Relevant bibliographies by topics / Machine learning not elsewhere classified

Academic literature on the topic 'Machine learning not elsewhere classified'

Author: Grafiati
Published: 10 December 2022
Last updated: 13 August 2024

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Journal articles on the topic "Machine learning not elsewhere classified"

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Yao, Hannah, Sina Rashidian, Xinyu Dong, Hongyi Duanmu, Richard N. Rosenthal, and Fusheng Wang. "Detection of Suicidality Among Opioid Users on Reddit: Machine Learning–Based Approach." Journal of Medical Internet Research 22, no. 11 (November 27, 2020): e15293. http://dx.doi.org/10.2196/15293.

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Background In recent years, both suicide and overdose rates have been increasing. Many individuals who struggle with opioid use disorder are prone to suicidal ideation; this may often result in overdose. However, these fatal overdoses are difficult to classify as intentional or unintentional. Intentional overdose is difficult to detect, partially due to the lack of predictors and social stigmas that push individuals away from seeking help. These individuals may instead use web-based means to articulate their concerns. Objective This study aimed to extract posts of suicidality among opioid users on Reddit using machine learning methods. The performance of the models is derivative of the data purity, and the results will help us to better understand the rationale of these users, providing new insights into individuals who are part of the opioid epidemic. Methods Reddit posts between June 2017 and June 2018 were collected from r/suicidewatch, r/depression, a set of opioid-related subreddits, and a control subreddit set. We first classified suicidal versus nonsuicidal languages and then classified users with opioid usage versus those without opioid usage. Several traditional baselines and neural network (NN) text classifiers were trained using subreddit names as the labels and combinations of semantic inputs. We then attempted to extract out-of-sample data belonging to the intersection of suicide ideation and opioid abuse. Amazon Mechanical Turk was used to provide labels for the out-of-sample data. Results Classification results were at least 90% across all models for at least one combination of input; the best classifier was convolutional neural network, which obtained an F1 score of 96.6%. When predicting out-of-sample data for posts containing both suicidal ideation and signs of opioid addiction, NN classifiers produced more false positives and traditional methods produced more false negatives, which is less desirable for predicting suicidal sentiments. Conclusions Opioid abuse is linked to the risk of unintentional overdose and suicide risk. Social media platforms such as Reddit contain metadata that can aid machine learning and provide information at a personal level that cannot be obtained elsewhere. We demonstrate that it is possible to use NNs as a tool to predict an out-of-sample target with a model built from data sets labeled by characteristics we wish to distinguish in the out-of-sample target.
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Hassan, Md Shareful, Md Tariqul Islam, and Mohammad Amir Hossain Bhuiyan. "Probable nexus between Methane and Air Pollution in Bangladesh using Machine Learning and Geographically Weighted Regression Modeling." Journal of Hyperspectral Remote Sensing 11, no. 3 (December 20, 2021): 136. http://dx.doi.org/10.29150/2237-2202.2021.251959.

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This paper investigates the probable nexus between methane (CH4) and air pollutants, a public health hazard in Bangladesh. The hypothesis considers that the concentration of CH4 is dependent on the ten air pollutants found in the five districts in Dhaka Division, a major urban and industrial area in Bangladesh. These pollutants are: Particular matters (PM2.5), Nitrogen dioxide (NO2), Nitrogen oxide (NOx), Aerosol optical thickness (AOT), Sulfur dioxide (SO2), Carbon monoxide (CO), Ozone (O3), Black carbon (BC), Formaldehyde (HCHO) and Dust. The study applies Machine Learning (ML) technique and Geographically Weighted Regression (GWR) Modeling. Temporal CH4 datasets from the Sentinel-5P sensor are classified to estimate the annual CH4 concentration during 2019-2021.Seven supervised classifiers of ML coupled with the GWR model are used to predict the statistical and spatial relationships. CH4 increases gradually during 2018-2021 in Dhaka, Gazipur, and Munshiganj Districts. It relates differently with various air pollutants, e.g., positively with BC, Dust, NO2, PM2.5, O3, and AOT, and negatively with NOx, CO, HCHO, and SO2.This study results that Rational quadratic (RMSE-0.001, MAE-0.001, R2-0.96), Random Forest (RMSE-0.004, MAE-0.003, R2-0.91), and Stepwise regression (RMSE-0.002, MAE-0.002, R2-0.87) are the suitable method in ML. The highest goodness-of-fit (R2) of 82%-96% is found in Dhaka and Narshingdi Districts. The key findings may help formulate the appropriate action plan to mitigate ongoing and future air pollution in Bangladesh. In addition, the methodology of the research may be applicable elsewhere nationally and internationally for air pollution research.
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Zhang, Meiling, Miaojun Zhu, Qin Hu, Chun Li, Zhenhua Zhu, Yingyong Hou, Jing Xu, et al. "Genome-wide microRNA expression profiling in malignant pleural effusion to identify a ten-microRNA signature." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): e23123-e23123. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.e23123.

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e23123 Background: Pleural carcinosis caused by tumors of the chest (e.g., lung and breast cancer) or elsewhere in the body (e.g., ovarian carcinoma) that metastasize to the visceral and/or parietal pleura. Recurrent malignant pleural effusion due to pleural carcinosis is one of the most common findings in oncology. The aim of the study was to identify a miRNA signature associated with clinicians’ prescription of patients with MPE. Methods: We used qRT-PCR assay to evaluate a wide range miRNAs (1920 miRNAs) signature measured from pleural effusion between patients with cancer and healthy controls. From the data of our previous studies we selected a panel of 96 miRNAs related to malignant pleural effusion. Data were compared by three classification methods software for statistics and modeling. We used a first set of 77 pleural effusion samples as training group, including 27 patients affected by Lung Adenocarcinomas (LAC), 9 with other lung cancer, 17 with other tumors and 24 inflammatory patients as negative controls. Moreover, we used more 64 pleural effusion samples for double-blind predictive study. Data analysis was performed using a machine learning approach of a Support Vector Machine classifier with a Student's t-test feature selection procedure. Results: We identified a panel of ten miRNAs with optimum classification performance. The combination of these 10-miRNAs alone could discriminate MPE cases from negative controls with an AUC of 0.969 (accuracy = 93.5%; specificity = 91.7%). The selected panel of another 10-miRNAs could separate lung cancer cases from negative controls with an AUC of 0.973 (accuracy = 94.8%; specificity = 95.8%), and a small panel of 4-miRNAs could good discriminate LAC cases from negative controls with an AUC of 0.946 (accuracy 88.9%; specificity = 100%). The accuracy rate of the double-blind predictive sensitivity value was 90.9% and specificity value was 95.8% for MPE patients with lung cancer of miRNA signature. Conclusions: Our miRNAs profile may serve as a new biomarker for MPE diagnosis. Otherwise, we identified a 10-miRNAs signature for MPE patients with lung cancer and a 4-miRNAs for MPE patients with lung adenocarcinoma.
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Chapman, Alec B., Kelly Peterson, Wathsala Widanagamaachchi, and Makoto M. Jones. "616. Predicting Misdiagnoses of Infectious Disease in Emergency Department Visits." Open Forum Infectious Diseases 8, Supplement_1 (November 1, 2021): S411. http://dx.doi.org/10.1093/ofid/ofab466.814.

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Abstract Background Diagnostic error leads to delays of care and mistaken therapeutic decisions that can cascade in a downward spiral. Thus, it is important to make accurate diagnostic decisions early on in the clinical care process, such as in the emergency department (ED). Clinical data from the Electronic Health Record (EHR) could identify cases where an initial diagnosis appears unusual in context. This capability could be developed into a quality measure for feedback. To that end, we trained a multiclass machine learning classifier to predict infectious disease diagnoses following an ED visit. Methods To train and evaluate our classifier, we sampled ED visits between December 31, 2016, and December 31, 2019, from Veterans Affairs (VA) Corporate Data Warehouse (CDW). Data elements used for prediction included lab orders and results, medication orders, radiology procedures, and vital signs. A multiclass XGBoost classifier was trained to predict one of five infectious disease classes for each ED visit based on the clinical variables extracted from CDW. Our model was trained on an enriched sample of 916,562 ED visits and evaluated on a non-enriched blind testing set of 356,549 visits. We compared our model against an ensemble of univariate Logistic Regression models as a baseline. Our model was trained to predict for an ED visit one of five infectious disease classes or “No Infection”. Labels were assigned to each ED visit based on ICD-9/10-CM diagnosis codes used elsewhere and other structured EHR data associated with a patient between 24 hours prior to an ED visit and 48 hours after. Results Classifier performance varied across each of the five disease classes (Table 1). The classifier achieved the highest F1 and AUC for UTI, the lowest F1 for Sepsis, and the lowest AUC for URI. We compared the average precision, recall and F1 scores of the multiclass XGBoost with the ensemble of Logistic Regression models (Table 2). XGBoost achieved higher scores in all three metrics. Table 1. Classification performance XGBoost testing set performance in each disease class, visits with no labels, and macro average. The infectious disease classes with the highest score in each metric are shown in bold. Table 2. Baseline comparison Macro average scores for XGBoost and baseline classifiers. Conclusion We trained a model to predict infectious disease diagnoses in the Emergency Department setting. Future work will further explore this technique and combine our supervised classifier with additional signs of medical error such as increased mortality or anomalous treatment patterns in order to study medical misdiagnosis. Disclosures All Authors: No reported disclosures
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Spelda, Petr, and Vit Stritecky. "Human Induction in Machine Learning." ACM Computing Surveys 54, no. 3 (June 2021): 1–18. http://dx.doi.org/10.1145/3444691.

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As our epistemic ambitions grow, the common and scientific endeavours are becoming increasingly dependent on Machine Learning (ML). The field rests on a single experimental paradigm, which consists of splitting the available data into a training and testing set and using the latter to measure how well the trained ML model generalises to unseen samples. If the model reaches acceptable accuracy, then an a posteriori contract comes into effect between humans and the model, supposedly allowing its deployment to target environments. Yet the latter part of the contract depends on human inductive predictions or generalisations, which infer a uniformity between the trained ML model and the targets. The article asks how we justify the contract between human and machine learning. It is argued that the justification becomes a pressing issue when we use ML to reach “elsewhere” in space and time or deploy ML models in non-benign environments. The article argues that the only viable version of the contract can be based on optimality (instead of on reliability, which cannot be justified without circularity) and aligns this position with Schurz's optimality justification. It is shown that when dealing with inaccessible/unstable ground-truths (“elsewhere” and non-benign targets), the optimality justification undergoes a slight change, which should reflect critically on our epistemic ambitions. Therefore, the study of ML robustness should involve not only heuristics that lead to acceptable accuracies on testing sets. The justification of human inductive predictions or generalisations about the uniformity between ML models and targets should be included as well. Without it, the assumptions about inductive risk minimisation in ML are not addressed in full.
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Cauvin, Bertrand, and Pierre Benning. "Machine Learning." International Journal of 3-D Information Modeling 6, no. 3 (July 2017): 1–16. http://dx.doi.org/10.4018/ij3dim.2017070101.

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A Bridge Data Dictionary contains an exhaustive list of terms used in the field of bridges. These terms are classified in systems in order to avoid any lacks, to identify all the expected object attributes, and to allow machines to understand the associated concepts. The main objectives of a Bridge Data Dictionary are many: ensure the sustainability of information over time; facilitate information exchange between the actors of the same project; ensure interoperability between the software packages. Other objectives have been reached during the process: to test a working methodology to be applied by other infrastructure domains (Roads, Rails, Tunnels, etc.); to check the current functions and capabilities of a buildingSMART Data Dictionary platform; and to define a common term list, in order to facilitate standardization and IFC-Bridge classes' development.
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Raji-Lawal, H. Y., A. O. Oloyede, O. Aiyeniko, P. E. Ishola, T. T. Ajagbe, and A. Abayomi-Alli. "ENSEMBLE OF MACHINE LEARNING CLASSIFIERS FOR SCHIZOPHRENIA DETECTION." Caleb International Journal of Development Studies 05, no. 02 (December 3, 2022): 386–405. http://dx.doi.org/10.26772/cijds-2022-05-02-20.

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Schizophenia disease is characterized by odd behavior, weird speech and decreased capacity to apprehend reality. The diagnosis of schizophrenia requires a complete and detailed medical examination. Machine learning has also helped computer scientists to classify and diagnose schizophrenia using neuroimaging data. This research implored the use of computer aided diagnosis to classify neuroimaging data of schizophrenia. The dataset of 86 instances which include 40 schizophenia patients, 46 healthy patients and 32 variable. They were obtained from Kaggle MLSF 2014 classification challenge and augmented due to small sized using synthetic minority oversampling technique (SMOTE). This yielded a larger data set of 1806 instances. The augmented data set were classified using machine learning algorithms support vector machine, K-neareast neighbours, logistic regression, Naïve bayes, artificial neural network. 350 instances was used for the training (70%) and 150 instances was used for testing (30%), KNN and SVM correctly classified 162 as Schizophrenia patients and classified 188 as healthy control, Tree correctly classified 159 as schizophrenia, mis-classified 3 as schizophrenia, correctly classified 185 as healthy and mis-classified 3 as healthy control, Logistic Regression correctly classified 139 as schizophrenia, mis-classified 23 as schizophrenia, correctly classified 170 as healthy and mis-classified 18 as healthy control, Naive Bayes correctly classified 139 as schizophrenia, mis- classified 23 as schizophrenia, correctly classified 166 as healthy and mis-classified 22 as healthy control. ANN used 549instances, 60% for training, 20% for testing and 20% for validation got an accuracy of 100%, this makes it the best classification method.
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Yuyun, Yuyun. "KLASIFIKASI SURAT DIGITAL MENGGUNAKAN ALGORITMA MACHINE LEARNING." JURNAL IT 13, no. 2 (August 30, 2023): 66–71. http://dx.doi.org/10.37639/jti.v13i2.350.

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Penelitian ini mengimplementasikan algoritm algoritma naive bayes dalam proses klasifikasi surat dan untuk membangun sistem yang dapat mengklasifikasi surat secara. Dalam penelitian ini jumlah sampel data corpus surat 1036 record, yang dibagi dalam 80% training dan 20% testing. Sampel data training algoritma Naïve Bayes di implementasikan dengan menghitung nilai bobot dari class tertinggi berdasarkan data training dengan data testing sehingga menghasilkan probabilitas tertinggi. Hasil pengolahan data mendapatkan nilai Correctly Classified Instance sebesar 86.245799% dan Incoreectly Classified Instance sebesar 13.754200% serta hasil pengujian dengan menggunakan confusion matrix mendapatkan nilai precision sebesar 86%, Recall 86 % dan Akurasi sebesar 76%.
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Hiray, Prof S. R. "Book Recommendation System Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 1981–83. http://dx.doi.org/10.22214/ijraset.2021.39658.

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Abstract: Users can use book recommendation systems to search and select books from a number of options available on the web or elsewhere electronic sources. They give the user a little bit selection of products that fit the description, given a large group of objects and a description of the user needs. Our system will simply provide recommendations. Recommendations are based on previous user activity, such as purchase, habits, reviews, and likes. These systems gain lot of interest. In the proposed system, we have a big problem: when the user buys book, we want to recommend some books that the user can enjoy. Buyers also have a great deal of options when it comes to recommending the best and most appropriate books for them. User development privacy while placing small and minor losses of accuracy. Recommendations. The proposed recommendation system will provide user's ability to view and search the publications and using the Support Vector Machine (SVM), will list the most purchased and top rated books based on the subject name given as input. Keywords: Recommender System, Support Vector Machine (SVM), Machine Learning, Classification etc.
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Kulkarni, Prasad, Suyash Karwande, Rhucha Keskar, Prashant Kale, and Sumitra Iyer. "Fake News Detection using Machine Learning." ITM Web of Conferences 40 (2021): 03003. http://dx.doi.org/10.1051/itmconf/20214003003.

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Everyone depends upon various online resources for news in this modern age, where the internet is pervasive. As the use of social media platforms such as Facebook, Twitter, and others has increased, news spreads quickly among millions of users in a short time. The consequences of Fake news are far-reaching, from swaying election outcomes in favor of certain candidates to creating biased opinions. WhatsApp, Instagram, and many other social media platforms are the main source for spreading fake news. This work provides a solution by introducing a fake news detection model using machine learning. This model requires prerequisite data extracted from various news websites. Web scraping technique is used for data extraction which is further used to create datasets. The data is classified into two major categories which are true dataset and false dataset. Classifiers used for the classification of data are Random Forest, Logistic Regression, Decision Tree, KNN and Gradient Booster. Based on the output received the data is classified either as true or false data. Based on that, the user can find out whether the given news is fake or not on the webserver.
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Dissertations / Theses on the topic "Machine learning not elsewhere classified"

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Abo, Al Ahad George, and Abbas Salami. "Machine Learning for Market Prediction : Soft Margin Classifiers for Predicting the Sign of Return on Financial Assets." Thesis, Linköpings universitet, Produktionsekonomi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-151459.

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Forecasting procedures have found applications in a wide variety of areas within finance and have further shown to be one of the most challenging areas of finance. Having an immense variety of economic data, stakeholders aim to understand the current and future state of the market. Since it is hard for a human to make sense out of large amounts of data, different modeling techniques have been applied to extract useful information from financial databases, where machine learning techniques are among the most recent modeling techniques. Binary classifiers such as Support Vector Machines (SVMs) have to some extent been used for this purpose where extensions of the algorithm have been developed with increased prediction performance as the main goal. The objective of this study has been to develop a process for improving the performance when predicting the sign of return of financial time series with soft margin classifiers. An analysis regarding the algorithms is presented in this study followed by a description of the methodology that has been utilized. The developed process containing some of the presented soft margin classifiers, and other aspects of kernel methods such as Multiple Kernel Learning have shown pleasant results over the long term, in which the capability of capturing different market conditions have been shown to improve with the incorporation of different models and kernels, instead of only a single one. However, the results are mostly congruent with earlier studies in this field. Furthermore, two research questions have been answered where the complexity regarding the kernel functions that are used by the SVM have been studied and the robustness of the process as a whole. Complexity refers to achieving more complex feature maps through combining kernels by either adding, multiplying or functionally transforming them. It is not concluded that an increased complexity leads to a consistent improvement, however, the combined kernel function is superior during some of the periods of the time series used in this thesis for the individual models. The robustness has been investigated for different signal-to-noise ratio where it has been observed that windows with previously poor performance are more exposed to noise impact.
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(10506350), Amogh Agrawal. "Compute-in-Memory Primitives for Energy-Efficient Machine Learning." Thesis, 2021.

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Machine Learning (ML) workloads, being memory and compute-intensive, consume large amounts of power running on conventional computing systems, restricting their implementations to large-scale data centers. Thus, there is a need for building domain-specific hardware primitives for energy-efficient ML processing at the edge. One such approach is in-memory computing, which eliminates frequent and unnecessary data-transfers between the memory and the compute units, by directly computing the data where it is stored. Most of the chip area is consumed by on-chip SRAMs in both conventional von-Neumann systems (e.g. CPU/GPU) as well as application-specific ICs (e.g. TPU). Thus, we propose various circuit techniques to enable a range of computations such as bitwise Boolean and arithmetic computations, binary convolution operations, non-Boolean dot-product operations, lookup-table based computations, and spiking neural network implementation - all within standard SRAM memory arrays.

First, we propose X-SRAM, where, by using skewed sense amplifiers, bitwise Boolean operations such as NAND/NOR/XOR/IMP etc. can be enabled within 6T and 8T SRAM arrays. Moreover, exploiting the decoupled read/write ports in 8T SRAMs, we propose read-compute-store scheme where the computed data can directly be written back in the array simultaneously.

Second, we propose Xcel-RAM, where we show how binary convolutions can be enabled in 10T SRAM arrays for accelerating binary neural networks. We present charge sharing approach for performing XNOR operations followed by a population count (popcount) using both analog and digital techniques, highlighting the accuracy-energy tradeoff.

Third, we take this concept further and propose CASH-RAM, to accelerate non-Boolean operations, such as dot-products within standard 8T-SRAM arrays by utilizing the parasitic capacitances of bitlines and sourcelines. We analyze the non-idealities that arise due to analog computations and propose a self-compensation technique which reduces the effects of non-idealities, thereby reducing the errors.

Fourth, we propose ROM-embedded caches, RECache, using standard 8T SRAMs, useful for lookup-table (LUT) based computations. We show that just by adding an extra word-line (WL) or a source-line (SL), the same bit-cell can store a ROM bit, as well as the usual RAM bit, while maintaining the performance and area-efficiency, thereby doubling the memory density. Further we propose SPARE, an in-memory, distributed processing architecture built on RECache, for accelerating spiking neural networks (SNNs), which often require high-order polynomials and transcendental functions for solving complex neuro-synaptic models.

Finally, we propose IMPULSE, a 10T-SRAM compute-in-memory (CIM) macro, specifically designed for state-of-the-art SNN inference. The inherent dynamics of the neuron membrane potential in SNNs allows processing of sequential learning tasks, avoiding the complexity of recurrent neural networks. The highly-sparse spike-based computations in such spatio-temporal data can be leveraged for energy-efficiency. However, the membrane potential incurs additional memory access bottlenecks in current SNN hardware. IMPULSE triew to tackle the above challenges. It consists of a fused weight (WMEM) and membrane potential (VMEM) memory and inherently exploits sparsity in input spikes. We propose staggered data mapping and re-configurable peripherals for handling different bit-precision requirements of WMEM and VMEM, while supporting multiple neuron functionalities. The proposed macro was fabricated in 65nm CMOS technology. We demonstrate a sentiment classification task from the IMDB dataset of movie reviews and show that the SNN achieves competitive accuracy with only a fraction of trainable parameters and effective operations compared to an LSTM network.

These circuit explorations to embed computations in standard memory structures shows that on-chip SRAMs can do much more than just store data and can be re-purposed as on-demand accelerators for a variety of applications.
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(9189263), Shuo Han. "FLUORESCENCE MICROSCOPY IMAGES SEGMENTATION AND ANALYSIS USING MACHINE LEARNING." Thesis, 2020.

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Microscopy image analysis can provide substantial information for clinical study and understanding of the biological structure. Two-photon microscopy is a type of fluorescence microscopy that can visualize deep into tissue with near-infrared excitation light. Large 3D image volumes of complex subcellular are often produced, which calls for automatic image analysis techniques. Automatic methods that can obtain nuclei quantity in microscopy image volumes are needed for biomedical research and clinical diagnosis. In general, several challenges exist for counting nuclei in 3D image volumes. These include “crowding” and touching of nuclei, overlapping of two or morenuclei, and shape and size variances of the nuclei. In this thesis, a 3D nuclei counterusing two different generative adversarial networks (GAN) is proposed and evaluated.Synthetic data that resembles real microscopy image is generated with a GAN. The synthetic data is used to train another 3D GAN network that counts the number o fnuclei. Our approach is evaluated with respect to the number of ground truth nuclei and compared with common ways of counting used in the biological research.Fluorescence microscopy 3D image volumes of rat kidneys are used to test our 3D nuclei counter. The evaluation of both networks shows that the proposed technique is successful for counting nuclei in 3D. Then, a 3D segmentation and classification method to segment and identify individual nuclei in fluorescence microscopy volumes without having ground truth volumes is introduced. Three dimensional synthetic data is generated using the Recycle-GAN with the Hausdorff distance loss introduced into preserve the shape of individual nuclei. Realistic microscopy image volumes with nuclei segmentation mask and nucleus boundary ground truth volumes are generated.A subsequent 3D CNN with a regularization term that discourage detection out of nucleus boundary is used to detect and segment nuclei. Nuclei boundary refinement is then performed to enhance nuclei segmentation. Experimental results on our rat kidney dataset show the proposed method is competitive with respect to several state-of-the-art methods. A Distributed and Networked Analysis of Volumetric Image Data(DINAVID) system is developed to enable remote analysis of microscopy images for biologists. There are two main functions integrated in the system, a 3D visualization tool and a remote computing tool for nuclei segmentation. The 3D visualization enables real-time rendering of large volumes of microscopy data. The segmentation tool provides fast inferencing of pre-trained deep learning models trained with 5 different types of microscopy data.

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(5930189), Javier Ribera Prat. "Image-based Plant Phenotyping Using Machine Learning." Thesis, 2019.

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Phenotypic data is of crucial importance for plant breeding in estimating a plant's biomass. Traits such as leaf area and plant height are known to be correlated with biomass. Image analysis and computer vision methods can automate data analysis for high-throughput phenotyping. Many methods have been proposed for plant phenotyping in controlled environments such as greenhouses. In this thesis, we present multiple methods to estimate traits of the plant crop sorghum from images acquired from UAV and field-based sensors. We describe machine learning techniques to extract the plots of a crop field, a method for leaf counting from low-resolution images, and a statistical model that uses prior information about the field structure to estimate the center of each plant. We also develop a new loss function to train Convolutional Neural Networks (CNNs) to count and locate objects of any type and use it to estimate plant centers. Our methods are evaluated with ground truth of sorghum fields and publicly available datasets and are shown to outperform the state of the art in generic object detection and domain-specific tasks.

This thesis also examines the use of crowdsourcing information in video analytics. The large number of cameras deployed for public safety surveillance systems requires intelligent processing capable of automatically analyzing video in real time. We incorporate crowdsourcing in an online basis to improve a crowdflow estimation method. We present various approaches to characterize this uncertainty and to aggregate crowdsourcing results. Our techniques are evaluated using publicly available datasets.
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(7534550), David Güera. "Media Forensics Using Machine Learning Approaches." Thesis, 2019.

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Consumer-grade imaging sensors have become ubiquitous in the past decade. Images and videos, collected from such sensors are used by many entities for public and private communications, including publicity, advocacy, disinformation, and deception.
In this thesis, we present tools to be able to extract knowledge from and understand this imagery and its provenance. Many images and videos are modified and/or manipulated prior to their public release. We also propose a set of forensics and counter-forensic techniques to determine the integrity of this multimedia content and modify it in specific ways to deceive adversaries. The presented tools are evaluated using publicly available datasets and independently organized challenges.
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(9136835), Sungbum Jun. "SCHEDULING AND CONTROL WITH MACHINE LEARNING IN MANUFACTURING SYSTEMS." Thesis, 2020.

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Numerous optimization problems in production systems can be considered as decision-making processes that determine the best allocation of resources to tasks over time to optimize one or more objectives in concert with big data. Among the optimization problems, production scheduling and routing of robots for material handling are becoming more important due to their impacts on system performance. However, the development of efficient algorithms for scheduling or routing faces several challenges. While the scheduling and vehicle routing problems can be solved by mathematical models such as mixed-integer linear programming to find optimal solutions to smallsized problems, they are not applicable to larger problems due to the nature of NP-hard problems. Thus, further research on machine learning applications to those problems is a significant step towards increasing the possibilities and potentialities of field application. In order to create truly intelligent systems, new frameworks for scheduling and routing are proposed to utilize machine learning (ML) techniques. First, the dynamic single-machine scheduling problem for minimization of total weighted tardiness is addressed. In order to solve the problem more efficiently, a decisiontree-based approach called Generation of Rules Automatically with Feature construction and Treebased learning (GRAFT) is designed to extract dispatching rules from existing or good schedules. In addition to the single-machine scheduling problem, the flexible job-shop scheduling problem with release times for minimizing the total weighted tardiness is analyzed. As a ML-based solution approach, a random-forest-based approach called Random Forest for Obtaining Rules for Scheduling (RANFORS) is developed to solve the problem by generating dispatching rules automatically. Finally, an optimization problem for routing of autonomous robots for minimizing total tardiness of transportation requests is analyzed by decomposing it into three sub-problems. In order to solve the sub-problems, a comprehensive framework with consideration of conflicts between routes is proposed. Especially to the sub-problem for vehicle routing, a new local search algorithm called COntextual-Bandit-based Adaptive Local search with Tree-based regression (COBALT) that incorporates the contextual bandit into operator selection is developed. The findings from my research contribute to suggesting a guidance to practitioners for the applications of ML to scheduling and control problems, and ultimately to lead the implementation of smart factories.
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(8812160), Alex Joseph Raynor. "DEVELOPMENT OF MACHINE LEARNING TECHNIQUES FOR APPLICATIONS IN THE STEEL INDUSTRY." Thesis, 2020.

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For a long time, the collection of data through sensors and other means was seen as inconsequential. However, with the somewhat recent developments in the areas of machine learning, data science, and statistical analysis, as well as in the rapid growth of computational power being allotted by the ever-expanding computer industry, data is not just being seen as secondhand information anymore. Data collection is showing that it currently is and will continue to be a major driving force in many applications, as the predictive power it can provide is invaluable. One such area that could benefit dramatically from the use of predictive techniques is the steel industry. This thesis applied several machine learning techniques to predict steel deformation issues collectively known as the hook index problem [1].

The first machine learning technique utilized in this endeavor was neural networking. The neural networks built and tested in this research saw the use of classification and regression prediction models. They also implemented the algorithms of gradient descent and adaptive moment estimation. Through the employment of these networks and learning strategies, as well as through the line process data, regression-based networks made predictions with average percent error ranging from 106-114%. In similar performance to the regression-based networks, classification-based networks made predictions with average accuracy percentage ranges of 38-40%.

To remedy the problems relating to neural networks, Bayesian networking techniques were implemented. The main method that was used as a model for these networks was the Naïve Bayesian framework. Also, variable optimization techniques were utilized to create well-performing network structures. In the same vein as the neural networks, Bayesian networks used line process data to make predictions. The classification-based networks made predictions with average accuracy ranges of 64-65%. Because of the increased accuracy results and their ability to draw causal reasoning from data, Bayesian networking was the preferred machine learning technique for this research application.
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(9811085), Anand Koirala. "Precision agriculture: Exploration of machine learning approaches for assessing mango crop quantity." Thesis, 2020. https://figshare.com/articles/thesis/Precision_agriculture_Exploration_of_machine_learning_approaches_for_assessing_mango_crop_quantity/13411625.

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A machine vision based system is proposed to replace the current in-orchard manual estimates of mango fruit yield, to inform harvest resourcing and marketing. The state-of-the-art in fruit detection was reviewed, highlighting the recent move from traditional image segmentation methods to convolution neural network (CNN) based deep learning methods. An experimental comparison of several deep learning based object detection frameworks (single shot detectors versus two-staged detectors) and several standard CNN architectures was undertaken for detection of mango panicles and fruit in tree images. The machine vision system used images of individual trees captured during night time from a moving platform mounted with a Global Navigation Satellite System (GNSS) receiver and a LED panel floodlight. YOLO, a single shot object detection framework, was re-designed and named as MangoYOLO. MangoYOLO outperformed existing state-of-the-art deep learning object detection frameworks in terms of fruit detection time and accuracy and was robust in use across different cultivars and cameras. MangoYOLO achieved F1 score of 0.968 and average precision of 0.983 and required just 70 ms per image (2048 × 2048 pixel) and 4417 MB memory. The annotated image dataset was made publicly available. Approaches were trialled to relate the fruit counts from tree images to the actual harvest count at an individual tree level. Machine vision based estimates of fruit load ranged between -11% to +14% of packhouse fruit counts. However, estimation of fruit yield (t/ha) requires estimation of fruit size as well as fruit number. A fruit sizing app for smart phones was developed as an affordable in-field solution. The solution was based on segmentation of the fruit in image using colour features and estimation of the camera to fruit perimeter distance based on use of fruit allometrics. For mango fruit, RMSEs of 5.3 and 3.7 mm were achieved on length and width measurements under controlled lighting, and RMSEs of 5.5 and 4.6 mm were obtained in-field under ambient lighting. Further, estimation of harvest timing can be informed by assessment of the spread of flowering. Deep learning object detection methods were deployed for assessment of the number and development stage of mango panicles, on tree. Methods to deal with different orientations of flower panicles in tree images were implemented. An R2 >0.8 was achieved between machine vision count of panicles on images and in-field human count per tree. Similarly, mean average precision of 69.1% was achieved for classification of panicle stages. These machine vision systems form a foundation for estimation of crop load and harvest timing, and for automated harvesting.
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(11173365), Youlin Liu. "MACHINE LEARNING METHODS FOR SPECTRAL ANALYSIS." Thesis, 2021.

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Measurement science has seen fast growth of data in both volume and complexity in recent years, new algorithms and methodologies have been developed to aid the decision
making in measurement sciences, and this process is automated for the liberation of labor. In light of the adversarial approaches shown in digital image processing, Chapter 2 demonstrate how the same attack is possible with spectroscopic data. Chapter 3 takes the question presented in Chapter 2 and optimized the classifier through an iterative approach. The optimized LDA was cross-validated and compared with other standard chemometrics methods, the application was extended to bi-distribution mineral Raman data. Chapter 4 focused on a novel Artificial Neural Network structure design with diffusion measurements; the architecture was tested both with simulated dataset and experimental dataset. Chapter 5 presents the construction of a novel infrared hyperspectral microscope for complex chemical compound classification, with detailed discussion in the segmentation of the images and choice of a classifier to choose.
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(10713342), Judy Yang. "Development of a warehouse model using machine learning technologies with application in receiving management." Thesis, 2021. https://figshare.com/articles/thesis/Development_of_a_warehouse_model_using_machine_learning_technologies_with_application_in_receiving_management/14499078.

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Warehouse management is a significant element of a supply chain. The warehouse's data accuracy is the foundation of the whole supply chain material flow, information flow, and capital flow. It requires the warehouse receiving management to have an accurate system that operates in real-time to collect receiving data. However, conventional solutions for data management at the receiving stage are managed manually. Human involvement in the current receiving stage means that data accuracy and real-time entry cannot be assured. This research aims to improve data accuracy and reduce human error in the warehouse receiving management. The purpose of this research is to develop a suitable Artificial Neural Network (ANN) model by using machine learning technologies, which can be applied to warehouse receiving management to improve data accuracy and reduce human errors. Based on comprehensive literature review and analysis of current algorithms, a conceptual ANN model, identified as the Artificial Neural Network for Components Identification and Counting (ANN-CIC) model, is proposed to perform components classification and counting. This study has evaluated three classic image identification algorithms by using sixteen groups of industrial components to compare classification performance. A modified white histogram correlation coefficient approach is chosen as the design model's classification algorithm after experiments. Besides, the counting model is tested. The model is verified with an enlarged dataset obtained from a local Australian Company. The simulation results demonstrated that the proposed model achieved a 91.37% accuracy rate in object classification and a 94.29% in object counting, which has outperformed the existing classical model accuracy rate, such as for VGG-16. The main contributions of this research can be highlighted as below: Firstly, a conceptual ANN-CIC model is proposed to perform the identification and counting of industrial components. Four basic geometric shapes as the attributes of images for shape analysis and pre-defined features are introduced. These introduced shapes assisted in verifying the feasibility of the preliminary experiments. Secondly, the white histogram correlation coefficient algorithm is improved by adjusting the colour ratio to achieve outstanding performance in the classification of various industrial components. Lastly, the model is simulated with industrial data that demonstrates its applicability and stability. Moreover, higher classification and counting accuracy rate are achieved, and the design goal is also achieved.
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Books on the topic "Machine learning not elsewhere classified"

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Gries, Stefan Th. Data in Construction Grammar. Edited by Thomas Hoffmann and Graeme Trousdale. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780195396683.013.0006.

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This chapter examines the types of data used in constructionist approaches and the parameters along which data types can be classified. It discusses different kinds of quantitative observational/corpus data (frequencies, probabilities, association measures) and their statistical analysis. In addition, it provides a survey of a variety of different experimental data (novel word/construction learning, priming, sorting, etc.). Finally, the chapter discusses computational-linguistic/machine-learning methods as well as new directions for the development of new data and methods in Construction Grammar.
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Book chapters on the topic "Machine learning not elsewhere classified"

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Emde, Werner. "Inductive learning of characteristic concept descriptions from small sets of classified examples." In Machine Learning: ECML-94, 103–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-57868-4_53.

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Cleophas, Ton J., and Aeilko H. Zwinderman. "Restructure Data Wizard for Data Classified the Wrong Way (20 Patients)." In Machine Learning in Medicine - a Complete Overview, 101–4. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15195-3_17.

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Cleophas, Ton J., and Aeilko H. Zwinderman. "Restructure Data Wizard for Data Classified the Wrong Way (20 Patients)." In Machine Learning in Medicine – A Complete Overview, 117–21. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-33970-8_17.

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Disabato, Simone. "Deep and Wide Tiny Machine Learning." In Special Topics in Information Technology, 79–92. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15374-7_7.

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AbstractIn the last decades, on the one hand, Deep Learning (DL) has become state of the art in several domains, e.g., image classification, object detection, and natural language processing. On the other hand, pervasive technologies—Internet of Things (IoT) units, embedded systems, and Micro-Controller Units (MCUs)—ask for intelligent processing mechanisms as close as possible to data generation. Nevertheless, memory, computational, and energy requirements characterizing DL models are three or more orders of magnitude larger than the corresponding memory, computation, and energy capabilities of pervasive devices. This work aims at introducing a methodology to address this issue and enable pervasive intelligent processing. In particular, by defining Tiny Machine Learning (TML) solutions, i.e., machine and deep learning models that take into account the constraints on memory, computation, and energy of the target pervasive device. The proposed methodology addresses the problem at three different levels. In the first approach, the methodology devices inference-based Deep TML solutions by approximation techniques, i.e., the TML model runs on the pervasive device but was trained elsewhere. Then, the methodology introduces on-device learning for TML. Finally, the third approach develops Wide Deep TML solutions that split and distribute the DL processing over connected heterogeneous pervasive devices.
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Karimanzira, Divas, and Helge Renkewitz. "Detection and localization of an underwater docking station in acoustic images using machine learning and generalized fuzzy hough transform." In Machine Learning for Cyber Physical Systems, 23–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-62746-4_3.

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AbstractLong underwater operations with autonomous battery charging and data transmission require an Autonomous Underwater Vehicle (AUV) with docking capability, which in turn presume the detection and localization of the docking station. Object detection and localization in sonar images is a very difficult task due to acoustic image problems such as, non-homogeneous resolution, non-uniform intensity, speckle noise, acoustic shadowing, acoustic reverberation and multipath problems. As for detection methods which are invariant to rotations, scale and shifts, the Generalized Fuzzy Hough Transform (GFHT) has proven to be a very powerful tool for arbitrary template detection in a noisy, blurred or even a distorted image, but it is associated with a practical drawback in computation time due to sliding window approach, especially if rotation and scaling invariance is taken into account. In this paper we use the fact that the docking station is made out of aluminum profiles which can easily be isolated using segmentation and classified by a Support Vector Machine (SVM) to enable selective search for the GFHT. After identification of the profile locations, GFHT is applied selectively at these locations for template matching producing the heading and position of the docking station. Further, this paper describes in detail the experiments that validate the methodology.
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Papenberg, Björn, Sebastian Hogreve, and Kirsten Tracht. "Machine Learning as an Enabler for Automated Assistance Systems for the Classification of Tool Wear on Milling Tools." In Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2022, 27–38. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-10071-0_3.

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AbstractTool wear and the decision when to replace tools is a universal challenge in the metal cutting industry. While the tool wear state can be accurately determined using optical measuring methods, the tool wear of milling tools is often examined by the CNC-machine operators, especially in small and medium enterprises. In order to increase the accuracy with which tool wear can be correctly classified, it is advisable to use an assistance system that automatically removes the tools from a buffer, examines the tool wear state based on visual sensor data and sorts them into separate boxes according to the classification result. In this context, the accurate classification of tool wear is a key capability that can be enabled using methods of machine learning, based on image data that was labeled by human experts. In this paper different machine learning models are examined based on their ability to classify images of milling tools into the categories worn and not worn. The EfficientNet_b0 model achieves an accuracy of 91.47% and outperforms human experts that classified similar images by 22.87%.
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Vijayaragavan, P., R. Ponnusamy, and M. Arrmuthan. "Automated Socio-psycho-economic Knowledge Behavior Classified in E-Commerce Applying Various Machine Learning Techniques." In Information and Communication Technology for Sustainable Development, 405–13. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7166-0_40.

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Mumu, Sabrina Mostafij, Hasibul Hoque, and Nazmus Sakib. "A Classified Mental Health Disorder (ADHD) Dataset Based on Ensemble Machine Learning from Social Media Platforms." In Proceedings of the Fourth International Conference on Trends in Computational and Cognitive Engineering, 395–405. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9483-8_33.

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Suda, Martin. "Improving ENIGMA-style Clause Selection while Learning From History." In Automated Deduction – CADE 28, 543–61. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79876-5_31.

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AbstractWe re-examine the topic of machine-learned clause selection guidance in saturation-based theorem provers. The central idea, recently popularized by the ENIGMA system, is to learn a classifier for recognizing clauses that appeared in previously discovered proofs. In subsequent runs, clauses classified positively are prioritized for selection. We propose several improvements to this approach and experimentally confirm their viability. For the demonstration, we use a recursive neural network to classify clauses based on their derivation history and the presence or absence of automatically supplied theory axioms therein. The automatic theorem prover Vampire guided by the network achieves a 41 % improvement on a relevant subset of SMT-LIB in a real time evaluation.
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Singstad, Bjørn Jostein, Bendik Steinsvåg Dalen, Sandhya Sihra, Nickolas Forsch, and Samuel Wall. "Identifying Ionic Channel Block in a Virtual Cardiomyocyte Population Using Machine Learning Classifiers." In Computational Physiology, 91–109. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05164-7_8.

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AbstractImmature cardiomyocytes, such as those obtained by stem cell differentiation, have been shown to be useful alternatives to mature cardiomyocytes, which are limited in availability and difficult to obtain, for evaluating the behaviour of drugs for treating arrhythmia. In silico models of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) can be used to simulate the behaviour of the transmembrane potential and cytosolic calcium under drug-treated conditions. Simulating the change in action potentials due to various ionic current blocks enables the approximation of drug behaviour. We used eight machine learning classification models to predict partial block of seven possible ion currents $$ (\textit{I}_{\textit{CaL}},\textit{I}_{\textit{Kr}},\textit{I}_{\textit{to}},\textit{I}_{\textit{K1}},\textit{I}_{\textit{Na}},\textit{I}_{\textit{NaL}} and \textit{I}_{\textit{Ks}}) $$ in a simulated dataset containing nearly 4600 action potentials represented as a paired measure of transmembrane potential and cytosolic calcium. Each action potential was generated under 1 $$ \textit{H}_{\textit{z}} $$ pacing. The Convolutional Neural Network outperformed the other models with an average accuracy of predicting partial ionic current block of 93% in noise-free data and 72% accuracy with 3% added random noise. Our results show that $$ \textit{I}_{\textit{CaL}} $$ and $$ \textit{I}_{\textit{Kr}} $$ current block were classified with high accuracy with and without noise. The classification of $$ \textit{I}_{\textit{to}} $$ , $$ \textit{I}_{\textit{K1}} $$ and $$ \textit{I}_{\textit{Na}} $$ current block showed high accuracy at 0% noise, but showed a significant decrease in accuracy when noise was added. Finally, the accuracy of $$ \textit{I}_{\textit{NaL}} $$ and $$ \textit{I}_{\textit{Ks}} $$ classification were relatively lower than the other current blocks at 0% noise and also showed a significant drop in accuracy when noise was added. In conclusion, these machine learning methods may present a pathway for estimating drug response in adult phenotype cardiac systems, but the data must be sufficiently filtered to remove noise before being used with classifier algorithms.
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Conference papers on the topic "Machine learning not elsewhere classified"

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Adnane, Marouane, Mohammed El, Sanaa El Fkihi, and Rachid Oulad Haj Thami. "Prediction Demand for Classified Ads Using Machine Learning." In the 2nd International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3320326.3320371.

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Ranawake, Dhanuja, Savandi Bandaranayake, Ravihari Jayasekara, Imashi Madhushani, Manori Gamage, and Suriyaa Kumari. "Tievs: Classified Advertising Enhanced Using Machine Learning Techniques." In 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). IEEE, 2021. http://dx.doi.org/10.1109/iemcon53756.2021.9623100.

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Ding, Shi-fei, Zhong-zhi Shi, and Xi-jun Zhu. "Information Classified Recognition Method Based on Fuzzy Control." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.259117.

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Chang-Shun Yan and Yi-Jun Li. "Classified forgetting neural network and its effectiveness analysis." In Proceedings of 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005. http://dx.doi.org/10.1109/icmlc.2005.1527646.

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Yang, Xu, De Xu, and Ying-Jian Qi. "Bag-of-words image representation based on classified vector quantization." In 2010 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.5580564.

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Itikawa, M. A., V. R. R. Ahón, T. A. Souza, A. M. V. Carrasco, J. C. Q. Neto, J. L. S. Gomes, R. R. H. Cavalcante, et al. "Automatic Cement Evaluation Using Machine Learning." In Offshore Technology Conference Brasil. OTC, 2023. http://dx.doi.org/10.4043/32961-ms.

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Abstract Cementing is an extremely important step in the well construction process. It has important objectives such as hydraulic sealing to prevent migration of undesired fluids from the formations and their collapse. One of the methods to verify the quality of cementat jobs is running acoustic logging tools such as CBL/VDL and ultrasonic and inferring zonal isolation by the interpretation of such data. This study aims to use machine learning techniques for automatic cement logs interpration. Cement logs of 25 wells were used as database. The logs responses have been classified in five classes according to the bond quality by specialized interpreters. These classified segments were used to train neural networks and other supervised machine learning models, such as random forests and k-nearest neighbor (KNN). Feature engineering is used in order to find new and high-performance features. The models were developed in a Jupyter environment using Python libraries. The best classifier has a simple accuracy of 61.4% and approximate accuracy (where the prediction is up to one class away from target) of 91.3%.
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Wu, Binlin, Yan Zhou, Liang Zhang, Shengjia Zhang, Xinguang Yu, Eric Wang, Ke Zhu, Cheng-hui Liu, and Robert R. Alfano. "Glioma Tumors Classified using Visible Resonance Raman Spectroscopy and Machine Learning." In Frontiers in Optics. Washington, D.C.: OSA, 2020. http://dx.doi.org/10.1364/fio.2020.jw6a.17.

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Alexstan, Aarone Steve J., Krishna M. Monesh, M. Poonkodi, and Vineet Raj. "Used Car Price Prediction Using Machine Learning." In International Research Conference on IOT, Cloud and Data Science. Switzerland: Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-9x4ue8.

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The increase in new cars and customers' economic inability, global sales of old cars are expanding. As a result, there exists a pressing need for a second-hand automobile method for predicting prices that accurately calculates the value of a car based on number of factors. In the current circumstance, the existing system involves a mechanism in which a seller sets a price at random and the buyer has no knowledge of the car or its value. In fact, the seller doesn't even know the current value of the car or the price at which the car should be sold. To solve this problem, we have developed a very effective model. Regression algorithms are used to provide continuous values as output rather than classified values. This allows for the prediction of the car's real price rather than its price range.
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Domnik, Jan, and Alexander Holland. "On Data Leakage Prevention And Machine Learning." In Digital Restructuring and Human (Re)action. University of Maribor Press, 2022. http://dx.doi.org/10.18690/um.fov.4.2022.45.

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An analyst in the field of Data Leakage Prevention (DLP) usually inspects suspicious file transfers which are called events. First of all, the data in question is classified. Then, the context of the transfer is determined. After this, the analyst decides whether the transfer was legitimate or not. This process is widely known as triage. It is monotonous, costly and resourceintensive. Therefore the following question arises; could modern DLP-Software utilize machine learning algorithms in order to automate the triage process? Further, this begs the question, which structural and organisational processes are necessary inside an organisation to automate that process. In this case, it could significantly enhance the quality of DLP practices and take work from the much needed human resources in the field of IT security. Further, DLP systems (today usually used in bigger organisations) could become more attractive and more specifically affordable for small- and medium-sized organisations.
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KAISER, ISAIAH, NATALIE RICHARDS, and K. T. TAN. "MACHINE LEARNING FOR STRENGTH AND DAMAGE PREDICTION OF ADHESIVE JOINTS." In Proceedings for the American Society for Composites-Thirty Seventh Technical Conference. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/asc37/36375.

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In this study, machine learning (ML), a subdivision of artificial intelligence (AI), is implemented to study the mechanical behavior of adhesive single-lap joints (SLJs) subjected to tensile loading. The experimental data for training and testing the ML models are compiled from peer-reviewed journal papers to eliminate bias and increase the diversity within the data. The dataset is comprised of eight continuous SLJ parameters, which are used to predict the SLJ damage mode and failure strength. To accomplish this, regression and classification models are built using deep neural networks (DNN) and random forests (RF). Finite element (FE) modeling is conducted, and the performance is compared with the accuracy of the regression ML models. Results show ML models were able to predict strength with higher accuracy than FE modeling. Furthermore, both DNN and RF classified damage mode accurately without failure criteria, exposing limitations within FE modeling. As a result, this study introduces the use of ML for strength and damage mode prediction of adhesive SLJ, providing insights into their mechanical behavior, revealing hidden property performance patterns, and enhancing predictability.
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Reports on the topic "Machine learning not elsewhere classified"

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Olivier, Jason, and Sally Shoop. Imagery classification for autonomous ground vehicle mobility in cold weather environments. Engineer Research and Development Center (U.S.), November 2021. http://dx.doi.org/10.21079/11681/42425.

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Autonomous ground vehicle (AGV) research for military applications is important for developing ways to remove soldiers from harm’s way. Current AGV research tends toward operations in warm climates and this leaves the vehicle at risk of failing in cold climates. To ensure AGVs can fulfill a military vehicle’s role of being able to operate on- or off-road in all conditions, consideration needs to be given to terrain of all types to inform the on-board machine learning algorithms. This research aims to correlate real-time vehicle performance data with snow and ice surfaces derived from multispectral imagery with the goal of aiding in the development of a truly all-terrain AGV. Using the image data that correlated most closely to vehicle performance the images were classified into terrain units of most interest to mobility. The best image classification results were obtained when using Short Wave InfraRed (SWIR) band values and a supervised classification scheme, resulting in over 95% accuracy.
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