Back-Analysis of Structurally Controlled Failure in an Open-Pit Mine with Machine Learning Tools

ADRD caregivers increasingly use social media to meet their health information wants (HIW). Machine learning (ML) tools may help understand caregivers’ HIW as expressed via social media. This pilot study explored a collaborative, iterative process between domain experts and ML tools to identify ADRD caregivers’ HIW from social media data. The HIW-ADRD framework was adapted from an existing HIW framework. Through multiple rounds of iteration between the experts and the ML tools, the framework was expanded to include 11 types of health information. Each type included corresponding keywords developed through a hybrid approach that included keywords from both the theoretical constructs (top-down) and caregivers’ posts (bottom-up). These keywords were then used to enhance the ML tools’ ability to code 106 recent posts extracted from an ADRD social media group in March 2020. When compared with expert coding results, ML tools accurately predicted 56% of HIW. Further work is underway.

Introduction: Cataract is a prevalent eye condition that affects millions of individuals worldwide, leading to visual impairment and reduced quality of life. Cataract surgery is the most effective treatment, but post-surgical complications can arise, impacting the success of the intervention. Optical coherence tomography (OCT) has emerged as a valuable imaging technique for evaluating these complications, but the manual interpretation of OCT images is time-consuming and subjective.Objective: In this study, we aimed to assess the performance of a machine learning (ML) tool specifically developed for detecting post-surgical complications in cataract patients.Methods: We employed a support vector machine (SVM) algorithm to analyze a comprehensive dataset of OCT images. The dataset comprised 700 OCT images obtained post-surgery, including patients with cystoid macular oedema (CMO), retinal detachment (RD), and healthy individuals. The ML tool utilized pre-processed OCT images with annotations provided by expert ophthalmologists, undergoing retinal layer segmentation using intensity-based features.Results: The SVM algorithm demonstrated high sensitivity and specificity in detecting and classifying post-surgical complications. It achieved a sensitivity of 92.5% in detecting CMO and 90.9% in identifying RD. The specificity of the algorithm was 90.9% and 96.2% for these complications, respectively. The overall accuracy of the ML tool in correctly identifying and classifying post-surgical complications was 92%.Conclusions: The integration of ML algorithms in OCT imaging shows promise in enhancing the accuracy and efficiency of assessing post-surgical complications in cataract patients. The ML tool developed in this study provides reliable and objective assessments, reducing the subjectivity and variability associated with the manual interpretation of OCT images

The increase in the global demand for plastics, and more recently during the pandemic, is a major concern for the future of plastic waste pollution and microplastics. Efficient microplastic monitoring is imperative to understanding the long-term effects and progression of microplastic effects in the environment. Numerical models are valuable in studying microplastic transport as they can be used to examine the effects of different parameters systematically to help elucidate the fate and transport processes of microplastics, thus providing a holistic view of microplastics in the ocean environment. By incorporating physical parameters (such as size, shape, density, and identity of microplastics), numerical models have gained better understanding of the physics of microplastic transport, predicted sinking velocities more accurately, and estimated microplastic pathways in marine environments. However, availability of large amounts of information about microplastic physical and chemical parameters is sparse. Machine learning and computer-vision tools can aid in acquiring environmental information and provide input to develop more accurate models and verify their predictions. More accurate models can further the understanding of microplastic transport, facilitate monitoring efforts, and thus optimize where more data collection can take place to ultimately improve machine learning tools. This review offers a perspective on how image-based machine learning can be exploited to help uncover the physics of microplastic transport behaviors. Additionally, the authors hope the review inspires studies that can bridge the gap between numerical modeling and machine learning for microplastic analysis to exploit their joined potential.

Predictive Modeling of Hematopoietic Stem Cell Transplant Outcomes: Discovering the Role of Immunogenetic Features through Machine Learning Models

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.

Machine Learning Algorithm Prospectively Predicts Survival for High-Risk Patients Undergoing Radiotherapy: A Survival Analysis of SHIELD-RT

Background: The unprecedented global spread of coronavirus disease 2019 (COVID-19) has imposed huge challenges on the healthcare facilities, and impacted every aspect of life. This has led to the development of several vaccines against COVID-19 within one year. This study aimed to assess the attitudes and the side effects among Arab communities after receiving a COVID-19 vaccine and use of machine learning (ML) tools to predict post-vaccination side effects based on predisposing factors. Methods: An online-based multinational survey was carried out via social media platforms from 14 June to 31 August 2021, targeting individuals who received at least one dose of a COVID-19 vaccine from 22 Arab countries. Descriptive statistics, correlation, and chi-square tests were used to analyze the data. Moreover, extensive ML tools were utilized to predict 30 post vaccination adverse effects and their severity based on 15 predisposing factors. The importance of distinct predisposing factors in predicting particular side effects was determined using global feature importance employing gradient boost as AutoML. Results: A total of 10,064 participants from 19 Arab countries were included in this study. Around 56% were female and 59% were aged from 20 to 39 years old. A high rate of vaccine hesitancy (51%) was reported among participants. Almost 88% of the participants were vaccinated with one of three COVID-19 vaccines, including Pfizer-BioNTech (52.8%), AstraZeneca (20.7%), and Sinopharm (14.2%). About 72% of participants experienced post-vaccination side effects. This study reports statistically significant associations (p < 0.01) between various predisposing factors and post-vaccinations side effects. In terms of predicting post-vaccination side effects, gradient boost, random forest, and XGBoost outperformed other ML methods. The most important predisposing factors for predicting certain side effects (i.e., tiredness, fever, headache, injection site pain and swelling, myalgia, and sleepiness and laziness) were revealed to be the number of doses, gender, type of vaccine, age, and hesitancy to receive a COVID-19 vaccine. Conclusions: The reported side effects following COVID-19 vaccination among Arab populations are usually non-life-threatening; flu-like symptoms and injection site pain. Certain predisposing factors have greater weight and importance as input data in predicting post-vaccination side effects. Based on the most significant input data, ML can also be used to predict these side effects; people with certain predicted side effects may require additional medical attention, or possibly hospitalization.

Introduction The multiple nutritional disorders producing the early decline of citrus productivity are commonly observed across the citrus belts of northeast India. This situation is further compounded by a mismatch between annual addition and consumption of fertilizers, in the backdrop of an erroneous diagnosis of nutrient imbalance. In this background, we attempted to diagnose nutrient balance in Khasi mandarin ( Citrus reticulata Blanco) orchards using several diagnostic tools comprising machine learning (ML) tools. Methods A database of soil available nutrients (KMn0 4 -N, Brays-P, NH 4 0Ac-K, DTPA-Fe, DTPA-Mn, DTPA-Cu, DTPA-Zn) and fruit yield documenting 180 Khasi mandarin orchards of seedling origin (10–30 years old with row-to-row distance of 4 m and trees 6 m apart) raised under rainfed conditions in the Meghalaya state of northeast India. Diagnosis methods were compared: the sufficiency level of available nutrients (SLAN), the basic cation saturation ratio (BCSR), the compositional nutrient diagnosis (CND), the diagnosis and recommendation integrated system (DRIS) and ML tools like random forest and xgboost. Results Soil test interpretation of a low-yielding and nutritionally imbalanced orchard differed among diagnostic methods. DRIS predicted deficient-to-low concentrations of Zn, Ca, P, N, and K; other nutrients like Fe, Cu, Mn, and Mg were at optimum-to-high concentrations. CND standards diagnosed Zn deficiency and Cu excess with potential agronomic manifestations for early decline in productivity. SLAN interpretation was highly skewed for Ca; moderately skewed for Mg, Cu and Zn, and unskewed for N, P, K, Fe and Mn. The accuracy of ML regression models relating nutrient expressions to fruit yield was invariably high, followed by SLAN. The ML xgboost regression model exhibited the highest accuracy in predicting fruit yield from soil test. Conversely, the BCSR, which considers only three cationic dual ratios, was inaccurate. There were distortions when relating concentration values to DRIS indices to determine ‘optimum’ concentration ranges. The ML classification models showed that concentration values were also less accurate than clr to classify data as true negative (TN) or true positive (TP). The xgboost classification model showed optimum ranges for N, P, Ca, Mg, Mn, and Cu; whereas K, Fe, and Zn fell below the lower limits. Conclusion ML, hence, as a classification approach, aided in discarding cases of poor yields and high yields showing luxury nutrient consumption or suboptimal nutrient levels. The soil test standards and site-by-site comparisons can further support site-specific nutrient management and precision fertilization. Considering Zn as the most deficient nutrient, Zn biofortification interventions are recommended to increase fruit yield and quality in Khasi mandarin.

AGEs are highly stable products formed by glycation of reducing sugars, proteins and lipids. Diabetes mellitus is the condition; where the cells cannot utilize the glucose present in body tend to glycate free proteins in surroundings and cause various complications. Once, the AGEs glycate tissue proteins, they accumulate over lifetime and therefore contributing to complications such as neurodegeneration, polyneuropathy, retinopathy, nephropathy and other macrovascular complications. AGEs can be formed in many ways and bind with RAGE cell surface receptor. Since the etiology of these diseases is now well understood and the biomarkers of the disease are available, it is important to consider them in all machine learning disease prediction tools. Machine learning and deep learning algorithms and tools are being used in predicting various diseases mostly on basis of medical history and symptoms. This review focuses about how different AGEs play role in pathogenesis of different diabetic complications and discusses the why these glycated proteins should be considered in prediction of diabetic complications using machine learning or by other means.

In recent years, additive manufacturing (AM) has garnered significant attention all over the world due to the exemplary benefits attained during design to achieving superior part quality. Researchers have also started utilizing machine learning (ML) tools to aid the AM process. Emphasis has been laid on the availability of ample datasets and the ease of their acquisition. The need for establishment of feature libraries has been highlighted. Different ML techniques and associated models such as Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Decision Trees (DT), Deep Convolution Network (DNN), and Convolutional Neural Network (CNN) are being used by researchers for optimization of parameters, defect detection, creation of online monitoring systems as well as predicting the powder spreading mechanism for AM. In fact, most ML tools are utilized either for classification or regression purposes. This paper focuses on the availability of the resources required to employ ML in AM, the applications of ML in AM, present limitations, and potential opportunities for extended use in future.

The use of Machine Learning (ML) tools has grown a lot in several areas, including in the civil construction space. Part one of this study tried to use ML to estimate results for sound insulation of floor systems and had concluded that ML is not applicable to those situations. Part two tried some adjustments to the ML script and used an on-site vertical partition system test database as input with ML tools used to estimate new results based on the input data. The conclusion of this new study is that the tool is now more applicable to floor systems but it is not the best form to predict the results, given the complexity of sound propagation between rooms, which is not considered in the statistical analysis of the machine.

Machine learning (ML) constitutes a division of artificial intelligence (AI) that aims to train computers how to perform specific tasks without explicit programming. Traditional ML tools are widely used for classification and identification of animals. However, these methods have some drawbacks because of the extensive manual reliance and the delay in data interpretation. To overcome this, Applied Deep Learning algorithms are used with Artificial Neural Networks (ANN) and Convolution Neural Network (CNN) models introduced to address species classification, characteristics detection, and pattern recognition tasks helping in accurate identification and classification of animals. In this paper, we have tried to compile and deliver a recent comprehensive information on latest available investigations in the field of life sciences particularly used for animal identification. We have also accentuated the diverse applications of machine learning models including other parameters like, features, accuracy gained, database used and their limitations. The red flour beetle, Tribolium castaneum (Coleoptera; Tenebrionidae) is a prevailing and detrimental secondary insect pest of stored grains along with derived products causing 7% to 35% annual loss. Despite of that, nowdays it is also extensively considered as a model organism for genetic disease investigation. While using it in scientific research, exact sex identification of these insects becomes a crucial preliminary step. Generally, pupal stage is used to sort these insects according to their sex and needs expert humans. It is crucial to employ image processing and ML algorithms to quickly identify gender of this insect which is not done yet. We have used a CNN-based smart technique to recognize and categorize gender differences in T. castaneum using microscopic images in order to build an intelligent system for applied research. For this study, a dataset is created by taking 116 microscopic images of both the dorsal and ventral sides of pupae of two different sexes. In this algorithm, a 2D matrix of feature map is selected sequentially and the maximum value in the matrix is selected to generate a pooled feature map. The Rectified Linear Unit (ReLU) activation function is used for the CNN. The classification model has an accuracy between 97 and 98% with an F-score of 0.67. These results demonstrate the robustness of the classification model, which does not rely heavily on manual intervention compared to traditional machine learning (ML) tools and automates the processes of feature extraction and gender classification regardless of the position of the pupae in the images.

Early detection of disease and accurate assessment of its extent are of paramount importance for the course of treatment and prognosis of larynx cancer. Machine learning and artificial intelligence tools have the potential to accelerate and improve diagnostic procedures in medicine, as well as to predict disease outcomes and response to specific therapies. Computer algorithms can analyze two-dimensional images obtained during procedures such as laryngeal spectroscopy and endoscopy. Radiological images can be evaluated using appropriate algorithms to determine whether the laryngeal tissue is benign or malignantly altered. In recent years, machine learning tools have been developed to determine the precise radiation doses, predict tumor radiosensitivity, as well as the possibility and severity of complications based on radiological image analysis. In the field of pathology, significant progress has been made by creating digital records of histopathological preparations, which can be further analyzed. This allows changes in intercellular interaction and tissue architecture that cannot be detected by conventional microscopic methods to be identified. With innovative computer techniques, it is possible to quantify tissue and cell structure parameters, which are calculated based on mathematical formulas and used to measure structural homogeneity and uniformity in both normal and pathologically altered tissue. Future multidisciplinary research aimed at developing new and innovative biosensors for the detection of discrete morphological changes characteristic of squamous cell carcinoma of the larynx will make a significant contribution to the advancement of diagnosis and treatment in the field of otolaryngology. In the future, the use of artificial intelligence and machine learning could enable the fusion of algorithms that combine data obtained from radiological, endoscopic, and histopathological findings, which could significantly increase the accuracy and precision of diagnosis, facilitate the process of deciding on therapeutic options, and improve the success rate of larynx cancer treatment.

Blast-induced ground vibration (PPV) evaluation for a safe blasting is a long-established criterion used mainly by the empirical equations. However, the empirical equations are again considering a limited information. Therefore, using Machine Learning (ML) tools [Support Vector Machine (SVM) and Random Forest (RF)] can help in this context, and the same is applied in this work. A total of 73 blasts are monitored and recorded in this work. For the ML tools, the dataset is divided into the 80-20 ratio for the training and testing purposes in order to evaluate the performance capacity of the models. The prediction accuracies by the SVM and RF models in predicting the PPV values are satisfactory (up to 9% accuracy). The results obtained show that the coefficient of determination (R2) for RF and SVM is 0.81 and 0.75, respectively. Compared to the existing linear regressions, this work recommends using a machine learning regression model for the PPV prediction.

Machine learning tools to improve nonlinear modeling parameters of RC columns