delves into the study of forecasting home burglary crimes in the realm of property-related offenses. Utilizing a dataset of criminal cases, relevant variables with high correlation to crime prediction are selected as features. Through employing diverse machine learning algorithms, the likelihood of the occurrence of home burglary crimes is forecasted. Consequently, a crime prediction model specifically tailored for home burglary cases is constructed, and the accuracy of the model is evaluated. By using the accuracy of the model as the benchmark, the optimal crime prediction model is chosen, and a system is implemented for building and evaulating the model. Experimental results demonstrate that the developed crime prediction model is capable of effectively foreseeing home burglary crimes, thereby providing valuable support and scientific evidence for the prevention and handling of such criminal cases.

Paper-based fluorescence sensor array with functionalized carbon quantum dots for bacterial discrimination using a machine learning algorithm

The rapid discrimination of bacteria is currently an emerging trend in the fields of food safety, medical detection, and environmental observation. Traditional methods often require lengthy culturing processes, specialized analytical equipment, and bacterial recognition receptors. In response to this need, we have developed a paper-based fluorescence sensor array platform for identifying different bacteria. The sensor array is based on three unique carbon quantum dots (CQDs) as sensing units, each modified with a different antibiotic (polymyxin B, ampicillin, and gentamicin). These antibiotic-modified CQDs can aggregate on the bacterial surface, triggering aggregation-induced fluorescence quenching. The sensor array exhibits varying fluorescent responses to different bacterial species. To achieve low-cost and portable detection, CQDs were formulated into fluorescent ink and used with an inkjet printer to manufacture paper-based sensor arrays. A smartphone was used to collect the responses generated by the bacteria and platform. Diverse machine learning algorithms were utilized to discriminate bacterial types. Our findings showcase the platform's remarkable capability to differentiate among five bacterial strains, within a detection range spanning from 1.0 × 103 CFU/mL to 1.0 × 107 CFU/mL. Its practicality is further validated through the accurate identification of blind bacterial samples. With its cost-effectiveness, ease of fabrication, and high degree of integration, this platform holds significant promise for on-site detection of diverse bacteria.Graphical abstract

Implementation of Credit Card Fraud Detection

The issue of credit card fraud presents a notable concern within the financial sector, leading to considerable finacial losses for both financial institutions and consumers. To address this challenge, this study investigates the application of machine learning methods for detecting credit card fraud. We explore the performance of diverse machine learning algorithms on an actual dataset and suggest an ensemble-based approach that harnesses the strengths of multiple models. Our experimental outcomes demonstrate the effectiveness of machine learning in accurately identifying fraudulent transactions while minimizing false positives.

Prediction of attention deficit hyperactivity disorder based on explainable artificial intelligence

Accurate assessment of Attention Deficit Hyperactivity Disorder (ADHD) is crucial for the effective treatment of affected individuals. Traditionally, psychometric tests such as the WISC-IV have been utilized to gather evidence and identify patterns or factors contributing to ADHD diagnosis. However, in recent years, the use of machine learning (ML) models in conjunction with post-hoc eXplainable Artificial Intelligence (XAI) techniques has improved our ability to make precise predictions and provide transparent explanations. The objective of this study is twofold: firstly, to predict the likelihood of an individual receiving an ADHD diagnosis using ML algorithms, and secondly, to offer interpretable insights into the decision-making process of the ML model. The dataset under scrutiny comprises 694 cases collected over the past decade in Spain, including information on age, gender, and WISC-IV test scores. The outcome variable is the professional diagnosis. Diverse ML algorithms representing various learning styles were rigorously evaluated through a stratified 10-fold cross-validation, with performance assessed using key metrics, including accuracy, area under the receiver operating characteristic curve, sensitivity, and specificity. Models were compared using both the full set of initial features and a well-suited wrapper-type feature selection algorithm (Boruta). Following the identification of the most suitable model, Shapley additive values were computed to assign weights to each predictor based on their additive contribution to the outcome and to elucidate the predictions. Strikingly, a reduced set of 8 out of the initial 20 variables produced results comparable to using the full feature set. Among the ML models tested, the Random Forest algorithm outperformed others on most metrics (ACC = 0.90, AUC = 0.94, Sensitivity = 0.91, Specificity = 0.92). Notably, the principal predictors, ranked by importance, included GAI – CPI, WMI, CPI, PSI, VCI, WMI – PSI, PRI, and LN. Individual case examples exhibit variations in predictions depending on unique characteristics, including instances of false positives and negatives. Our ML model adeptly predicted ADHD diagnoses in 90% of cases, with potential for further enhancement by expanding our database. Furthermore, the use of XAI techniques enables the elucidation of salient factors in individual cases, thereby aiding inexperienced professionals in the diagnostic process and facilitating comparison with expert assessments. It is important to note that this tool is designed to support the ADHD diagnostic process, where the medical professional always has the final say in decision-making.

Comparative analysis of different rainfall prediction models: A case study of Aligarh City, India

This research paper delves into creating and comparing rainfall prediction models, employing diverse machine learning algorithms, including Logistic Regression, Decision Tree Classifier, Multi-Layer Perceptron classifier (neural network), and Random Forest. The study aims not only to predict rainfall patterns but also to evaluate the performance of each model through metrics such as Accuracy, Cohen's kappa coefficient, and Receiver Operating Characteristic (ROC) curve analysis. Additionally, the relevance of the predictors employed in each model is thoroughly assessed. The results of extensive experimentation and analysis reveal that the Logistic Regression (Accuracy = 82.80 %, ROC = 82.45 %, Cohen's Kappa = 65.05 %) and Neural Network model (Accuracy = 82.59 %, ROC = 81.94 %, Cohen's Kappa = 64.40 %) has emerged as the most promising approach, achieving the highest percentage of accuracy, ROC and Cohen's Kappa metrics; among the models considered. This outcome underscores the effectiveness of Logistic Regression and Neural Network architectures in capturing intricate patterns and relationships within rainfall data.

AI-powered in the digital age: Ensemble innovation personalizes the food recommendations

This study proposes and evaluates a novel approach utilizing ensemble machine learning techniques for personalized meal services to address a critical gap in understanding AI-powered decision-making within the food delivery and restaurant industry. We draw inspiration from diverse fields, including non-traditional simulation methodologies and open innovation dynamics, to create a framework that leverages the combined strengths of individual algorithms. Three machine learning algorithms – decision trees, logistic regression, and naïve Bayes – are rigorously evaluated for their efficacy in classifying and assigning algorithms within an ensemble model for a new service. A simulated dataset, informed by expert tagging, is the training ground, ensuring practical relevance. We employ the voting probability metric on a held-out test set to provide a robust measure of accuracy in this critical task. Our findings reveal the significant potential of AI-powered personalized meal services. Ensemble models demonstrate high accuracy, showcasing the collaboration of combining individual algorithms. This originality lies in applying ensemble techniques to a business case with far-reaching implications for management and societal well-being. Beyond technical success, we explore this technology’s broader impact. AI-powered food recommendations can enhance accessibility for individuals with dietary needs, promote healthier lifestyles through nutritious meal suggestions, and generate new job opportunities. Acknowledging limitations and future research avenues, we invite further exploration of diverse machine learning algorithms and broader applications across various domains.

Predicting Thermal Preferences - A Comparative Analysis of Machine Learning Algorithms using ASHRAE Global Thermal Comfort Database II

Predicting thermal preferences and ensuring comfort through machine learning is a highly active research field that has attracted significant attention from researchers aiming to achieve accurate forecasting and a deeper understanding of human thermal comfort in buildings. The primary objective of this study is to develop machine learning models for predicting thermal preference using the ASHRAE Global Thermal Comfort Database II. Additionally, the algorithms developed in this study can serve as valuable groundwork for transferring the acquired knowledge to develop personalized machine learning models, thereby enhancing individualized comfort. To enhance the dataset's accuracy and reliability, rigorous data exploration and preprocessing were executed. A comparative analysis of diverse machine learning algorithms was conducted, revealing that ensemble-based methods, namely Random Forest, Extra Trees, LightGBM, CatBoost, Gradient Boosting Machine, and XGBoost, exhibited superior performance in predicting thermal preferences. The accuracy of these ensemble models was further refined through hyperparameter optimization using the Optuna framework. This optimization led to a notable improvement, increased the model accuracy from 65% for traditional machine learning algorithms to 70% for the optimized ensemble algorithms.

Comparison of diverse machine learning algorithms for forest fire susceptibility mapping in Antalya, Türkiye

Antalya is one of the provinces with the highest number of forest fires in Türkiye. In 2021, 278 forest fires occurred within the administrative boundaries of Antalya Regional Directorate of Forestry. The main objective of this study is to produce forest fire susceptibility (FFS) maps of Antalya province using machine learning (ML) models. In addition to forest fire inventory data, 16 factors, including topographic, environmental, meteorological, and human-driven, were used in the study. Inventory data included 2166 fire ignition points from the General Directorate of Forestry. 70 % of the inventory dataset was used to train the ML models and 30 % to validate the models. Overall accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC) approaches were considered as validation metrics. FFS maps of Antalya were produced using stand-alone ML algorithms, K-Nearest Neighbors, and Support Vector Machines, as well as tree-based Conditional Inference Trees (CTREE), Random Forest (RF), Gradient Boosting Machines (GBM), and Extreme Gradient Boosting (XGBoost) algorithms. To the best of our knowledge, this is the first study using the CTREE algorithm for forest fire susceptibility mapping. Therefore, this study is important for the related literature. The validation results revealed that the XGBoost model outperformed other models. It is thought that the FFS map produced using the XGBoost model will guide forest engineers, wildland firefighting teams, and firefighters to minimize damage and control forest fires.

Cervical Cancer Prediction Using Machine Learning

Cervical cancer constitutes a profound global health challenge, affecting a substantial number of women annually. If this cancer is not diagnosed and attended with hypervigilance, it can spread to other body parts, damage tissues, and typically deteriorate the immune system of the body. This ultimately becomes deadly and, in many cases, non-curable. This research paper looks into machine learning used to predict cervical cancer. The 5-year relative survival rate after the spread of the disease is almost 50%. Hence, detecting the tumor in advance can prevent its proliferation and consequently assist in the process of curing the disease. 
 The study emphasizes the global impact of cervical cancer, shedding light on its prevalence and its connection to the human papillomavirus (HPV). In a managerial but approachable manner, this research discusses the factors like weakened immune systems, smoking, and contraceptive use that contribute to the risk of cervical cancer. Four essential diagnostic tests—Hinselmann, Schiller, Citology, and Biopsy—are discussed as integral components of the predictive model. Diverse Machine Learning algorithms used show promise in enhancing accuracy for early cervical cancer prediction. With potential implications for public health, this paper concludes by providing insights into future research directions.

An Extensive Investigation into the Use of Machine Learning Tools and Deep Neural Networks for the Recognition of Skin Cancer: Challenges, Future Directions, and a Comprehensive Review

Skin cancer poses a serious risk to one’s health and can only be effectively treated with early detection. Early identification is critical since skin cancer has a higher fatality rate, and it expands gradually to different areas of the body. The rapid growth of automated diagnosis frameworks has led to the combination of diverse machine learning, deep learning, and computer vision algorithms for detecting clinical samples and atypical skin lesion specimens. Automated methods for recognizing skin cancer that use deep learning techniques are discussed in this article: convolutional neural networks, and, in general, artificial neural networks. The recognition of symmetries is a key point in dealing with the skin cancer image datasets; hence, in developing the appropriate architecture of neural networks, as it can improve the performance and release capacities of the network. The current study emphasizes the need for an automated method to identify skin lesions to reduce the amount of time and effort required for the diagnostic process, as well as the novel aspect of using algorithms based on deep learning for skin lesion detection. The analysis concludes with underlying research directions for the future, which will assist in better addressing the difficulties encountered in human skin cancer recognition. By highlighting the drawbacks and advantages of prior techniques, the authors hope to establish a standard for future analysis in the domain of human skin lesion diagnostics.

A Dual Fluorescence Turn-On Sensor Array Formed by Poly(para-aryleneethynylene) and Aggregation-Induced Emission Fluorophores for Sensitive Multiplexed Bacterial Recognition.

Bacterial infections have emerged as the leading causes of mortality and morbidity worldwide. Herein, we developed a dual-channel fluorescence "turn-on" sensor array, comprising six electrostatic complexes formed from one negatively charged poly(para-aryleneethynylene) (PPE) and six positively charged aggregation-induced emission (AIE) fluorophores. The 6-element array enabled the simultaneous identification of 20 bacteria (OD600=0.005) within 30s (99.0 % accuracy), demonstrating significant advantages over the array constituted by the 7 separate elements that constitute the complexes. Meanwhile, the array realized different mixing ratios and quantitative detection of prevalent bacteria associated with urinary tract infection (UTI). It also excelled in distinguishing six simulated bacteria samples in artificial urine. Remarkably, the limit of detection for E. coli and E. faecalis was notably low, at 0.000295 and 0.000329 (OD600), respectively. Finally, optimized by diverse machine learning algorithms, the designed array achieved 96.7 % accuracy in differentiating UTI clinical samples from healthy individuals using a random forest model, demonstrating the great potential for medical diagnostic applications.