Open-source Machine Learning Algorithm Research Articles

Predictive Modeling Through Fusion of Passengers Information Transferred to Alternate Dimensions

This research focuses on the identification of passengers, in dimensions using information fusion as a tool. We recognize the challenges involved in identifying individuals who have been transferred to alternate dimensions and in this study we make use of CatBoost, an open source machine learning algorithm to address this problem. Our approach includes a preprocessing strategy that involves filling in missing values using techniques like priori distribution terms, which helps ensure the reliability of our dataset. By leveraging CatBoosts ability to handle variables and prevent overfitting we achieve results in accurately predicting passenger movement across dimensions. Our analysis highlights CatBoosts effectiveness in identifying patterns within data leading to more precise predictions for interdimensional passenger transportation. Additionally we incorporate techniques, like Greedy TS augmentation to enhance the adaptability of the algorithm and improve precision while reducing bias in modeling. Proof-of-concept experiments demonstrate that the proposed fusion system not only advances predictive modeling in niche domains but also paves the way for broader applications of machine learning in deciphering complex phenomena beyond traditional realms, marking a significant stride in understanding and addressing unconventional challenges.

High-pressure structural behavior and elastic properties of Ce2In and Ce3In: A combined in situ synchrotron X-ray diffraction and density functional theory study

Across the cerium (Ce)-indium (In) binary phase diagram, there are five line compounds including Ce3In, Ce2In, Ce3In5, CeIn2 and CeIn3. So far, CeIn3 is the only compound whose high-pressure structural behavior and elastic properties have been studied in detail. In this work, we investigated the compression behavior of Ce3In and Ce2In at pressures up to 26.2 GPa using in situ high-pressure angle dispersive synchrotron powder X-ray diffraction (XRD). Our results indicate that Ce3In retains its Cu3Au-type structure in the pressure range investigated, whereas Ce2In undergoes a phase transformation from the Ni2In-type structure to an unidentified phase at ∼4.6 GPa. Unit-cell parameters as a function of pressure were obtained by Rietveld analysis of XRD data. Unit cell volumes were fitted to a second order Birch-Murnaghan equation of state (EOS). The experimentally determined bulk moduli (K0) of the two cerium indides are 50 ± 3 GPa for Ce3In and 39.2 ± 1.6 GPa for Ce2In. The zero-pressure compressibilites of the a-axis (βa,0) for Ce3In is 6.67 (±0.36) × 10–3 GPa–1 and of the a- and c-axis for Ce2In are βa,0 = 8.85 (±0.39) × 10−3 GPa−1 and βc,0 = 9.26 (±0.51) × 10−3 GPa−1, respectively. These results are in general agreement with those calculated by density functional theory (DFT + U) and open-source machine learning algorithms.

Smartphone Imaging Flow Cytometry for High-Throughput Single-Cell Analysis.

We present a portable imaging flow cytometer comprising a smartphone, a small-footprint optical framework, and a PDMS-based microfluidic device. Flow cytometric analysis is performed in a sheathless manner via elasto-inertial focusing with a custom-written Android program, integrating a graphical user interface (GUI) that provides a high degree of user control over image acquisition. The proposed system offers two different operational modes. First, "post-processing" mode enables particle/cell sizing at throughputs of up to 67 000 particles/s. Alternatively, "real-time" mode allows for integrated cell/particle classification with machine learning at throughputs of 100 particles/s. To showcase the efficacy of our platform, polystyrene particles are accurately enumerated within heterogeneous populations using the post-processing mode. In real-time mode, an open-source machine learning algorithm is deployed within a custom-developed Android application to classify samples containing cells of similar size but with different morphologies. The flow cytometer can extract high-resolution bright-field images with a spatial resolution <700 nm using the developed machine learning-based algorithm, achieving classification accuracies of 97% and 93% for Jurkat and EL4 cells, respectively. Our results confirm that the smartphone imaging flow cytometer (sIFC) is capable of both enumerating single particles in flow and identifying morphological features with high resolution and minimal hardware.

Segmentation of human functional tissue units in support of a Human Reference Atlas

The Human BioMolecular Atlas Program (HuBMAP) aims to compile a Human Reference Atlas (HRA) for the healthy adult body at the cellular level. Functional tissue units (FTUs), relevant for HRA construction, are of pathobiological significance. Manual segmentation of FTUs does not scale; highly accurate and performant, open-source machine-learning algorithms are needed. We designed and hosted a Kaggle competition that focused on development of such algorithms and 1200 teams from 60 countries participated. We present the competition outcomes and an expanded analysis of the winning algorithms on additional kidney and colon tissue data, and conduct a pilot study to understand spatial location and density of FTUs across the kidney. The top algorithm from the competition, Tom, outperforms other algorithms in the expanded study, while using fewer computational resources. Tom was added to the HuBMAP infrastructure to run kidney FTU segmentation at scale—showcasing the value of Kaggle competitions for advancing research.

Integrated use of Sentinel-1 and Sentinel-2 data and open-source machine learning algorithms for burnt and unburnt scars

This research compares the use of the SAR (Sentinel-1) and Optical (Sentinel-2) sensors in identifying and mapping burnt and unburnt scars are rising during a bushfire in southeastern Australia and Margalla Hills, Islamabad, Pakistan, in 2019 and 2020. In order to evaluate the backscatter strength along with the Polarimetric decomposition portion, the C-band dual-polarized Sentinel-1 data was investigated to determine the magnitude of the burnt areas of forest cover in the study area. We could derive texture measurements from locally-based statistics using the Grey Level Co-occurrence Matrix (GLCM) and the backscatter coefficient. This was because of how well it picked up on differences in texture between burned and unburned scars. In contrast, Sentinel-2 optical remote sensing was employed to evaluate the extent of the burnt intensity levels for both regions utilizing the differential Normalized Burnt Ratio (dNBR). A Support Vector Machine (SVM) and Markov Random Field (MRF) classifier were utilized to investigate the study’s context. The ideal smoothing parameter is the result of incorporating the image’s spectral characteristics and spatial meaning. Sentinel-2 images were used as a foundation for both the test and training datasets, which were built from images of both unburned and burned areas broken down pixel by pixel. In both types, including spectral sensitivity and sensitivity of Polarimetric for the two groups identified after classification, the experimental findings showed a clear association between them. The algorithm’s efficiency was evaluated using the kappa coefficient and F-score calculation. Except for Sentinel-1 data in Pakistan, all fire areas have more than 0.80 accuracies. The highest precision of both Sentinel-1 and Sentinel-2 was also provided by the performance of users’ and producers’ accuracy. The entropy alpha decomposition helped define the target given by the H-a plane based on its physical properties. After the burn, the entropy and alpha values diminished and formed a pattern. However, the findings in this field validate the effectiveness of SAR sensors data and optical satellite in forest applications. The related sensitivity is highly dependent on the composition of the landscape, the geographical nature of the study area, and the severity of the burn.

Usability of deep learning pipelines for 3D nuclei identification with Stardist and Cellpose

Segmentation of 3D images to identify cells and their molecular outputs can be difficult and tedious. Machine learning algorithms provide a promising alternative to manual analysis as emerging 3D image processing technology can save considerable time. For those unfamiliar with machine learning or 3D image analysis, the rapid advancement of the field can make navigating the newest software options confusing. In this paper, two open-source machine learning algorithms, Cellpose and Stardist, are compared in their application on a 3D light sheet dataset counting fluorescently stained proliferative cell nuclei. The effects of image tiling and background subtraction are shown through image analysis pipelines for both algorithms. Based on our analysis, the relative ease of use of Cellpose and the absence of need to train a model leaves it a strong option for 3D cell segmentation despite relatively longer processing times. When Cellpose's pretrained model yields results that are not of sufficient quality, or the analysis of a large dataset is required, Stardist may be more appropriate. Despite the time it takes to train the model, Stardist can create a model specialized to the users' dataset that can be iteratively improved until predictions are satisfactory with far lower processing time relative to other methods.

Optimal Dimensioning of Retaining Walls Using Explainable Ensemble Learning Algorithms.

This paper develops predictive models for optimal dimensions that minimize the construction cost associated with reinforced concrete retaining walls. Random Forest, Extreme Gradient Boosting (XGBoost), Categorical Gradient Boosting (CatBoost), and Light Gradient Boosting Machine (LightGBM) algorithms were applied to obtain the predictive models. Predictive models were trained using a comprehensive dataset, which was generated using the Harmony Search (HS) algorithm. Each data sample in this database consists of a unique combination of the soil density, friction angle, ultimate bearing pressure, surcharge, the unit cost of concrete, and six different dimensions that describe an optimal retaining wall geometry. The influence of these design features on the optimal dimensioning and their interdependence are explained and visualized using the SHapley Additive exPlanations (SHAP) algorithm. The prediction accuracy of the used ensemble learning methods is evaluated with different metrics of accuracy such as the coefficient of determination, root mean square error, and mean absolute error. Comparing predicted and actual optimal dimensions on a test set showed that an score of 0.99 could be achieved. In terms of computational speed, the LightGBM algorithm was found to be the fastest, with an average execution speed of 6.17 s for the training and testing of the model. On the other hand, the highest accuracy could be achieved by the CatBoost algorithm. The availability of open-source machine learning algorithms and high-quality datasets makes it possible for designers to supplement traditional design procedures with newly developed machine learning techniques. The novel methodology proposed in this paper aims at producing larger datasets, thereby increasing the applicability and accuracy of machine learning algorithms in relation to optimal dimensioning of structures.

Applications of Machine Learning Models to Predict and Prevent Obesity: A Mini-Review.

Research on obesity and related diseases has received attention from government policymakers; interventions targeting nutrient intake, dietary patterns, and physical activity are deployed globally. An urgent issue now is how can we improve the efficiency of obesity research or obesity interventions. Currently, machine learning (ML) methods have been widely applied in obesity-related studies to detect obesity disease biomarkers or discover intervention strategies to optimize weight loss results. In addition, an open source of these algorithms is necessary to check the reproducibility of the research results. Furthermore, appropriate applications of these algorithms could greatly improve the efficiency of similar studies by other researchers. Here, we proposed a mini-review of several open-source ML algorithms, platforms, or related databases that are of particular interest or can be applied in the field of obesity research. We focus our topic on nutrition, environment and social factor, genetics or genomics, and microbiome-adopting ML algorithms.

Integrated use of Sentinel-1 and Sentinel-2 data and open-source machine learning algorithms for land cover mapping in a Mediterranean region

ABSTRACT This paper aims to develop a supervised classification integrating synthetic aperture radar (SAR) Sentinel-1 (S1) and optical Sentinel-2 (S2) data for land use/land cover (LULC) mapping in a heterogeneous Mediterranean forest area. The time-series of each SAR and optical bands, three optical indices (normalized difference vegetation index, NDVI; normalized burn ratio, NBR; normalized difference red-edge index, NDRE), and two SAR indices (radar vegetation index, RVI; radar forest degradation index, RFDI), constituted the dataset. The coherence information from SAR interferometry (InSAR) analysis and three optical biophysical variables (leaf area index, LAI; fraction of green vegetation cover, fCOVER; fraction of absorbed photosynthetically active radiation, fAPAR) of the single final month of the time-series were added to exploit their correlation with the canopy structure and improve the classification. The random forests (RF) algorithm was used to train and classify the final dataset, and an exhaustive grid search analysis was applied to set the optimal hyperparameters. The overall accuracy reached an F-scoreM of 90.33% and the integration of SAR improved it by 2.53% compared to that obtained using only optical data. The whole process was performed using freely available data and open-source software and libraries (SNAP, Google Earth Engine, Scikit-Learn) executed in Python-script language.

Can machine learning optimize the efficiency of the operating room in the era of COVID-19?

The cancellation of large numbers of surgical procedures because of the coronavirus disease 2019 (COVID-19) pandemic has drastically extended wait lists and negatively affected patient care and experience. As many facilities resume clinical work owing to the currently low burden of disease in our community, we are faced with operative booking protocols and procedures that are not mathematically designed to optimize efficiency. Using a subset of artificial intelligence called "machine learning," we have shown how the use of operating time can be optimized with a custom Python (a high-level programming language) script and an open source machine-learning algorithm, the ORTools software suite from the Google AI division of Alphabet Inc. This allowed the creation of customized models to optimize the efficiency of operating room booking times, which resulted in a reduction in nursing overtime of 21% - a theoretical cost savings of $469 000 over 3 years.

Effect of automated TIL quantification in early-stage melanoma on accuracy of standard T staging using AJCC guidelines.

10076 Background: Patients diagnosed with early stage melanoma are at risk of recurrence and death. Adjuvant therapy decreases risk but incurs toxicity and expense. While tumor-infiltrating lymphocytes (TILs) improve prognosis, studies have shown conflicting results due, at least in part, to inter-observer variability. Thus, TILs are not included in standard American Joint Committee on Cancer (AJCC) staging. Here, we quantitatively analyze TILs in hematoxylin and eosin (H&amp;E) melanoma images using two machine learning algorithms. Methods: H&amp;E images were evaluated by two methods for patients with resectable stage I-III melanoma from Columbia (N = 81) and validated using samples from Geisinger and Moffitt (N = 128). For both methods, H&amp;E images were manually annotated using open source software, QuPath, to specify tumor regions. For Method A, images were divided into patches and, for each patch, a probability was generated to detect lymphocytes. Patches above a set threshold were considered to be “TIL positive”. Ratio of TIL positive patches to total patches was assessed for every image. For Method B, a classifier was manually trained in QuPath and then applied on each image to determine the ratio of the areas of all immune cells to all tumor cells as previously published. Cutoff values to define high and low risk groups were established based on a test set and then validated in an independent cohort. Results: Both methods distinguished patients with visceral recurrence from those without for the Columbia training set (Method A p = .0015, Method B p = .043). Using Method A, Kaplan-Meier curve at the selected cutoff also correlated significantly with disease specific survival (DSS) for Columbia (p = .022) and was validated in the Geisinger/Moffitt (p = .046) cohort. Cox analysis using Method A showed that TIL status predicted DSS in the validation set (p = .047) and added significantly to depth and ulceration (HR = 3.43, CI: 1.047-11.257, p = .042). Conclusions: Both open source machine learning algorithms find significantly higher TILs in patients who do not develop metastasis. Notably, Method A may add to standard predictors, such as depth and ulceration. These results demonstrate the promise of computational algorithms to enhance visual grading, and suggest that digital TIL evaluation may add to current AJCC staging. [Table: see text]

Robust Intelligent Malware Detection using Light GBM Algorithm

Attackers take advantage of every second that the anti- vendor delays identifying the attacking malware signature and to provide notifications. In addition, the longer the detection period delayed, the greater the damage to the host device. To put it another way, the lack of ability to detect attacks early complicates the problem and rises serious harm. Consequently, this research intends to develop a knowledgeable anti-malware system capable of immediately detecting and terminating malware actions, rather than waiting for anti-malware updates. The research concentrates in its scope on the detection of malware on the Internet of Things (IoT), based on Machine Learning (ML) techniques. A latest open source ML algorithm called the Light Gradient Boosting Algorithm (LightGBM) has been used to develop our instant host and network layer anti-malware approach without any human intervention. For examination reasons, the suggested approach serves the LightGBM machine learning algorithm to adopt datasets obtained from real IoT devices using the LightGBM machine learning algorithm. The results indicate a successful method to detecting and classifying high accuracy malware at both network and host levels based on the Holdout method of cross-validation. Additionally, this result is better than many prior related studies which used different algorithms of Machine Learning and Deep Learning. Though, an old study which used the same dataset was the best among the literature. However, it still slightly less than what this study achieved, besides the complexity which deep learning adds. Lastly, the results show the ability of the proposed approach to detect IoT botnet attacks fast, which is a vital feature to end botnet activity before spreading to any new network device.

Applying Machine Learning to Nanoindentation Data of (Nano-) Enhanced Composites

Carbon fiber reinforced polymers (CFRPs) are continuously gaining attention in aerospace and space applications, and especially their multi-scale reinforcement with nanoadditives. Carbon nanotubes (CNTs), graphene, carbon nanofibers (CNFs), and their functionalized forms are often incorporated into interactive systems to engage specific changes in the environment of application to a smart response. Structural integrity of these nanoscale reinforced composites is assessed with advanced characterization techniques, with the most prominent being nanoindentation testing. Nanoindentation is a well-established technique, which enables quantitative mapping of nanomechanical properties with the μm surficial and nm indentation resolution scale and high precision characterization. This feature enables the characterization of the interface in a statistical and quantitative manner and the correlation of (nano-) reinforcement to interface properties of CFRPs. Identification of reinforcement is performed with k-Nearest Neighbors and Support Vector Machine classification algorithms. Expertise is necessary to describe the physical problem and create representative training/testing datasets. Development of open source Machine Learning algorithms can have an influential impact on uniformity of nanometry data creation and management. The statistical character of nanoindentation is a key factor to supply information on heterogeneity of multiscale reinforced composites. Both the identification of (nano-) reinforcement and quality assessment of composites are provided by involving artificial intelligence.

Classification of drilling stick slip severity using machine learning

Rate of penetration (ROP) is a key metric used to monitor the success of drilling a well. It is directly affected by drilling vibrations since excessive vibrations result in a reduction of ROP. Vibration modeling and monitoring is a complex process often requiring many simplifying assumptions that may not always generalize to different BHAs, reservoirs, geology and formations. Therefore, it would be desirable to minimize drill string vibrations using data driven models using readily available drilling data. The hypothesis tested is the classification of stick slip severity due to drilling vibrations using open source machine learning algorithms. The stick slip index (SSI) – measuring the severity of stick slip due to drilling vibrations – is classified as low or high using machine learning classification algorithms such as logistic regression, support vector machines, random forests, gaussian mixture models and discriminant analysis. Each algorithm was evaluated based on classification accuracy, F-1 score and area under the receiver operating characteristic curve (AUC). The random forest algorithm outperforms other algorithms with an average accuracy of 90% (F-1 score of 0.91 and AUC score of 0.89). The classification model can then be used within a ROP optimization model (or framework) to determine optimal operation parameters which do not result in stick-slip conditions while drilling addressing a serious limitation of previously published ROP optimization papers.

Post hoc support vector machine learning for impedimetric biosensors based on weak protein-ligand interactions.

Impedimetric biosensors for measuring small molecules based on weak/transient interactions between bioreceptors and target analytes are a challenge for detection electronics, particularly in field studies or in the analysis of complex matrices. Protein-ligand binding sensors have enormous potential for biosensing, but achieving accuracy in complex solutions is a major challenge. There is a need for simple post hoc analytical tools that are not computationally expensive, yet provide near real time feedback on data derived from impedance spectra. Here, we show the use of a simple, open source support vector machine learning algorithm for analyzing impedimetric data in lieu of using equivalent circuit analysis. We demonstrate two different protein-based biosensors to show that the tool can be used for various applications. We conclude with a mobile phone-based demonstration focused on the measurement of acetone, an important biomarker related to the onset of diabetic ketoacidosis. In all conditions tested, the open source classifier was capable of performing as well as, or better, than the equivalent circuit analysis for characterizing weak/transient interactions between a model ligand (acetone) and a small chemosensory protein derived from the tsetse fly. In addition, the tool has a low computational requirement, facilitating use for mobile acquisition systems such as mobile phones. The protocol is deployed through Jupyter notebook (an open source computing environment available for mobile phone, tablet or computer use) and the code was written in Python. For each of the applications, we provide step-by-step instructions in English, Spanish, Mandarin and Portuguese to facilitate widespread use. All codes were based on scikit-learn, an open source software machine learning library in the Python language, and were processed in Jupyter notebook, an open-source web application for Python. The tool can easily be integrated with the mobile biosensor equipment for rapid detection, facilitating use by a broad range of impedimetric biosensor users. This post hoc analysis tool can serve as a launchpad for the convergence of nanobiosensors in planetary health monitoring applications based on mobile phone hardware.