Application Of Machine Learning Tools Research Articles

Optimal Routing in Wireless Sensor Networks for Advancing IoT Efficiency and Sustainability using Enhanced Ant Colony Algorithm with machine learning approaches

This research study aims to investigate the incorporation of machine learning tools, such as Q-learning, Genetic Algorithms, Unsupervised Learning, and Ensemble Learning, into Enhanced Ant Colony Algorithm to assess the impacts of such incorporation on the WSN’s performance. Ten experimental trials were conducted on each to analyze the accuracy, precision, and F1 score results. It was observed that Q-learning achieves an average accuracy of 0.867; precision of 0.842; and F1 score of 0.854, making it highly adaptable and efficient in making routing decisions. The GA presented average accuracy of 0.833; precision of 0.812; and F1 score of 0.821 which show that the tool is highly robust in evolutionary optimization. Unsupervised learning machine performances indicate that the mean accuracy, precision, and F1 score for the model are 0.875, 0.856, and 0.865, respectively . As for ES with multi source models, the model showed the highest performance of 0.898, 0.882, and 0.891 in accuracy, precision, and F1 score, respectively . This study is thus very valuable in the backend regarding application of machine learning tools into routing optimization algorithms and efforts geared towards WSN efficiencies and sustainability..

FDA-Approved Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices: An Updated Landscape

As artificial intelligence (AI) has been highly advancing in the last decade, machine learning (ML)-enabled medical devices are increasingly used in healthcare. In this study, we collected publicly available information on AI/ML-enabled medical devices approved by the FDA in the United States, as of the latest update on 19 October 2023. We performed comprehensive analysis of a total of 691 FDA-approved artificial intelligence and machine learning (AI/ML)-enabled medical devices and offer an in-depth analysis of clearance pathways, approval timeline, regulation type, medical specialty, decision type, recall history, etc. We found a significant surge in approvals since 2018, with clear dominance of the radiology specialty in the application of machine learning tools, attributed to the abundant data from routine clinical data. The study also reveals a reliance on the 510(k)-clearance pathway, emphasizing its basis on substantial equivalence and often bypassing the need for new clinical trials. Also, it notes an underrepresentation of pediatric-focused devices and trials, suggesting an opportunity for expansion in this demographic. Moreover, the geographical limitation of clinical trials, primarily within the United States, points to a need for more globally inclusive trials to encompass diverse patient demographics. This analysis not only maps the current landscape of AI/ML-enabled medical devices but also pinpoints trends, potential gaps, and areas for future exploration, clinical trial practices, and regulatory approaches. In conclusion, our analysis sheds light on the current state of FDA-approved AI/ML-enabled medical devices and prevailing trends, contributing to a wider comprehension.

Simultaneously maximizing microalgal biomass and lipid productivities by machine learning driven modeling, global sensitivity analysis and multi-objective optimization for sustainable biodiesel production

Simultaneous enhancement in the productivities of microalgal biomass and lipids that are inversely correlated to each other has been a long-standing challenge before the scientists engaged in algal biodiesel research and innovation. This study, develops an Artificial Neural Network (ANN) model and uses multi-objective optimization approach to determine the process parameters, namely, light intensity, CO2%, air flow rate, and C/N ratio to maximize biomass and lipid productivities of microalga, Chlorella sorokiniana, simultaneously. Experimental data based on central composite design (CCD) were used to train a feed-forward multilayer ANN with four critical parameters. The global sensitivity analysis was performed on the trained ANN model using Sobol's method to assess the relative significance of the four process variables. Since the objectives of maximizing biomass and lipid productivities conflict with each other, multi-objective optimization approach was used for deriving the optimal process parameters, experimental validation of which resulted in approximately 3 times increase in biomass productivity and 7 times increase in lipid productivity. On transesterification of the lipid, the biodiesel product with saturated to unsaturated fatty acids ratio of 48:52 conformed very well to the international standards, ASTM D6751 and EN14214, thereby making it an environment friendly green biofuel. Thus, the present study showcases the successful application of machine learning tool in analysing the global sensitivity analysis of process variables, and employ it for multi-objective optimization towards achieving maximum microalgal biomass and lipid productivities concomitantly for potentially sustainable production of biodiesel.

Neural Networks for Portfolio Analysis With Cardinality Constraints.

Portfolio analysis is a crucial subject within modern finance. However, the classical Markowitz model, which was awarded the Nobel Prize in Economics in 1991, faces new challenges in contemporary financial environments. Specifically, it fails to consider transaction costs and cardinality constraints, which have become increasingly critical factors, particularly in the era of high-frequency trading. To address these limitations, this research is motivated by the successful application of machine learning tools in various engineering disciplines. In this work, three novel dynamic neural networks are proposed to tackle nonconvex portfolio optimization under the presence of transaction costs and cardinality constraints. The neural dynamics are intentionally designed to exploit the structural characteristics of the problem, and the proposed models are rigorously proven to achieve global convergence. To validate their effectiveness, experimental analysis is conducted using real stock market data of companies listed in the Dow Jones Index (DJI), covering the period from November 8, 2021 to November 8, 2022, encompassing an entire year. The results demonstrate the efficacy of the proposed methods. Notably, the proposed model achieves a substantial reduction in costs (which combines investment risk and reward) by as much as 56.71% compared with portfolios that are averagely selected.

Machine learning identification of Pseudomonas aeruginosa strains from colony image data.

When grown on agar surfaces, microbes can produce distinct multicellular spatial structures called colonies, which contain characteristic sizes, shapes, edges, textures, and degrees of opacity and color. For over one hundred years, researchers have used these morphology cues to classify bacteria and guide more targeted treatment of pathogens. Advances in genome sequencing technology have revolutionized our ability to classify bacterial isolates and while genomic methods are in the ascendancy, morphological characterization of bacterial species has made a resurgence due to increased computing capacities and widespread application of machine learning tools. In this paper, we revisit the topic of colony morphotype on the within-species scale and apply concepts from image processing, computer vision, and deep learning to a dataset of 69 environmental and clinical Pseudomonas aeruginosa strains. We find that colony morphology and complexity under common laboratory conditions is a robust, repeatable phenotype on the level of individual strains, and therefore forms a potential basis for strain classification. We then use a deep convolutional neural network approach with a combination of data augmentation and transfer learning to overcome the typical data starvation problem in biological applications of deep learning. Using a train/validation/test split, our results achieve an average validation accuracy of 92.9% and an average test accuracy of 90.7% for the classification of individual strains. These results indicate that bacterial strains have characteristic visual 'fingerprints' that can serve as the basis of classification on a sub-species level. Our work illustrates the potential of image-based classification of bacterial pathogens and highlights the potential to use similar approaches to predict medically relevant strain characteristics like antibiotic resistance and virulence from colony data.

Machine Learning-Enabled Genome Mining and Bioactivity Prediction of Natural Products.

Natural products (NPs) produced by microorganisms and plants are a major source of drugs, herbicides, and fungicides. Thanks to recent advances in DNA sequencing, bioinformatics, and genome mining tools, a vast amount of data on NP biosynthesis has been generated over the years, which has been increasingly exploited to develop machine learning (ML) tools for NP discovery. In this review, we discuss the latest advances in developing and applying ML tools for exploring the potential NPs that can be encoded by genomic language and predicting the types of bioactivities of NPs. We also examine the technical challenges associated with the development and application of ML tools for NP research.

Application of machine learning tools and integrated OMICS for screening and diagnosis of inborn errors of metabolism

Tandem mass spectrometry (TMS) has emerged an important screening tool for various metabolic disorders in newborns. However, there is inherent risk of false positive outcomes. Objective To establish analyte-specific cutoffs in TMS by integrating metabolomics and genomics data to avoid false positivity and false negativity and improve its clinical utility. TMS was performed on 572 healthy and 3000 referred newborns. Urine organic acid analysis identified 23 types of inborn errors in 99 referred newborns. Whole exome sequencing was performed in 30 positive cases. The impact of physiological changes such as age, gender, and birthweight on various analytes was explored in healthy newborns. Machine learning tools were used to integrate demographic data with metabolomics and genomics data to establish disease-specific cut-offs; identify primary and secondary markers; build classification and regression trees (CART) for better differential diagnosis; for pathway modeling. This integration helped in differentiating B12 deficiency from methylmalonic acidemia (MMA) and propionic acidemia (Phi coefficient=0.93); differentiating transient tyrosinemia from tyrosinemia type 1 (Phi coefficient=1.00); getting clues about the possible molecular defect in MMA to initiate appropriate intervention (Phi coefficient=1.00); to link pathogenicity scores with metabolomics profile in tyrosinemia (r2=0.92). CART model helped in establishing differential diagnosis of urea cycle disorders (Phi coefficient=1.00). Calibrated cut-offs of different analytes in TMS and machine learning-based establishment of disease-specific thresholds of these markers through integrated OMICS have helped in improved differential diagnosis with significant reduction of the false positivity and false negativity rates.

Progress and challenges for the application of machine learning for neglected tropical diseases

Neglected tropical diseases (NTDs) continue to affect the livelihood of individuals in countries in the Southeast Asia and Western Pacific region. These diseases have been long existing and have caused devastating health problems and economic decline to people in low- and middle-income (developing) countries. An estimated 1.7 billion of the world’s population suffer one or more NTDs annually, this puts approximately one in five individuals at risk for NTDs. In addition to health and social impact, NTDs inflict significant financial burden to patients, close relatives, and are responsible for billions of dollars lost in revenue from reduced labor productivity in developing countries alone. There is an urgent need to better improve the control and eradication or elimination efforts towards NTDs. This can be achieved by utilizing machine learning tools to better the surveillance, prediction and detection program, and combat NTDs through the discovery of new therapeutics against these pathogens. This review surveys the current application of machine learning tools for NTDs and the challenges to elevate the state-of-the-art of NTDs surveillance, management, and treatment.

Application of Machine Learning in Water Resources Management: A Systematic Literature Review

In accordance with the rapid proliferation of machine learning (ML) and data management, ML applications have evolved to encompass all engineering disciplines. Owing to the importance of the world’s water supply throughout the rest of this century, much research has been concentrated on the application of ML strategies to integrated water resources management (WRM). Thus, a thorough and well-organized review of that research is required. To accommodate the underlying knowledge and interests of both artificial intelligence (AI) and the unresolved issues of ML in WRM, this overview divides the core fundamentals, major applications, and ongoing issues into two sections. First, the basic applications of ML are categorized into three main groups, prediction, clustering, and reinforcement learning. Moreover, the literature is organized in each field according to new perspectives, and research patterns are indicated so attention can be directed toward where the field is headed. In the second part, the less investigated field of WRM is addressed to provide grounds for future studies. The widespread applications of ML tools are projected to accelerate the formation of sustainable WRM plans over the next decade.

Application of machine learning tools for feature selection in the identification of prognostic markers in COVID-19

Abstract Objective To identify prognostic markers by applying machine learning strategies to the feature selection. Methods An observational, retrospective, multi-center study that included hospitalized patients with a confirmed diagnosis of COVID-19 in three hospitals in Colombia. Eight strategies were applied to select prognostic-related characteristics. Eight logistic regression models were built from each set of variables and the predictive ability of the outcome was evaluated. The primary endpoint was transfer to intensive care or in-hospital death. Results The database consisted of 969 patients of which 486 had complete data. The main outcome occurred in 169 cases. The development database included 220 patients, 137 (62.3%) were men with a median age of 58.2, 39 (17.7%) were diabetic, 62 (28.2%) had high blood pressure, and 32 (14.5%) had chronic lung disease. Thirty-three variables were identified as prognostic markers, and those selected most frequently were: LDH, PaO2/FIO2 ratio, CRP, age, neutrophil and lymphocyte counts, respiratory rate, oxygen saturation, ferritin, and HCO3. The eight logistic regressions developed were validated on 266 patients in whom similar results were reached (accuracy: 65.8–72.9%). Conclusions The combined use of strategies for selecting characteristics through machine learning techniques makes it possible to identify a broad set of prognostic markers in patients hospitalized for COVID-19 for death or hospitalization in intensive care.

Predicting ruthenium catalysed hydrogenation of esters using machine learning

The report describes the application of machine learning tools to predict hydrogenation of esters using molecular catalysts based on ruthenium.

Application of Machine Learning Tools in the Evaluation of the Risk of Falls in the Elderly: An Integrative Review

This integrative review seeks to present an overview of the application of machine learning (ML) tools in the assessment of the risk of falls in the elderly. We searched the CAPES and IEEE Xplore Periodical databases, articles published in English, Portuguese and Spanish, in the last eleven years. Thirteen articles were selected. Most studies use data from sensors to classify the risk of falling and compare the results obtained with results of clinical tests or history of falls. Some studies carried out the selection of characteristics of the collected signals. Research that compared CI tools and conventional scales pointed to a certain superiority of the former. In the selected articles, Multilayer Perceptron (MLP) neural networks were the most explored. It was possible to observe that the ML tools can be applied in the assessment of the risk of falls in the elderly as a classification resource, showing good results.

Regulatory Considerations on the use of Machine Learning based tools in Clinical Trials

BackgroundThe widespread increasing use of machine learning (ML) based tools in clinical trials (CTs) impacts the activities of Regulatory Agencies (RAs) that evaluate the development of investigational medicinal products (IMPs) in clinical studies to be carried out through the use of data-driven technologies. The fast progress in this field poses the need to define new approaches and methods to support an agile and structured assessment process.MethodThe assessment of key information, characteristics and challenges deriving from the application of ML tools in CTs and their link with the principles for a trustworthy artificial intelligence (AI) that directly affect the decision-making process is investigated.ResultsPotential issues are identified during the assessment and areas of greater interaction combining key regulatory points and principles for a trustworthy AI are highlighted. The most impacted areas are those related to technical robustness and safety of the ML tool, in relation to data used and the level of evidence generated. Additional areas of attention emerged, like the ones related to data and algorithm transparency.ConclusionWe evaluate the applicability of a new method to further support the assessment of medicinal products developed using data-driven tools in a CT setting. This is a first step and new paradigms should be adopted to support policy makers and regulatory decisions, capitalizing on technology advancements, considering stakeholders’ feedback and still ensuring a regulatory framework on safety and efficacy.Graphical

Applications of machine learning tools for ultra-sensitive detection of lipoarabinomannan with plasmonic grating biosensors in clinical samples of tuberculosis.

BackgroundTuberculosis is one of the top ten causes of death globally and the leading cause of death from a single infectious agent. Eradicating the Tuberculosis epidemic by 2030 is one of the top United Nations Sustainable Development Goals. Early diagnosis is essential to achieving this goal because it improves individual prognosis and reduces transmission rates of asymptomatic infected. We aim to support this goal by developing rapid and sensitive diagnostics using machine learning algorithms to minimize the need for expert intervention.Methods and findingsA single molecule fluorescence immunosorbent assay was used to detect Tuberculosis biomarker lipoarabinomannan from a set of twenty clinical patient samples and a control set of spiked human urine. Tuberculosis status was separately confirmed by GeneXpert MTB/RIF and cell culture. Two machine learning algorithms, an automatic and a semiautomatic model, were developed and trained by the calibrated lipoarabinomannan titration assay data and then tested against the ground truth patient data. The semiautomatic model differed from the automatic model by an expert review step in the former, which calibrated the lower threshold to determine single molecules from background noise. The semiautomatic model was found to provide 88.89% clinical sensitivity, while the automatic model resulted in 77.78% clinical sensitivity.ConclusionsThe semiautomatic model outperformed the automatic model in clinical sensitivity as a result of the expert intervention applied during calibration and both models vastly outperformed manual expert counting in terms of time-to-detection and completion of analysis. Meanwhile, the clinical sensitivity of the automatic model could be improved significantly with a larger training dataset. In short, semiautomatic, and automatic Gaussian Mixture Models have a place in supporting rapid detection of Tuberculosis in resource-limited settings without sacrificing clinical sensitivity.

Application of Machine Learning Tools for Long-Term Diagnostic Feature Data Segmentation

In this paper, a novel method for long-term data segmentation in the context of machine health prognosis is presented. The purpose of the method is to find borders between three data segments. It is assumed that each segment contains the data that represent different statistical properties, that is, a different model. It is proposed to use a moving window approach, statistical parametrization of the data in the window, and simple clustering techniques. Moreover, it is found that features are highly correlated, so principal component analysis is exploited. We find that the probability density function of the first principal component may be sufficient to find borders between classes. We consider two cases of data distributions, Gaussian and α-stable, belonging to the class of non-Gaussian heavy-tailed distributions. It is shown that for random components with Gaussian distribution, the proposed methodology is very effective, while for the non-Gaussian case, both features and the concept of moving window should be re-considered. Finally, the procedure is tested for real data sets. The results provided here may be helpful in understanding some specific cases of machine health prognosis in the presence of non-Gaussian noise. The proposed approach is model free, and thus it is universal. The methodology can be applied for any long-term data where segmentation is crucial for the data processing.

A survey of deep reinforcement learning application in 5G and beyond network slicing and virtualization

The 5th Generation (5G) and beyond networks are expected to offer huge throughputs, connect large number of devices, support low latency and large numbers of business services. To realize this vision, there is a need for a paradigm shift in the way cellular networks are designed, built, and maintained. Network slicing divides the physical network infrastructure into multiple virtual networks to support diverse business services, enterprise applications and use cases. Multiple services and use cases with varying architectures and quality of service requirements on such shared infrastructure complicates the network environment. Moreover, the dynamic and heterogeneous nature of 5G and beyond networks will exacerbate network management and operations complexity. Inspired by the successful application of machine learning tools in solving complex mobile network decision making problems, deep reinforcement learning (Deep RL) methods provide potential solutions to address slice lifecycle management and operation challenges in 5G and beyond networks. This paper aims to bridge the gap between Deep RL and the 5G network slicing research, by presenting a comprehensive survey of their existing research association. First, the basic concepts of Deep RL framework are presented. 5G network slicing and virtualization principles are then discussed. Thirdly, we review challenges in 5G network slicing and the current research efforts to incorporate Deep RL in addressing them. Lastly, we present open research problems and directions for future research.

Impact on outcomes of mixed chimerism of bone marrow CD34+ sorted cells after matched or haploidentical allogeneic stem cell transplantation for myeloid malignancies.

Allogeneic hematopoietic stem cell transplantation (Allo-HSCT), proposed to patients with high-risk myeloid malignancies, may ultimately fail because of disease relapse. Bone marrow (BM) CD34+ cells in Allo-HSCT recipients can be either re-emerging recipient malignant cells or donor cells attesting of hematopoietic reconstitution. In this context, investigating donor/recipient chimerism in the population of BM CD34+ sorted cells (BM-CD34+SC) was performed in 261 Allo-HSCT recipients (matched n = 145, haploidentical n = 65, matched unrelated n = 51) with myeloid malignancies. BM-CD34+SC chimerism was compared to that of whole peripheral blood (PB) cells as well as other Allo-HSCT-related parameters, and impact on relapse and survival was assessed. Thresholds of 98% donor cells for PB and 90% for BM-CD34+SC were found to allow relapse prediction. This was completed by the application of machine learning tools to explore the predictive value of these parameters in multidimensional models with repeated iterations. BM-CD34+SC mixed chimerism stood out with all these methods as the most robust predictor of relapse with a significant impact on disease-free and overall survivals even after haploidentical Allo-HSCT and/or PTCY administration. This marker therefore appears to be of great interest for the decision of preemptive treatment to avoid post-transplant relapse.

A Machine Learning Model Helps Process Interviewer Comments in Computer-assisted Personal Interview Instruments: A Case Study

During data collection, field interviewers often append notes or comments to a case in open text fields to request updates to case-level data. Processing these comments can improve data quality, but many are non-actionable, and processing remains a costly manual task. This article presents a case study using a novel application of machine learning tools to assist in the evaluation of these comments. Using over 5,000 comments from the Medical Expenditure Panel Survey, we built features that were fed to a machine learning model to predict a grouping category for each comment as previously assigned by data technicians to expedite processing. The model achieved high top-3 accuracy and was incorporated into a production tool for editing. A qualitative evaluation of the tool also provided encouraging results. This application of machine learning tools allowed a small but worthwhile increase in processing efficiency, while maintaining exacting standards for data quality.

Enhancing Orthopedic Surgery and Treatment Using Artificial Intelligence and Its Application in Health and Dietary Welfare

The current decade has seen an increased usage of high-end digital technologies like machine learning in the field of health care services which enable in supporting and performing different functions with less or no human interventions. The application of machine learning tools in the orthopedic area is gaining more popularity as it can support in analyzing the issues in a more comprehensive manner, provide accurate data, support in forecasting the pattern. It enables offering critical information for taking quick decisions by the medical practitioners in order to enhance the health and dietary care service delivery. The ML tools can support in collecting patient centric data related to orthopedic surgery and also estimate the postoperative complications, level of treatment modalities to be provided, and guide the medical practitioners in taking effective clinical device decisions. The ML approach also supports in providing prediction methods of implementing the ortho surgical outcomes. Furthermore, it can also guide in making better treatment procedures, forecast the patterns, and stream the health care management services for better patient recovery. This study implements a quantitative research approach which will support in sourcing the data from the respondents who are currently working as medical practitioners, orthopedic experts, and radiologists who use ML-based models in making critical decisions related to orthopedic surgery. The researchers chose nearly 149 respondents, and the information was analysed using the IBM SPSS package for gaining critical interpretation. The major analyses cover descriptive analysis, regression analysis, and analysis of variances.

Machine Learning Model-Based Applications for Food Management in Alzheimer’s Using Regression Analysis Approach

Alzheimer’s disease (AD) has become a public health concern due to its misinterpretation with vascular dementia (VD) and mixed dementia Alzheimer’s disease (MXD). Therefore, an accurate differentiation of these diseases is essential for improving the treatment procedure. It has been seen that nutrition along with several other factors plays a role in the disease progression. Scientists are trying to find a solution using some machine learning (ML) techniques. The ML algorithms used for this purpose are neural networks, support vector machines, regression and many more. The current research is focused on understanding the extent of the application of machine learning tools in enhancing food management for patients with Alzheimer’s since there is no cure known for the same. A total of 100 patient data have been collected where the patients had AD, VD, and MXD. Their demographic data, dietary intake, Fazekas scores, and Hachinski scores were collected (independent variables) and analysed in IBM SPSS by considering the risk of development of AD, VD, and MXD as dependent variables. The findings showed that age is highly related ( p < 0.001 ) to the development of these three diseases and other demographics are not prioritized. Discussion of other available journal articles showed that nutritional intake, Fazekas scores, Hachinski scores, and gender are also indicators for predicting these diseases ( p < 0.001 ). Thus, this study concluded that age, gender, diet consumption, and Fazekas and Hachinski scores are important indicators for differentiating AD from other diseases, and ML can be used to create a custom nutrition plan based on the patient’s diet and stage of disease progression. Lastly, future scopes of ML have been explained in this paper.