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Development of a novel sustainable, portable, fast, and non-invasive platform based on ATR-FTIR technology coupled with machine learning algorithms for Helicobacter pylori detection in human saliva

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TALANTA OPEN 2024年 10卷

作者： Honorio-Silva, Ghabriel Guevara-Vega, Marco Silva, Nagela Bernadelli Sousa Garcia-Junior, Marcelo Augusto Alves, Deborah Cristina Teixeira Goulart, Luiz Ricardo Martins, Mario Machado Oliveira, Andre Luiz Vitorino, Rui Miguel Pinheiro Cunha, Thulio Marquez Martins, Carlos Henrique Gomes Carneiro, Murillo Guimaraes Sabino-Silva, Robinson Univ Fed Uberlandia Inst Biomed Sci Innovat Ctr Salivary Diagnost & Nanobiotechnol Dept Physiol Uberlandia MG Brazil Univ Fed Uberlandia Inst Biomed Sci Antimicrobial Testing Lab Uberlandia MG Brazil Univ Fed Uberlandia Inst Biotechnol Uberlandia MG Brazil Univ Fed Uberlandia Sch Med Uberlandia MG Brazil Univ Aveiro Dept Med Sci Aveiro Portugal Univ Fed Uberlandia Fac Comp Uberlandia MG Brazil

Helicobacter pylori (H. pylori) infection can increase the risk of peptic ulcers and gastric neoplasms. H. pylori detection in gastric epithelial tissue collected by esophagogastroduodenoscopy (EGD) is an invasive, costly, and stands as an invasive and examiner-dependent procedure necessitating suitable sedation. complex execution procedure, reducing access for isolated populations. H. pylori detection by Urea Breath Test (UBT) presents high outlay cost with limited access in low- and middle-income countries. In this context, it is critical to develop novel alternative non-invasive platforms for the portable, fast, accessible through self-collection and reagent-free detection of H. pylori. Here, we used attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) supported by machine learning algorithms to identify infrared vibrational modes of H. pylori diluted in human saliva. To perform it, saliva was diluted in 4 different concentrations (108 CFU/mL, 107CFU/mL, 106 CFU/mL, and 105 CFU/mL) of H. pylori. Then, diluted saliva with or without H. pylori were applied to ATR-FTIR spectroscopy to perform a reagent-free, fast, and sustainable analysis of spectral signatures to identify unique vibrational modes to identify this pathogen. The obtained spectra were applied to Linear Discriminant Analysis (LDA) and Support Vector machine (SVM) algorithms to perform the H. pylori detection. The results indicate that the method was highly accurate between 108- 105 CFU/mL, achieving an accuracy of 89 % for 108 CFU/mL, 93 % for 107CFU/mL, 94 % for 106 CFU/mL, and 85 % for 105 CFU/mL with SVM algorithm. This proof-of-concept study demonstrates the significant potential of a biophotonic platform supported by artificial intelligence for the non-invasive detection of H. pylori in human saliva samples obtained by self-collection, without the use of reagents. The data reveal that this proof-of-concept study has significant potential for the non-invasive detection of

关键词： ATR-FTIR Helicobacter pylori Gastrointestinal infection Screening test Salivary detection Saliva non-invasive test machine learning algorithms

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Predictive Modeling for ***. Nursing Placement Using machine learning algorithms 3

Predictive Modeling for ***. Nursing Placement Using Machine...

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International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)

作者： Chavan, Ranjana Dumbre, Dipali Devi, Seeta Symbiosis Int Deemed Univ SIU Pune Symbiosis Coll Nursing SCON Pune Maharashtra India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

The demand for well-trained nursing professionals is ever-increasing, necessitating efficient placement processes post-graduation. This study explores the application of machine learning (ML) algorithms to predict ***. Nursing placement outcomes, aiming to optimize workforce management and graduate placement efficiency. Traditional placement methods often suffer from subjectivity and inefficiency. Leveraging historical placement data and relevant features, ML algorithms discern patterns to forecast individual placement likelihood accurately. Various ML techniques including logistic regression, decision trees, support vector machines, and ensemble methods are employed and evaluated for their predictive efficacy. The CN2 rule inducer model emerges as the top-performing model, demonstrating superior discriminatory ability and accuracy. These findings offer significant implications for nursing education institutions and healthcare organizations, facilitating better alignment of education with workforce demands. However, the study also emphasizes the importance of balancing model performance with interpretability. Further research and implementation efforts are warranted to refine predictive modeling approaches and seamlessly integrate them into nursing education and workforce management practices, ultimately contributing to the advancement of nursing practice and patient care delivery.

关键词： Placement Nursing students Prediction Artificial Intelligence machine learning algorithms

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A comparative analysis of prognostic regression models and machine learning algorithms in surgical decision-making of cardial submucosal tumors

Gastroenterology and Endoscopy

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Gastroenterology and Endoscopy 2024年第1期2卷 19-24页

作者： Geng, Zi-Han Zhu, Yan Fu, Pei-Yao Qu, Yi-Fan Li, Quan-Lin Zhou, Ping-Hong Endoscopy Center and Endoscopy Research Institute Zhongshan Hospital Fudan University Shanghai China

Background and aims: Non-tunneling and submucosal tunneling endoscopic resection (STER) techniques are the most frequent treatments for cardial submucosal tumor (SMT). Here, we analyzed common machine learning (ML) algorithms and compared them with traditional regression models in surgical decision-making for cardial SMTs. Methods: Using key baseline predictive factors, ML algorithms and logistic regression (LR) were conducted in 246 patients. For the ML algorithms, gradient boosting machines (GBM), artificial neural networks (ANN), random forests (RF), and support vector machines (SVM), were included. For small sample-sized data, a technique for k-fold cross-validation was exploited to avoid over-fitting. Meanwhile, we tuned the parameters through several replications. Then, we quantified the discrimination (area under the curve, AUC) and predictive ability (Brier score, F1 score, specificity, sensitivity, and accuracy) of models. We divided patients (n = 246) into STER-treated (n = 97) and non-tunneling endoscopic resection (NTER)-treated (n = 149) groups. Results: LR outperformed among all groups (Brier score = 0.1398, F1 score = 0.7391, AUC = 0.8729, and predictive accuracy = 80.65 %). In comparison to ML algorithms, an outperformance of the traditional regression approach was also found in a low-dimensional setting for surgical decision prediction of cardial SMTs. Conclusions: The traditional regression approach outperformed ML algorithms for the prediction of the best surgical method in patients with SMTs. © 2024 The Authors

关键词： Cardial submucosal tumors Logistic regression machine learning algorithms Surgical decision-making

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Malware Detection and Classification with machine learning algorithms 1

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Kumar, R. Vinoth Islam, Md Mojahidul Apon, Abir Hossain Prantha, C. S. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Comp Sci & Engn Chennai Tamil Nadu India

ISBN: (数字)9789819713134

ISBN: (纸本)9789819713127;9789819713134

The Windows platform can be easily affected by abnormal growth of malicious software, so we need to find abnormal activities in the system and vary them effectively. Manually finding an abnormal growth of activities in the system is not possible at all times and takes time. We need to assign humans to monitor activities. To solve this problem, we have to develop an automated system for finding abnormal activities at all times without human interaction. We applied several kinds of techniques involving machine learning to quickly determine whether the algorithm models were effective and detect any abnormal system growth. machine learning methods such as XGBoost, Naive Bayes, K-NN, Random Forest, decision tree, logistic regression, and Support Vector machine have been utilized to detect abnormal growth in the system. We found that the XGBoost classification algorithm performs the most accurate classification among the algorithms tested. At the end of the testing, a 99.28% accurate prediction was obtained.

关键词： Malicious software machine learning algorithms XGBoost

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Predictive Analytics for Inventory Management in E-commerce Using machine learning algorithms 3

Predictive Analytics for Inventory Management in E-commerce ...

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International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)

作者： Manoharan, Geetha Sharma, Anupama Vani, V. Divya Raj, Vijilius Helena Jain, Rishabh Nijhawan, Ginni SR Univ Sch Business Warangal Telangana India BK BirlaInst Engn & Technol Dept Math Pilani 333031 Rajasthan India Inst Aeronaut Engn Hyderabad India NewHorizon Coll Engn Dept Appl Sci Bangalore Karnataka India Constituentof Symbiosis Int Univ Symbiosis Ctr Management Studies Plot 47 &amp 48Block AInd AreaSect 62 Noida Uttar Pradesh 20130 India Lovely Profess Univ Phagwara India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

A description of the abstract for the research paper titled "Predictive Analytics for Inventory Management in E-commerce Using machine learning algorithms" is as follows: In this research, a novel strategy for implementing force operations in e-commerce is presented. This strategy involves the utilization of predictive analytics and machine literacy algorithms. When it comes to the ever-changing landscape of online retail, efficient force operation is necessary to satisfy customer demand while simultaneously minimizing the costs that are connected with overstocking and stockouts. To predict future demand for specific products, prophetic analytics methods are utilized, which involve the utilization of literal deal data in addition to other relevant elements. To create accurate prophetic models that are capable of locating complicated patterns and trends in the data, machine literacy methods such as arbitrary timbers, support vector machines, and neural networks are utilized. Through the incorporation of these prophetic models into the process of force operation, e-commerce businesses canoptimize force circumstances, expedite operations, and improve customer happiness. In addition to making a contribution to the expanding body of literature on data-driven approaches to force operation, this investigation demonstrates the potential for predictive analytics and machine literacy to be utilized in the process of tackling the specific issues that are associated with e-commerce.

关键词： Predictive Analytics Inventory Management E-commerce machine learning algorithms Inventory Forecasting Data-driven Decision Making Supply Chain Optimization

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Particle Swarm Optimization-Based machine learning algorithms for Developing the Modified Proctor Compaction Parameter Prediction Software

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TRANSPORTATION INFRASTRUCTURE GEOTECHNOLOGY 2024年第4期11卷 1492-1519页

作者： Verma, Gaurav Kumar, Brind Ransinchung, R. N. G. D. Indian Inst Technol BHU Dept Civil Engn Varanasi 221005 Uttar Pradesh India Indian Inst Technol Dept Civil Engn Roorkee 247667 Uttarakhand India

Maximum dry density (MDD) and optimum moisture content (OMC) are two significant compaction criteria, especially for quality control and design engineers. Estimating laboratory proctor compaction test is rigorous, time-consuming, and expensive, hindering projects with limited budgets and tight schedules. This study proposed the novel application of hybrid particle swarm optimization (PSO) optimized Gaussian process regression (GPR), K-nearest neighbor (KNN), random forest (RF), and extreme gradient boosting (XGB) algorithms for predicting the soil compaction parameters. Analyzing 2148 in situ soil samples from various geological locations established the maximum proficiency of the XGB algorithm followed by KNN, GPR, and RF in MDD, whereas XGB, KNN, RF, and GPR in OMC. Furthermore, the level 1 and level 2 validation results ascertain the robustness of models in predicting MDD and OMC on different geological location datasets. Eventually, the AI-based computer software developed through this study offers reliable and efficient predictions for civil engineers.

关键词： machine learning algorithms Hyperparameter optimization Computer software Modified compaction parameters Borrow materials

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Predicting the use of chatbot systems in education: a comparative approach using PLS-SEM and machine learning algorithms

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CURRENT PSYCHOLOGY 2024年第28期43卷 23656-23674页

作者： Durak, Hatice Yildiz Onan, Aytug Necmettin Erbakan Univ Eregli Fac Educ Dept Educ Sci Konya Turkiye Izmir Katip Celebi Univ Dept Comp Engn Izmir Turkiye

Adopting innovations in educational practice is a challenging task. In order to promote the use of technological innovations, acceptance of the technology by potential users is a prerequisite. Indeed, understanding the various factors that influence technology acceptance is critical for technology acceptance research. The use and acceptance of chatbots in education as a technological innovation is a topic that needs to be investigated. Chatbots, which offer close to human interaction between the user and technology through text and voice, can provide significant benefits in educational environments. The UTAUT2 model (extending UTAUT), which is widely used to evaluate technology acceptance, can serve as a framework for evaluating the acceptance and use of chatbots. This study aims to predict factors influencing students' use of chatbots in education within the UTAUT2 framework. PLS-SEM and machine learning tested the model, involving 926 students. According to the findings of the study, behavioral intentions were influenced by various factors including performance expectations and attitudes. Facilitating conditions and intentions significantly impacted chatbot usage time. Moderator effects were observed with age, gender, and usage experience affecting behavioral intentions. Support vector machine and logistic regression showed high prediction accuracies for behavioral intentions and usage time, respectively. These results provide insights for chatbot designers to meet user needs in educational settings.

关键词： Chatbot technology UTAUT2 Educational chatbot usage Usage intensity PLS-SEM machine learning algorithms

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Comparative Analysis of Transformer Fault Classification Based on DGA Data Using machine learning algorithms 6

Comparative Analysis of Transformer Fault Classification Bas...

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6th Global Power, Energy and Communication Conference (GPECOM)

作者： Coban, Melih Fidan, Murat Aytar, Oktay Bolu Abant Izzet Baysal Univ Electelect Engn Dept Bolu Turkiye

ISBN: (纸本)9798350351088;9798350351095

Dissolved gas analysis (DGA) is considered a leading technique for fault classification in power transformers. However, accurate analysis results can only be achieved if the measured gases are interpreted, appropriately. In DGA interpretation, traditional techniques, artificial intelligence techniques such as machine learning algorithms, and hybrid techniques are generally used. In this study, four well-known machine learning algorithms have been compared in terms of DGA fault classification: Support Vector machine (SVM), K-Nearest Neighbor (KNN), Naive Bayes (NB) and Decision Tree (DT). The lowest accuracy rate was obtained as 63.63% using the NB algorithm and raw data. In addition to raw data, data converted to logarithmic form has been also used to develop classification models. The highest accuracy rate was determined as 94.54% using the DT algorithm and logarithmic data. The obtained results have been demonstrated the efficiency and stability of the DT algorithm for transformer fault classification, especially when the data was appropriately preprocessed.

关键词： transformer fault classification machine learning algorithms dissolved gas analysis

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Performance Evaluation of Various machine learning algorithms for Lung Cancer Prediction Using Demographic Data 3rd

Performance Evaluation of Various Machine Learning Algorithm...

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3rd International Conference on Artificial Intelligence and Knowledge Processing (AIKP)

作者： Sharmila, Mulagada Surya Kumar, K. Shiridi Ganie, Shahid Mohammad Hemachandran, K. Rege, Manjeet Woxsen Univ Sch Business Hyderabad 502345 Telangana India Woxsen Univ AI Res Ctr Dept Analyt Hyderabad 502345 India Univ St Thomas Dept Software Engn & Data Sci St Paul MN USA

ISBN: (纸本)9783031686160;9783031686177

Lung cancer is among the top deadly diseases, affecting human beings globally. Therefore, it is crucial to predict and detect this disease as early as possible, allowing the doctors and the patients to take the appropriate and essential actions. Techniques like machine learning can be applied to the same. In this study, we used machine learning to predict cancer in the lungs. We explored five machine learning algorithms, viz., Decision Tree (DT), Support vector machine (SVM), Naive Bayes (NB), Logistic Regression (LR), and Random Forest (RF). A publicly available dataset that contains demographic information of 284 patients with 16 parameters is used to conduct this study. An extensive explorative analysis is performed to improve the quality assessment of the dataset. Among five algorithms, Logistic Regression (LR) exhibited best findings in terms of accuracy, precision, recall, specificity, f1-score, and negative predicted values (NPV). Compared to similar research works, the proposed model achieved better results based on various performance evaluation metrics. The proposed model can be used for other illnesses that have similar symptoms by using transfer learning approach.

关键词： Lung cancer prediction machine learning algorithms Predictive modeling Disease prediction Lifestyle Data

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Analysis of Predictive Parameters in Prediction of the Occurrence of Type 2 Diabetes Using machine learning algorithms 16th

Analysis of Predictive Parameters in Prediction of the Occur...

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Mediterranean Conference on Medical and Biological Engineering and Computing (MEDICON) and International Conference on Medical and Biological Engineering (CMBEBIH)

作者： Hadzalic, Sumeja Obralija, Arnela Becirovic, Seila Kelle, Belma Pehlivanovic Krupalija, Ehlimana Univ Sarajevo Fac Pharm Zmaja Bosne 8 Sarajevo 71000 Bosnia & Herceg Univ Sarajevo Fac Elect Engn Zmaja Bosne 8 Sarajevo 71000 Bosnia & Herceg

ISBN: (纸本)9783031490613;9783031490620

Type 2 diabetes is one of the most common chronic non-infectious diseases in the world's developed countries. It is a metabolic disease characterized by hyperglycemia (fasting glycemia >7,0 mmol/l, or more than 11,1 mmol/l 2 h after a meal). The generally accepted definition of type 2 diabetes is a progressive, degenerative, chronic disease, a disorder of glucose metabolism caused by insufficient pancreatic function and/or, most often, insensitivity of cells to insulin. This paper presents the development of a predictive model for the risk of occurrence of type 2 diabetes based on diagnostic parameters. A model has been developed based on 768 samples, 500 healthy individuals, and 268 diagnosed with type 2 diabetes. The model has the following input parameters: plasma glucose concentration, diastolic blood pressure, insulin, BMI (Body mass index), and age. The outcome is presented as a zero for a healthy person or a one for a for developing a classifier for the prediction of diabetes, the following machine learning algorithms were used: Decision tree, Pruned decision tree, kNN, Tuned kNN, SVM, Tuned SVMand Logistic regression. The classification was performed on the original data containing six features with low Pearson correlation coefficients. Data transformation based on principal component analysis was used for improving the accuracy metric values of the ML models. Obtained results were compared and suggested for future use.

关键词： Type 2 diabetes Type 2 diabetes risk machine learning algorithms

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