Simple Classifier Research Articles

Reliability Analysis of Complex Structures Under Multi-Failure Mode Utilizing an Adaptive AdaBoost Algorithm

A reliability analysis can become intricate when addressing issues related to nonlinear implicit models of complex structures. To improve the accuracy and efficiency of such reliability analyses, this paper presents a surrogate model based on an adaptive AdaBoost algorithm. This model employs an adaptive method to determine the optimal training sample set, ensuring it is as evenly distributed as possible on both sides of the failure curve and fully contains the information it represents. Subsequently, with the integration and iterative characteristics of the AdaBoost algorithm, a simple binary classifier is iteratively applied to build a high-precision alternative model for complex structural fault diagnosis to cope with multiple failure modes. Then, the Monte Carlo simulation technique is employed to meticulously assess the failure probability. The accuracy and stability of the proposed method’s iterative convergence process are validated through three numerical examples. The findings of the study illuminate that the proposed method is not only remarkably precise but also exceptionally efficient, capable of addressing the challenges related to the reliability evaluation of complex structures under multi-failure mode. The method proposed in this paper enhances the application of mechanical structures and facilitates the utilization of complex mechanical designs.

Modelling behavior of Crested gecko (Correlophus ciliatus) using classification algorithms

Animal behavior plays a crucial role in evolution of many species. Many studies focused on animal behavior enhance the ability to collect large and detailed data. However, this kind of data is surpassing the capability of traditional statistical methods for analysis. In this study we propose to use artificial intelligence (AI) with machine learning models (ML) as tools to study animal behavior and potentially assumed evolution patterns in their behavior. For the Crested gecko (Correlophus ciliatus), some guidelines have been published regarding the breeding of these reptiles, focusing on their behavior. However, little is known about moderating their behavior using AI and advanced ML algorithms. In this study, based on information collected from twenty individuals, we proposed building a supervised classifier model using simple Decision Tree classifier (DT), Gradient Boosting classifier (GB) and Extreme Gradient Boosting classifier (XGBoost). Our results show that the highest accuracy (above 60 %) was achieved for variables which were not complex in terms of animal behavior. The analysis presented in this study, demonstrates that it is possible to model Crested Gecko behavior using ML models.

Automated sample annotation for diabetes mellitus in healthcare integrated biobanking

Healthcare integrated biobanking describes the annotation and collection of residual samples from hospitalized patients for research purposes. The central idea of the current work is to establish an automated workflow for sample annotation, selection and storage for diabetes mellitus. This is challenging due to incomplete data at the time of sample selection. The study evaluates a machine learning (ML) and natural language processing (NLP) based two-step procedure for timely and precise sample annotation for diabetes mellitus. Electronic health record data of 785 persons were extracted from the hospital information system. In the first step, a conditional inference forest (CIF) model was trained and tested based on laboratory values from the first 72 h of the hospital stay using test- (n = 550) and training data sets (n = 235). Performance was compared with a simple laboratory cut-off classifier (LCC) and a logistic regression (LR) model. Algorithms based on laboratory values, ICD-10 codes or information from discharge summaries extracted by a natural language processing software (NLP-DS) were evaluated as a second (review) step designed to increase the precision of annotations. For the first step, recall/precision/F1-score/accuracy were 71 %/86 %/0.78/0.82 for CIF and 77 %/70 %/0.74/0.75 for LR compared to 73 %/68 %/0.70/0.72 for LCC. NLP-DS was the best-performing second (review) step (93 %/100 %/0.97/0.97). Combining first-step models with NLP-DS increased precision to 100 % for all procedures (66 %/100 %/0.80/0.85 for CIF&NLP-DS, 72 %/100 %/0.84/87.2 for LR&NLP-DS and 66 %/100 %/0.80/0.85 for LCC&NLP-DS). The number of samples removed by NLP-DS was higher for LR&NLP-DS and LCC&NLP-DS (removal rate 35 % and 38 % of initially selected samples) compared to CIF&NLP-DS (removal rate of 20 %). The developed two-step procedure is an efficient implementable method for timely and precise annotation of samples from diabetic hospitalized patients.

Encoding human activities using multimodal wearable sensory data

Human Activity Recognition (HAR) is the task to automatically analyze and recognize human body gestures or actions. HAR using time-series multi-modal sensory data is a challenging and important task in the field of machine learning and feature engineering due to its increasing demands in numerous real-world applications such as healthcare, sports and surveillance. Numerous daily wearable devices e.g., smartphones, smartwatches, and smart glasses can be used to collect and analyze the human activities on an unprecedented scale. This paper presents a generic framework to recognize the different human activities using continuous time-series multimodal sensory data of these smart gadgets. The proposed framework follows the channel of Bag-of-Features which consists of four steps: (i) Data acquisition and pre-processing, (ii) codebook computation, (iii) feature encoding, and (iv) classification. Each step in the framework plays a significant role to generate an appropriate feature representation of raw sensory data for efficient activity recognition. In the first step, we employed a simple overlapped-window sampling approach to segment the continuous time-series sensory data to make it suitable for activity recognition. Secondly, we build a codebook using k-means clustering algorithm to group the similar sub-sequences. The center of each group is known as codeword and we assume that it represents a specific movement in the activity sequence. The third step consists of feature encoding which transform the raw sensory data of activity sequence into its respective high-level representation for the classification. Specifically, we presented three reconstruction-based encoding techniques to encode sensory data, namely: Sparse Coding, Local Coordinate Coding, and Locality-constrained Linear Coding. The segmented activity sub-sequences are transformed to high-level representation using these techniques and earlier computed codebook. Finally, the encoded features are classified using a simple Random Forest classifier. The proposed HAR framework using three different encoding techniques is evaluated on three large benchmark datasets: UniMiB-SHAR dataset, MHEALTH dataset and WISDM dataset and their results are compared with most recent state-of-the-art techniques. It outperforms the existing techniques and achieves 98.39%, 99.5%, and 99.4% recognition scores on UniMiB-SHAR dataset, MHEALTH dataset, and WISDM dataset respectively. The excellent recognition results and computational analysis confirms the effectiveness and efficiency of the proposed framework.

Classification of Breathing Phase and Path with In-Ear Microphones.

In recent years, the use of smart in-ear devices (hearables) for health monitoring has gained popularity. Previous research on in-ear breath monitoring with hearables uses signal processing techniques based on peak detection. Such techniques are greatly affected by movement artifacts and other challenging real-world conditions. In this study, we use an existing database of various breathing types captured using an in-ear microphone to classify breathing path and phase. Having a small dataset, we use XGBoost, a simple and fast classifier, to address three different classification challenges. We achieve an accuracy of 86.8% for a binary path classifier, 74.1% for a binary phase classifier, and 67.2% for a four-class path and phase classifier. Our path classifier outperforms existing algorithms in recall and F1, highlighting the reliability of our approach. This work demonstrates the feasibility of the use of hearables in continuous breath monitoring tasks with machine learning.

CONTINUOUS SCORING OF AUTISM SPECTRUM DISORDER PATIENTS BY ANALYZING THEIR EEG SIGNALS

Clinical manifestations and standard psychological tests have been widely used to diagnose autism spectrum disorder (ASD) patients and evaluate their severity level. The gold-standard criterion to diagnose ASD patients is the childhood autism rating scale (CARS), which is a qualitative questionnaire that is filled out through a systematic interview while no physiological test/record is performed to determine this score. To make the diagnosis process quantitative, electroencephalography (EEG) signals have been repeatedly analyzed to differentiate healthy subjects from ASD patients. However, the precise relationship between the abnormal behavior of EEG signals and different ASD severity levels is not well investigated. Here, we use CARS to qualitatively determine the severity level of 14 autistic children, who voluntarily enrolled in our study. We recorded their EEG signals from 19 scalp channels when they were awake in the idle state and elicited three informative features including approximation entropy, multiscale entropy and sample entropy in successive time frames. Among the three measures of entropy, the last one exhibits the highest sensitivity, where its correlation coefficient (CC) exceeds 0.7, on the electrode positions T6 (CC [Formula: see text] 0.74), P4 (CC [Formula: see text] 0.76) and Cz (CC [Formula: see text] 0.75). Results of sample entropy in channels Cz-versus-Pz and P4-versus-FP2 show that a simple K-nearest neighbor classifier can provide 93% classification accuracy among patients with mild, moderate and severe ASD levels. Comparing the proposed method to the conventional ones, we also extracted power spectral density features from the channels but they failed to identify the ASD severity level with an acceptable accuracy.

Boosting Learning Algorithms for Chronic Diseases Prediction: A Review

Boosting algorithms are a set of machine learning techniques that are predicated on the notion that a weak learner's acquisition of multiple basic classifiers might yield results that are superior to those of any one simple classifier used alone. A comprehensive evaluation of regularly used boosting techniques against highly investigated diseases is lacking, despite the fact that boosting approaches have been used for disease prediction in many studies. Thus, the purpose of this work is to highlight the main algorithms and strategies in the boosting learning. The results of this work will help academics identify a more appropriate boosting approach to predict disease, as well as better understand current patterns and hotspots in diseases prediction models that use boosting learning. The results showed that adaboost algorithm outperformed other algorithms in terms of accuracy, achieving above 90%. This review also demonstrates how combining two boosting methods can increase the basic classifier's accuracy. By using AdaBoost and LightGBM, the accuracy reached 99.75%. XGBoost and Gradient Boosting techniques were employed more frequently in researches than other boosting algorithms.

Predictability of antigen binding based on short motifs in the antibody CDRH3.

Adaptive immune receptors, such as antibodies and T-cell receptors, recognize foreign threats with exquisite specificity. A major challenge in adaptive immunology is discovering the rules governing immune receptor-antigen binding in order to predict the antigen binding status of previously unseen immune receptors. Many studies assume that the antigen binding status of an immune receptor may be determined by the presence of a short motif in the complementarity determining region 3 (CDR3), disregarding other amino acids. To test this assumption, we present a method to discover short motifs which show high precision in predicting antigen binding and generalize well to unseen simulated and experimental data. Our analysis of a mutagenesis-based antibody dataset reveals 11 336 position-specific, mostly gapped motifs of 3-5 amino acids that retain high precision on independently generated experimental data. Using a subset of only 178 motifs, a simple classifier was made that on the independently generated dataset outperformed a deep learning model proposed specifically for such datasets. In conclusion, our findings support the notion that for some antibodies, antigen binding may be largely determined by a short CDR3 motif. As more experimental data emerge, our methodology could serve as a foundation for in-depth investigations into antigen binding signals.

The dutch draw: constructing a universal baseline for binary classification problems

Abstract Novel prediction methods should always be compared to a baseline to determine their performance. Without this frame of reference, the performance score of a model is basically meaningless. What does it mean when a model achieves an $F_1$ of 0.8 on a test set? A proper baseline is, therefore, required to evaluate the ‘goodness’ of a performance score. Comparing results with the latest state-of-the-art model is usually insightful. However, being state-of-the-art is dynamic, as newer models are continuously developed. Contrary to an advanced model, it is also possible to use a simple dummy classifier. However, the latter model could be beaten too easily, making the comparison less valuable. Furthermore, most existing baselines are stochastic and need to be computed repeatedly to get a reliable expected performance, which could be computationally expensive. We present a universal baseline method for all binary classification models, named the Dutch Draw (DD). This approach weighs simple classifiers and determines the best classifier to use as a baseline. Theoretically, we derive the DD baseline for many commonly used evaluation measures and show that in most situations it reduces to (almost) always predicting either zero or one. Summarizing, the DD baseline is general, as it is applicable to any binary classification problem; simple, as it can be quickly determined without training or parameter tuning; and informative, as insightful conclusions can be drawn from the results. The DD baseline serves two purposes. First, it is a robust and universal baseline that enables comparisons across research papers. Second, it provides a sanity check during the prediction model’s development process. When a model does not outperform the DD baseline, it is a major warning sign.

Diagnosa Penyakit Paru-Paru dengan Metode Naive Bayes

The lungs as the only pump for the respiratory system are very important organs for the continuation of life. Diagnosing or checking lung symptoms early can help people recognize the possibility that they are suffering from lung disease, so that treatment or care can be done earlier to prevent the severity of the disease. The method used in this study is the Naïve Bayes method. Naive Bayes is a simple probabilistic classifier that calculates a set of probabilities by adding up the frequencies and combinations of values ​​from the given dataset. An expert system is a computer application that can help decision making in more specific fields with methods that have been analyzed in advance by experts or specialists. This study used variables, namely types of lung disease including Pulmonary Tuberculosis (TB), Chronic Obstructive Pulmonary Disease (COPD), Bronchial Asthma and Lung Cancer. The results of this study are that lung disease or types of lungs can be diagnosed using the web-based Naïve Bayes method, and make it easier for sufferers to consult without seeing a doctor by selecting symptoms of lung disease.

Detection and mitigation of DDoS attacks based on multi-dimensional characteristics in SDN

Due to the large computational overhead, underutilization of features, and high bandwidth consumption in traditional SDN environments for DDoS attack detection and mitigation methods, this paper proposes a two-stage detection and mitigation method for DDoS attacks in SDN based on multi-dimensional characteristics. Firstly, an analysis of the traffic statistics from the SDN switch ports is performed, which aids in conducting a coarse-grained detection of DDoS attacks within the network. Subsequently, a Multi-Dimensional Deep Convolutional Classifier (MDDCC) is constructed using wavelet decomposition and convolutional neural networks to extract multi-dimensional characteristics from the traffic data passing through suspicious switches. Based on these extracted multi-dimensional characteristics, a simple classifier can be employed to accurately detect attack samples. Finally, by integrating graph theory with restrictive strategies, the source of attacks in SDN networks can be effectively traced and isolated. The experimental results indicate that the proposed method, which utilizes a minimal amount of statistical information, can quickly and accurately detect attacks within the SDN network. It demonstrates superior accuracy and generalization capabilities compared to traditional detection methods, especially when tested on both simulated and public datasets. Furthermore, by isolating the affected nodes, the method effectively mitigates the impact of the attacks, ensuring the normal transmission of legitimate traffic during network attacks. This approach not only enhances the detection capabilities but also provides a robust mechanism for containing the spread of cyber threats, thereby safeguarding the integrity and performance of the network.

Frequency-focused sound data generator for fault diagnosis in industrial robots

Abstract A frequency-focused sound data generator was developed for the in situ fault sound diagnosis of industrial robot reducers. The sound data generator, based on a conditional generative adversarial network, selects a target frequency range without relying on domain knowledge. A sound dataset of normal and faulty harmonic drive rotations of in situ industrial robots was collected using an attachable wireless sound sensor. The generated sound data were evaluated based on the fault diagnosis accuracy of a simple classifier trained using the generated data and tested using real data. The proposed method well-defined the frequency feature clusters and produced high-quality data, exhibiting up to 16.0% higher precision score on normal and 13.0% higher accuracy on weak-fault harmonic drive compared with the conventional methods, achieving fault diagnosis accuracy of 95.6% even in situations of fault data comprising only 5% of the normal data.

Natural Language Processing (NLP) Accurately Identifies LTCF Exposure from Clinical Notes: A Proof-of-Principle Study

Background: Residence or recent stay in a long-term care facility (LTCF) is one of the most important risk factors for multidrug-resistant organism (MDRO) carriage and infection, making reliable identification of LTCF-exposed inpatients a critical priority for infection control day-to-day practice and research. However, because most hospital electronic health records (EHRs) do not include a dedicated field for documenting LTCF exposure, absent manual review of patient charts, identifying LTCF-exposed inpatients is challenging. We aimed to develop an automated, natural language processing (NLP)-based classifier for identifying LTCF exposure from clinical notes. Methods: We randomly sampled 1020 adult admissions from 2016-2021 across the 12-hospital University of Maryland Medical System and manually reviewed each admission’s history & physical (H&P) note for mention of LTCF exposure (Figure 1). After H&P pre-processing, we calculated feature representations for documents based on term frequencies and visually explored between-group (LTCF-exposed vs. LTCF-unexposed) feature differences. To predict LTCF status from the H&P notes, we trained and tuned a LASSO regression-based classifier on 70% of the data with 3-fold cross-validation and 1:1 up-sampling to address class imbalance. The final classifier was evaluated on the 30% held-out sample (not up-sampled), with calculation of the C-statistic (area-under-the-curve, AUC) with bootstrapped 95% confidence intervals, and construction of receiver-operating-characteristic and variable importance plots (R Version 4.3.2). Results: 7% (n=76 cases) of H&P notes documented LTCF exposure. In our visual analysis, the H&P words and phrases that were over-represented among LTCF patients had high face validity (Figure 2). The final LASSO-regression-based classifier achieved a C-statistic of 0.89 (95% CI: 0.80–0.98) on the held-out data for identifying LTCF exposure from the H&P notes (Figure 3). The most important model predictors (i.e., words) for distinguishing LTCF-exposed from LTCF-unexposed patients are reflected in Figure 4. The most important predictor-words of LTCF-exposure were “rehab,” “place,” “status,” “egd,” and “dementia.” Conclusion: In this multi-center study, even a simple NLP classifier demonstrated very strong discrimination for identifying LTCF exposure status from H&P notes, which could substantially reduce the manual review time required to identify LTCF-exposed inpatients. If automated in the electronic health record, it could also inform real-time MDRO screening decisions. Future research is planned to build more sophisticated classifiers using machine learning best practices, to build classifiers for additional MDRO risk factors, and to externally validate NLP classifiers on notes from an external healthcare system.

The Deep-Match Framework: R-Peak Detection in Ear-ECG.

The Ear-ECG provides a continuous Lead I like electrocardiogram (ECG) by measuring the potential difference related to heart activity by electrodes which are embedded within earphones. However, the significant increase in wearability and comfort enabled by Ear-ECG is often accompanied by a degradation in signal quality - an obstacle that is shared by the majority of wearable technologies. We aim to resolve this issue by introducing a Deep Matched Filter (Deep-MF) for the highly accurate detection of R-peaks in wearable ECG, thus enhancing the utility of Ear-ECG in real-world scenarios. The Deep-MF consists of an encoder stage, partially initialised with an ECG template, and an R-peak classifier stage. Through its operation as a Matched Filter, the encoder searches for matches with an ECG template in the input signal, prior to filtering these matches with the subsequent convolutional layers and selecting peaks corresponding to the ground-truth ECG. The latent representation of R-peak information is then fed into a R-peak classifier, of which the output provides precise R-peak locations. The proposed Deep Matched Filter is evaluated using leave-one-subject-out cross-validation over 36 subjects with an age range of 18-75, with the Deep-MF outperforming existing algorithms for R-peak detection in noisy ECG. The Deep-MF achieves a median R-peak recall of 94.9% and a median precision of 91.2% across subjects when evaluated with leave-one-subject-out cross validation. Overall, this Deep-Match framework serves as a valuable step forward for the real-world functionality of Ear-ECG and, through its interpretable operation, the acceptance of deep learning models in e-Health.

Mixed Representations of Sound and Action in the Auditory Midbrain.

Linking sensory input and its consequences is a fundamental brain operation. During behavior, the neural activity of neocortical and limbic systems often reflects dynamic combinations of sensory and task-dependent variables, and these "mixed representations" are suggested to be important for perception, learning, and plasticity. However, the extent to which such integrative computations might occur outside of the forebrain is less clear. Here, we conduct cellular-resolution two-photon Ca2+ imaging in the superficial "shell" layers of the inferior colliculus (IC), as head-fixed mice of either sex perform a reward-based psychometric auditory task. We find that the activity of individual shell IC neurons jointly reflects auditory cues, mice's actions, and behavioral trial outcomes, such that trajectories of neural population activity diverge depending on mice's behavioral choice. Consequently, simple classifier models trained on shell IC neuron activity can predict trial-by-trial outcomes, even when training data are restricted to neural activity occurring prior to mice's instrumental actions. Thus, in behaving mice, auditory midbrain neurons transmit a population code that reflects a joint representation of sound, actions, and task-dependent variables.

Efficient human activity recognition: A deep convolutional transformer-based contrastive self-supervised approach using wearable sensors

Artificial intelligence has advanced the applications of sensor-based human motion capture and recognition technology in various engineering fields, such as human–robot collaboration and health monitoring. Deep learning methods can achieve satisfactory recognition results when provided with sufficient labeled data. However, labeling a large dataset is expensive and time-consuming in practical applications. To address this issue, this paper proposes a deep convolutional transformer-based contrastive self-supervised (DCTCSS) model under the bootstrap your own latent (BYOL) framework. The DCTCSS model aims to achieve reliable activity recognition using only a small amount of labeled data. Firstly, a deep convolutional transformer (DCT) model is proposed as the backbone of DCTCSS model, to learn high-level feature representations from unlabeled data in pre-training period. Subsequently, a simple linear classifier is trained with supervised fine-tuning using a limited amount of labeled data to recognize activities. In addition, this paper experimentally formulates a random data augmentation strategy to increase the diversity of input data. The performance of the DCTCSS model is evaluated and compared with several state-of-the-art algorithms on three datasets widely used in daily life, medical monitoring, and intelligent manufacturing. Experimental results show that the DCTCSS model achieves mean F1 scores of 95.64%, 88.39%, and 98.40% on the UCI-HAR, Skoda, and Mhealth datasets, respectively, using only 10% of the labeled data. These results outperform both supervised and unsupervised baseline models. Consequently, the DCTCSS model demonstrates its effectiveness in reducing the dependence on large amounts of labeled data while still achieving competitive activity recognition performance.

Implementasi Algoritma Naive Bayes Dalam Penentuan Bonus Karyawan

. CV. Aneka Kaca is a company engaged in the supply of the best glass materials, because CV. Aneka Kaca is a direct distributor from Japan. In a company like CV. Aneka Kaca, employees are one of the important assets in helping to improve and stabilize the company. Employees are people who work in a company or agency to carry out an operational task and expect remuneration in the form of a commission or salary. Usually employees will also get bonuses from the company. Bonuses that will be received by employees are one of the ways companies use to increase the motivation of their employees' performance. But giving bonuses to CV. Aneka Kaca still uses the conventional method, so it can take a long time if only managers do the math. The determination of bonuses can sometimes be missed due to one or two problems that can have an impact on awarding bonuses. The Naive Bayes algorithm is a simple probabilistic classifier that computes a probabilistic set by summing the frequencies and value combinations from a given dataset.

The Application of Machine Learning in Diagnosing the Financial Health and Performance of Companies in the Construction Industry

Diagnosing the financial health of companies and their performance is currently one of the basic questions that attracts the attention of researchers and experts in the field of finance and management. In this study, we focused on the proposal of models for measuring the financial health and performance of businesses. These models were built for companies doing business within the Slovak construction industry. Construction companies are identified by their higher liquidity and different capital structure compared to other industries. Therefore, simple classifiers are not able to effectively predict their financial health. In this paper, we investigated whether boosting ensembles are a suitable alternative for performance analysis. The result of the research is the finding that deep learning is a suitable approach aimed at measuring the financial health and performance of the analyzed sample of companies. The developed models achieved perfect classification accuracy when using the AdaBoost and Gradient-boosting algorithms. The application of a decision tree as a base learner also proved to be very appropriate. The result is a decision tree with adequate depth and very good interpretability.

A Novel Method to Train Classification Models for Structure Detection in In Situ Spacecraft Data

AbstractWe present a method for creating spacecraft‐like data which can be used to train Machine Learning (ML) models to detect and classify structures in in situ spacecraft data. First, we use the Grad‐Shafranov equation to numerically solve for several magnetohydrostatic equilibria which are variations on a known analytic equilibrium. These equilibria are then used as the initial conditions for Particle‐In‐Cell simulations in which the structures of interest are observed and labeled. We then take one‐dimensional slices through the simulations to replicate what a spacecraft collecting data from the simulation would observe. This sliced data then can be used as training data for the initial training of ML models intended for use on spacecraft data. We demonstrate the method applied to the problem of detecting small‐scale plasmoids in the magnetotail, which is important for understanding complex magnetotail reconnection dynamics. The simple 1D classifier we train is able to detect more than 70% of the plasmoid points in the data set but also produces a large number of false positives. Our further work on this example problem is detailed, and further potential uses of the method are discussed.

Radio Galaxy Zoo: Leveraging latent space representations from variational autoencoder

We propose to learn latent space representations of radio galaxies, and train a very deep variational autoencoder (VDVAE) on RGZ DR1, an unlabeled dataset, to this end. We show that the encoded features can be leveraged for downstream tasks such as classifying galaxies in labeled datasets, and similarity search. Results show that the model is able to reconstruct its given inputs, capturing the salient features of the latter. We use the latent codes of galaxy images, from MiraBest Confident and FR-DEEP NVSS datasets, to train various non-neural network classifiers. It is found that the latter can differentiate FRI from FRII galaxies achieving accuracy ≥ 76%, roc-auc ≥ 0.86, specificity ≥ 0.73 and recall ≥ 0.78 on MiraBest Confident dataset, comparable to results obtained in previous studies. The performance of simple classifiers trained on FR-DEEP NVSS data representations is on par with that of a deep learning classifier (CNN based) trained on images in previous work, highlighting how powerful the compressed information is. We successfully exploit the learned representations to search for galaxies in a dataset that are semantically similar to a query image belonging to a different dataset. Although generating new galaxy images (e.g. for data augmentation) is not our primary objective, we find that the VDVAE model is a relatively good emulator. Finally, as a step toward detecting anomaly/novelty, a density estimator — Masked Autoregressive Flow (MAF) — is trained on the latent codes, such that the log-likelihood of data can be estimated. The downstream tasks conducted in this work demonstrate the meaningfulness of the latent codes.