Application Of Machine Learning Tools Research Articles

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.

Institutional pressures as drivers of circular economy in firms: A machine learning approach

This paper investigates how institutional pressures affect the development of Circular Economy (CE) in firms. Using Institutional Entrepreneurship as a theoretical framework, this paper considers three different levels of institutional pressures (coercive, normative, and mimetic) to examine the effect of each pressure and their interactions on the development of CE. Seeking to clarify the debate on the effect of institutional pressures, this paper considers that the main limitation arises from the fact that previous research has analysed the relationship between institutional pressures without considering the interaction between them and the non-linearity of the processes. Deviating from previous papers, our analysis combines regression methods with Machine learning (i.e. Artificial Neural Networks), and employs data from the EU survey on Public Consultation on the Circular Economy. This research finds that while coercive pressures have a compulsory effect on the development of CE, mimetic and normative pressures do not have an effect by themselves, but only in interaction with coercive pressures. Moreover, this paper shows that the application of machine learning tools has an important contribution in solving interaction problems. From the perspective of environmental policy, this means that a comprehensive policy is required, which implies the coexistence or interaction of the three types of pressures.

Application of machine learning tools and immersive technologies in management

The article considers the experience of using machine learning tools and the application of immersive technologies in management: the development areas have been identified, the dynamics of the technologies and application practices implementation has been studied, the risks of their application have been analysed. The study uses general scientific and special methods, comparative analysis and systematisation method. An analysis of the domestic and foreign experience in the application of these technologies in the field of management has been carried out. As a result of the study, the main directions have been identified, within the framework of which, the application of these tools is developed, threats and disadvantages of the application practice of these instruments and technologies have been formulated. It has been concluded that most of the technologies described in the study are able to significantly improve the organisation’s efficiency, identify hidden relationships, more productively manage employees in the field of hiring, selection, personnel training, motivation and development of talents, improving corporate culture, to ensure the organisation’s security, etc. It has been also noted that the application of individual technologies can be used for pressure on employees, invasion of privacy, administrative pressure due to a political position, personal medical information, etc. and requires a balanced policy with the involvement of all interested parties.

Application of machine learning tools: Potential and useful approach for the prediction of type 2 diabetes mellitus based on the gut microbiome profile.

The gut microbiota plays an important role in the regulation of the immune system and the metabolism of the host. The aim of the present study was to characterize the gut microbiota of patients with type 2 diabetes mellitus (T2DM). A total of 118 participants with newly diagnosed T2DM and 89 control subjects were recruited in the present study; six clinical parameters were collected and the quantity of 10 different types of bacteria was assessed in the fecal samples using quantitative PCR. Taking into consideration the six clinical variables and the quantity of the 10 different bacteria, 3 predictive models were established in the training set and test set, and evaluated using a confusion matrix, area under the receiver operating characteristic curve (AUC) values, sensitivity (recall), specificity, accuracy, positive predictive value and negative predictive value (npv). The abundance of Bacteroides, Eubacterium rectale and Roseburia inulinivorans was significantly lower in the T2DM group compared with the control group. However, the abundance of Enterococcus was significantly higher in the T2DM group compared with the control group. In addition, Faecalibacterium prausnitzii, Enterococcus and Roseburia inulinivorans were significantly associated with sex status while Bacteroides, Bifidobacterium, Enterococcus and Roseburia inulinivorans were significantly associated with older age. In the training set, among the three models, support vector machine (SVM) and XGboost models obtained AUC values of 0.72 and 0.70, respectively. In the test set, only SVM obtained an AUC value of 0.77, and the precision and specificity were both above 0.77, whereas the accuracy, recall and npv were above 0.60. Furthermore, Bifidobacterium, age and Roseburia inulinivorans played pivotal roles in the model. In conclusion, the SVM model exhibited the highest overall predictive power, thus the combined use of machine learning tools with gut microbiome profiling may be a promising approach for improving early prediction of T2DM in the near feature.

Mapping organizational complexity: a network-based approach to behavior through machine learning tools

The current uncertain, dynamic scenario calls for a systemic perspective when referring to organizational complexity and behavior. Our research contributes to the analysis of organizational complexity through multidimensional behavioral mapping. Our method uses machine learning tools to detect the interconnections between the different behaviors of a person in his/her operating context. First, the research project dealt with prototyping a model to read the organizational behavior, the related detection tool, and a data analysis methodology. It used machine learning tools and ended with a data visualization phase. We set our model to read the organizational behavior by comparing the literature benchmark theories with our field experience. The model was organized around 4 areas and 16 behaviors. These were the basis for singling out the indicators and the questionnaire items. The data analysis methodology aimed at detecting the interconnections between behaviors. We designed it by joining univariate analysis with a multivariate technique based on the application of machine learning tools. This led to a high-resolution network map through three specific steps: (a) creating a multidimensional topology based on a Kohonen Map (a type of unsupervised learning artificial neural network) to geometrically represent behavioral relationships; (b) implementing k-means clustering for identifying which areas of the map have behavior similarity or affinity factors; and (c) locating people and the various identified clusters within the map. The research highlighted the validity of machine learning tools to detect the multidimensionality of organizational behavior. Therefore, we could delineate the networking of the observed elements and visualize an otherwise unattainable complexity through multimedia and interactive reporting. Application in the field of research consisted of the design and development of a prototype integrated with our LMS platform via a plugin. Field experimentation confirmed the effectiveness of the method for creating professional growth and development paths. Furthermore, this experimentation allowed us to obtain significant data by applying our model to several sectors, namely pharmaceutical, TLC, banking, automotive, machinery, and services.

Review of machine learning applications to power systems studies

The complexity of electric power networks from generation, transmission and distribution stations in modern times has resulted to generation of big and more complex data that requires more technical and mathematical analysis because it deals with monitoring, supervisory control and data acquisition all in real time. This has necessitated the need for more accurate analysis and predictions in power systems studies especially under transient, uncertainty or emergency conditions without interference of humans. This is necessary so as to minimize errors with the aim targeted towards improving the overall performance and the need to use more technical but very intelligent predictive tools has become very relevant. Machine learning (ML) is a powerful tool which can be utilized to make accurate predictions about the future nature of data based on past experiences. ML algorithms operate by building a model (mathematical or pictorial) from input examples to make data driven predictions or decisions for the future. ML can be used in conjunction with big data to build effective predictive systems or to solve complex data analytic problems. Electricity generation forecasting systems that could predict the amount of power required at a rate close to the electricity consumption have been proposed in several works. This study seeks to review machine learning applications to power system studies. This paper reviewed applications of ML tools in power systems studies.

Evaluating the Potential of Applying Machine Learning Tools to Metabolic Pathway Optimization

Successful engineering of a microbial host for efficient production of a target product from a given substrate can be viewed as an extensive optimization task. Such a task involves the selection of high activity enzymes as well as their gene expression regulatory control elements (i.e., promoters and ribosome binding sites). Finally, there is also the need to tune expression of multiple genes along a heterologous pathway to relieve constraints from rate-limiting step and help reduce metabolic burden on cells from unnecessary over-expression of high activity enzymes. While the aforementioned tasks could be performed through combinatorial experiments, such an approach incurs significant cost, time and effort, which is a handicap that can be relieved by application of modern machine learning tools. Such tools could attempt to predict high activity enzymes from sequence, but they are currently most usefully applied in classifying strong promoters from weaker ones as well as combinatorial tuning of expression of multiple genes. This perspective reviews the application of machine learning tools to aid metabolic pathway optimization through identifying challenges in metabolic engineering that could be overcome with the help of machine learning tools.

Machine Learning: New Ideas and Tools in Environmental Science and Engineering.

The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper model interpretation, and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.

Immunological tumor heterogeneity and diagnostic profiling for advanced and immune therapies

Immunotherapies have changed the way how we treat cancer at all stages. The understanding of the immune system in individual tumor specimens guides the selection of immune-modulating agents such as immune checkpoint inhibitors alone or in combination with other therapeutic agents that target, modulate or unleash the patient's immune system. Despite the similar histopathological diagnosis, each tumor is unique at its primary site and site of metastasis, also depending on previous treatment regimens or genetic alterations, such as chromosomal instability or acquired mutations. The clinically well-established use of PD-1/PD-L1 inhibitors already requires the assessment of its target molecules in different cells (viable tumor cells alone or in combination with immune cells or immune cells alone) with different thresholds in various indications. Anyhow, checkpoint inhibitors show the best overall response rate when immune effector cells like tumor-infiltrating lymphocytes are in close spatial proximity without being suppressed by other humoral or cellular regulatory mechanisms. Therefore, immune cell-rich tumors (“hot tumors”) are usually quite reactive to immune-modulating agents, whereas other immune-depleted or immune-excluded tumor areas are less responsive and require alternative treatment regimens such as modified immune effectors cells or immune-stimulating agents, for example, oncolytic viruses. Here, we summarize the relevance to understand the entire tumor heterogeneity and its environment, the contextual relationship and spatial quantification of all immune and tumor cells along with the genetic background of the individual cancer through the application of multiplex in-situ technologies and the application of machine learning tools.

Application of machine learning tools for constrained multi-objective optimization of an HCCI engine

Because of strict emission regulations, significant efforts are made by researchers to reduce the pollutants from internal combustion engines. Homogeneous mixture of fuel and air in Homogeneous Charge Compression Ignition (HCCI) engine results in lower amounts of thermal NOx while CO and HC emissions would be higher in this process. Besides, performance instability due to knock and misfire is another problem that HCCI engines are facing. In this paper, machine learning tools have been implemented to learn the behavior of a single-cylinder engine using three different fuels with the HCCI strategy. Changes in six objective functions with engine Inlet Valve Closing (IVC) temperature and air-fuel equivalence ratio are studied with the help of an Artificial Neural Network (ANN). Those objective functions are thermal efficiency, combustion efficiency, IMEP, together with NOx, CO, and HC emissions. A multi-objective optimization algorithm based on a decomposition method and genetic algorithm is used to identify the best operating condition of studied fuels. Results show that HCCI engine fueled with Ethanol and Methanol could produce significantly lower HC and CO emissions at their optimum point. However, the produced NOx would be higher in these cases comparing to the engine fueled with CNG. The genetic algorithm indicates that the optimum point of operation for CNG-fueled engine is TIVC ≈ 445 K while λ ≈ 2.25. In contrast, for the Ethanol-fueled case, this optimum point is located in the IVC temperature of ≈450 K and λ ≈ 2.75. The TOPSIS point is located in minimum air-fuel equivalence ratio and lower IVC temperatures (TIVC ≈ 420 K) when the engine is being operated with Methanol as fuel.

From Village to Globe: A Dynamic Real-Time Map of African Fields Through PlantVillage

A major bottleneck to the application of machine learning tools to satellite data of African farms is the lack of high-quality ground truth data. Here we describe a high throughput method using youth in Kenya that results in a cost-effective method for high-quality data in near real-time. This data is presented to the global community, as a public good and is linked to other data sources that will inform our understanding of crop stress, particularly in the context of climate change.

Early Detection of Hypotension Using a Multivariate Machine Learning Approach.

The ability to accurately detect hypotension in trauma patients at the earliest possible time is important in improving trauma outcomes. The earlier an accurate detection can be made, the more time is available to take corrective action. Currently, there is limited research on combining multiple physiological signals for an early detection of hemorrhagic shock. We studied the viability of early detection of hypotension based on multiple physiologic signals and machine learning methods. We explored proof of concept with a small (5 minutes) prediction window for application of machine learning tools and multiple physiologic signals to detecting hypotension. Multivariate physiological signals from a preexisting dataset generated by an experimental hemorrhage model were employed. These experiments were conducted previously by another research group and the data made available publicly through a web portal. This dataset is among the few publicly available which incorporate measurement of multiple physiological signals from large animals during experimental hemorrhage. The data included two hemorrhage studies involving eight sheep. Supervised machine learning experiments were conducted in order to develop deep learning (viz., long short-term memory or LSTM), ensemble learning (viz., random forest), and classical learning (viz., support vector machine or SVM) models for the identification of physiological signals that can detect whether or not overall blood loss exceeds a predefined threshold 5 minutes ahead of time. To evaluate the performance of the machine learning technologies, 3-fold cross-validation was conducted and precision (also called positive predictive value) and recall (also called sensitivity) values were compared. As a first step in this development process, 5 minutes prediction windows were utilized. The results showed that SVM and random forest outperform LSTM neural networks, likely because LSTM tends to overfit the data on small sized datasets. Random forest has the highest recall (84%) with 56% precision while SVM has 62% recall with 82% precision. Upon analyzing the feature importance, it was observed that electrocardiogram has the highest significance while arterial blood pressure has the least importance among all other signals. In this research, we explored the viability of early detection of hypotension based on multiple signals in a preexisting animal hemorrhage dataset. The results show that a multivariate approach might be more effective than univariate approaches for this detection task.

Application of Machine Learning Tools for the Improvement of Reactive Extrusion Simulation

AbstractThe purpose of this paper is to combine a classical 1D twin‐screw extrusion model with machine learning techniques to obtain accurate predictions of a complex system despite few data. Systems involving reactive polyethylene oligomer dispersed in situ in a polypropylene matrix by reactive twin‐screw extrusion are studied for this purpose. The twin‐screw extrusion simulation software LUDOVIC is used and machine learning techniques dealing with low data limit are used as a correction of the simulation.

Application of machine learning tool to predict the porosity of clastic depositional system, Indus Basin, Pakistan

Porosity is one of the key factors of a reservoir system that is typically distributed in a spatially non-uniform and non-linear manner. Nevertheless, spatial distribution of porosity in stratigraphic traps (channel sand bodies) is challenging due to the variations in depositional environments, shale intercalation, and repetition of textural changes in the pore sizes. This paper presents a strategy of joint inversion that combines support vector machine (SVM) and particle swarm optimization (PSO) algorithms to predict the spatial distribution of porosity using well logs and seismic data. In addition, Gaussian simulation algorithms (sequential Gaussian and Gaussian indicator simulations) were used to predict lithology and porosity distribution using wireline logs and core data. Rock physics modeling was performed to bridge the gap between the elastic and petrophysical properties for seismic-inversion based reservoir characterization. The results show that the joint inversion strategy leads to the most stable prediction of AI and porosity distribution in the lower Goru heterogeneous reservoir of Sawan Gas Field, Pakistan. The calibration of the individual spatial distribution of lithology and porosity from wireline logs data using Gaussian simulation algorithms and post-stack seismic inversion using SVM and PSO; revealed favorable matching. In fact, the spatial pattern of low AI corresponds with the high porosity and sandstone lithofacies. The study results provide valuable insights for identifying the sweet spots in the heterogeneous lower Goru clastic packages of Sawan Gas Field through quantitative analysis of rock physics, petrophysics, seismic inversion, and fuzzy optimization. The proposed joint inversion strategy may be considered useful for the prediction of porosity in similar context of extremely heterogeneous stratigraphic traps in other basins of the world.

Evaluation of ML-Based Clinical Decision Support Tool to Replace an Existing Tool in an Academic Health System: Lessons Learned.

There is increasing application of machine learning tools to problems in healthcare, with an ultimate goal to improve patient safety and health outcomes. When applied appropriately, machine learning tools can augment clinical care provided to patients. However, even if a model has impressive performance characteristics, prospectively evaluating and effectively implementing models into clinical care remains difficult. The primary objective of this paper is to recount our experiences and challenges in comparing a novel machine learning-based clinical decision support tool to legacy, non-machine learning tools addressing potential safety events in the hospitals and to summarize the obstacles which prevented evaluation of clinical efficacy of tools prior to widespread institutional use. We collected and compared safety events data, specifically patient falls and pressure injuries, between the standard of care approach and machine learning (ML)-based clinical decision support (CDS). Our assessment was limited to performance of the model rather than the workflow due to challenges in directly comparing both approaches. We did note a modest improvement in falls with ML-based CDS; however, it was not possible to determine that overall improvement was due to model characteristics.

Air quality and urban sustainable development: the application of machine learning tools

Air quality has an effect on a population’s quality of life. As a dimension of sustainable urban development, governments have been concerned about this indicator. This is reflected in the references consulted that have demonstrated progress in forecasting pollution events to issue early warnings using conventional tools which, as a result of the new era of big data, are becoming obsolete. There are a limited number of studies with applications of machine learning tools to characterize and forecast behavior of the environmental, social and economic dimensions of sustainable development as they pertain to air quality. This article presents an analysis of studies that developed machine learning models to forecast sustainable development and air quality. Additionally, this paper sets out to present research that studied the relationship between air quality and urban sustainable development to identify the reliability and possible applications in different urban contexts of these machine learning tools. To that end, a systematic review was carried out, revealing that machine learning tools have been primarily used for clustering and classifying variables and indicators according to the problem analyzed, while tools such as artificial neural networks and support vector machines are the most widely used to predict different types of events. The nonlinear nature and synergy of the dimensions of sustainable development are of great interest for the application of machine learning tools.

Multilevel Intrusion Detection System with Affinity Clustering and Ensemble SVM

Now-a-days with growing applications over internet increases the security issues over network. Many security applications are designed to cope with such security concerns but still it required more attention to improve speed as well accuracy. With advancement of technologies there is also evolution of new threats or attacks in network. So, it is required to design such detection system that can handle new threats in network. One of the network security tools is intrusion detection system which is used to detect malicious data packets. Machine learning tool is also used to improve efficiency of network-based intrusion detection system. In this paper, an intrusion detection system is proposed with an application of machine learning tools. The proposed model integrates feature reduction, affinity clustering and multilevel Ensemble Support Vector Machine. The proposed model performance is analyzed over two datasets i.e. NSL-KDD and UNSW-NB 15 dataset and achieved approx. 12% of efficiency over other existing work.

Application of machine learning tools in classifying pedestrian crash types: A case study

Abstract Crash occurrence is a complex phenomenon, and crashes associated with pedestrians and bicyclists are even more complex. Furthermore, pedestrian- and bicyclist-involved crashes are typically not reported in detail in state or national crash databases. To address this issue, developers created the Pedestrian and Bicycle Crash Analysis Tool (PBCAT). However, it is labour-intensive to manually identify the types of pedestrian and bicycle crash from crash-narrative reports and to classify different crash attributes from the textual content of police reports. Therefore, there is a need for a supporting tool that can assist practitioners in using PBCAT more efficiently and accurately. The objective of this study is to develop a framework for applying machine-learning models to classify crash types from unstructured textual content. In this study, the research team collected pedestrian crash-typing data from two locations in Texas. The XGBoost model was found to be the best classifier. The high prediction power of the XGBoost classifiers indicates that this machine-learning technique was able to classify pedestrian crash types with the highest accuracy rate (up to 77% for training data and 72% for test data). The findings demonstrate that advanced machine-learning models can extract underlying patterns and trends of crash mechanisms. This provides the basis for applying machine-learning techniques in addressing the crash typing issues associated with non-motorist crashes.

Application of machine learning tools for evaluating the impact of premenopausal hysterectomy on serum anti-mullerian hormone levels

Women who have had premenopausal total hysterectomy at a young age could probably experience partial or total loss of ovarian function. The purpose of this retrospective cross-sectional study is to investigate the ovarian function in women underwent hysterectomy at an early age. A total of 1165 subjects comprised of 685 hysterectomised women and 480 age matched controls were enrolled in the study. We found that there is a steady decline in serum Anti Mullerian Hormone (AMH) levels, a marker of ovarian function after every five years post - hysterectomy in early age groups (20-30 yr and 31-40 yr) followed by loss of ovarian function in the age group of 40-50 yr. The application of multiple linear regression and machine learning tools has revealed that AMH is positively correlated with LH and estradiol and negatively correlated with age, FSH, years since hysterectomy and vitamin D. Serum AMH level of <0.08 ng/ml is associated with the increased of FSH, decreased LH and estradiol. The decreased ovarian function is associated with lower calcium levels, which are likely to influence the bone health. In conclusion, by utilizing multiple linear regression and machine learning tools, we found that serum FSH is the most important in predicting the AMH-mediated ovarian function.

Machine learning for robo-advisors: testing for neurons specialization

The rise of robo-advisor wealth management services, which constitute a key element of fintech revolution, unveils the question whether they can dominate human-based advice, namely how to address the client’s behavioral biases in an automated way. One approach to it would be the application of machine learning tools during client profiling. However, trained neural network is often considered as a black box, which may raise concerns from the customers and regulators in terms of model validity, transparency, and related risks. In order to address these issues and shed more light on how neurons work, especially to figure out how they perform computation at intermediate layers, this paper visualizes and estimates the neurons’ sensitivity to different input parameters. Before it, the comprehensive review of the most popular optimization algorithms is presented and based on them respective data set is generated to train convolutional neural network. It was found that selected hidden units to some extent are not only specializing in the reaction to such features as, for example, risk, return or risk-aversion level but also they are learning more complex concepts like Sharpe ratio. These findings should help to understand robo-advisor mechanics deeper, which finally will provide more room to improve and significantly innovate the automated wealth management process and make it more transparent.