Learning Solutions Research Articles

Few-shot satellite image classification for bringing deep learning on board OPS-SAT

Bringing artificial intelligence on board Earth observation satellites unlocks unprecedented possibilities to extract actionable items from various image modalities at the global scale in real time. This is of paramount importance nowadays, as downlinking large amounts of imagery is not only prohibitively expensive but also time-consuming. However, building deep learning solutions that could be deployed on board an edge device is challenging due to the limited manually-annotated satellite datasets and hardware constraints of an edge device. This paper addresses these challenges through harnessing a blend of data-centric and model-centric approaches to build a well-generalizing yet efficient and resource-frugal deep learning model for multi-class satellite image classification in the few-shot learning settings. This integrated strategy is formulated to enhance classification performance while accommodating the unique demands of an image analysis chain on board OPS-SAT, a nanosatellite operated by the European Space Agency. The experiments performed over a real-world dataset of OPS-SAT images delves into the interactions between data- and model-centric techniques, underscores the significance of synthesizing artificial training data and emphasizes the value of ensemble learning. However, they also caution against negative transfer in domain adaptation. This study sheds light on effective model training strategies and highlights the multifaceted challenges inherent in deep learning for practical Earth observation, contributing insights to the field of satellite image classification within the constraints of nanosatellite operations. To ensure reproducibility of our study, the implementation is available at https://github.com/ShendoxParadox/Few-shot-satellite-image-classification-OPS-SAT.

A Metadata-Enhanced Deep Learning Method for Sea Surface Height and Mesoscale Eddy Prediction

Predicting the mesoscale eddies in the ocean is crucial for advancing our understanding of the ocean and climate systems. Establishing spatio-temporal correlation among input data is a significant challenge in mesoscale eddy prediction tasks, especially for deep learning techniques. In this paper, we first present a deep learning solution based on a video prediction model to capture the spatio-temporal correlation and predict future sea surface height data accurately. To enhance the performance of the model, we introduced a novel metadata embedding module that utilizes neural networks to fuse remote sensing metadata with input data, resulting in increased accuracy. To the best of our knowledge, our model outperforms the state-of-the-art method for predicting sea level anomalies. Consequently, a mesoscale eddy detection algorithm will be applied to the predicted sea surface height data to generate mesoscale eddies in future. The proposed solution achieves competitive results, indicating that the prediction error for the eddy center position is 5.6 km for a 3-day prediction and 13.6 km for a 7-day prediction.

Differentiating stable and unstable protein using convolution neural network and molecular dynamics simulations

Protein stability is a critical aspect of molecular biology and biochemistry, hinges on an intricate balance of thermodynamic and structural factors. Determining protein stability is crucial for understanding and manipulating biological machineries, as it directly correlated with the protein function. Thus, this study delves into the intricacies of protein stability, highlighting its dependence on various factors, including thermodynamics, thermal conditions, and structural properties. Moreover, a notable focus is placed on the free energy change of unfolding (ΔGunfolding), change in heat capacity (ΔCp) with protein structural transition, melting temperature (Tm) and number of disulfide bonds, which are critical parameters in understanding protein stability. In this study, a machine learning (ML) predictive model was developed to estimate these four parameters using the primary sequence of the protein. The shortfall of available tools for protein stability prediction based on multiple parameters propelled the completion of this study. Convolutional Neural Network (CNN) with multiple layers was adopted to develop a more reliable ML model. Individual predictive models were prepared for each property, and all the prepared models showed results with high accuracy. The R2 (coefficient of determination) of these models were 0.79, 0.78, 0.92 and 0.92, respectively, for ΔG, ΔCp, Tm and disulfide bonds. A case study on stability analysis of two homologous proteins was presented to validate the results predicted through the developed model. The case study included in silico analysis of protein stability using molecular docking and molecular dynamic simulations. This validation study assured the accuracy of each model in predicting the stability associated properties. The alignment of physics-based principles with ML models has provided an opportunity to develop a fast machine learning solution to replace the computationally demanding physics-based calculations used to determine protein stability. Furthermore, this work provided valuable insights into the impact of mutation on protein stability, which has implications for the field of protein engineering. The source codes are available at https://github.com/Growdeatechnology.

Learning Management of Islamic Religious Education for Children with Intellectual Disabilities: Multicase Study

This research aims to examine the learning of Islamic Education for children with intellectual disabilities in Sekolah Luar Biasa (SLB) Negeri Binjai and Sekolah Luar Biasa Negeri 1 Binjai, including planning, implementation, learning evaluation, as well as barriers and solutions in learning. The research method uses a qualitative approach with a multi-site study to explore the learning process at SLB Negeri Binjai and SLB Negeri 1 Binjai The research findings indicate that Islamic Education learning in both SLBs includes the introduction of basic Islamic concepts, teaching methods tailored to the needs of students, and parental involvement in the learning process. However, there are still obstacles such as limited resources and a lack of training for Islamic Education teachers. In conclusion, this research provides a significant contribution to efforts to improve the quality of Islamic education for children with special needs SLB Negeri Binjai and SLB Negeri 1 Binjai.

Reliable arrival time picking of acoustic emission using ensemble machine learning models

This study presents an innovative method for accurately picking the first-wave arrival time in acoustic emission (AE) localization, particularly effective in environments with low or variable signal-to-noise ratios (SNR). Utilizing an ensemble learning model, it synergizes multiple automatic arrival time estimation algorithms to enhance both consistency and robustness. The model, rooted in decision tree methodologies, integrates a variety of techniques, including Akaike information criterion (AIC), improved AIC, Hinkley, energy ratios, and wavelet transformation-based binary map. Its efficacy is demonstrated through testing on 549 manually annotated AE datasets, where the model’s predictions were benchmarked against manual picks, showcasing superior accuracy and robustness in AE event source localization. Evaluations of the model’s performance across various ensemble machine learning models highlighted its ability to significantly diminish localization errors, achieving an average absolute error of less than 1.5 mm. The study also delved into the impact of base pickers and the size of the training dataset on the model’s predictions. Findings indicated consistent performance across different decision tree models, with the accuracy of base pickers and training set size playing a significant role in outcomes. This research culminates in a decision tree-based ensemble machine learning solution that effectively estimates AE first-wave arrival times with high accuracy and robustness, even amidst fluctuating SNR conditions. Its adaptability and interpretability greatly reduce source localization errors in practical AE monitoring, effectively overcoming challenges associated with imprecise arrival time picking.

Multi-view GCN for loan default risk prediction

As a significant application of machine learning in financial scenarios, loan default risk prediction aims to evaluate the client’s default probability. However, most existing deep learning solutions treat each application as an independent individual, neglecting the explicit connections among different application records. Besides, these attempts suffer from the problem of missing data and imbalanced distribution (i.e., the default records are small samples against all the applications). We believe similar records could provide some auxiliary signals, which are of critical importance to alleviate the data missing issue and facilitate data argumentation. To this end, we propose multi-view loan application graphs, dubbed MLAGs. By evaluating the similarity between the records, a loan application graph can be constructed. Furthermore, we arrange different similarity thresholds to organize various graph structures for multi-graph constructions; thus, a variety of representations can be generated via information propagation and aggregation for small sample argumentation. Consequently, the imbalanced data distribution and missing values issues can be alleviated effectively. We conduct experiments on three public datasets from real-world home credit and P2P lending platforms, which show that MGCN outperforms both conventional and deep learning models. Ablation studies also illustrated the validity of each module design.

Enhanced privacy-preserving distributed deep learning with application to fog-based IoT

Generally, privacy-preserving distributed deep learning (PPDDL) solutions provided so far present a trade-off between privacy and efficiency/effectiveness, especially, in terms of high communication and run-time costs (i.e. efficiency) and declined accuracy (i.e. effectiveness). Additionally, not much attention has been paid to proposing privacy-preserving solutions that are multi-key compliant, able to extend to new participants and well fit for an unbounded number of participants as well as being post-quantum robust. Also, application of DDL to fog-based IoT needs more attention. To this effect, we present enhanced PPDDL by proposing two solutions (basic and advanced). The basic combines LWE-based additive homomorphic encryption and partial sharing, making it communication cost friendly. The advanced, however, uses a lattice-based fully dynamic multi-key fully homomorphic encryption (FHE) scheme and partial sharing with the additional advantages of being multi-key compliant, able to extend to new participants and well fit for an unbounded number of participants. Furthermore, we put our work in the context of Fog-Based IoT. Extensive experimental evaluations for the basic solution which also best suits the Fog-Based IoT, show that the PPDDL accuracy is maintained, recording accuracy of about 97% for the MNIST dataset. For a much deeper architecture, accuracy could reach about 99%. To assess the generalizability of the accuracy trend observed in the PPDDL, the proposed solution was also evaluated using the CIFAR-10 dataset with interesting results recorded in this study. This paper is innovative as it combines partial sharing and homomorphic encryption in DDL . Solutions are post-quantum robust and communication cost efficient.

Investigating attention mechanisms for plant disease identification in challenging environments

There is an increasing demand for efficient and precise plant disease detection methods that can quickly identify disease outbreaks. For this, researchers have developed various machine learning and image processing techniques. However, real-field images present challenges due to complex backgrounds, similarities between different disease symptoms, and the need to detect multiple diseases simultaneously. These obstacles hinder the development of a reliable classification model. The attention mechanisms emerge as a critical factor in enhancing the robustness of classification models by selectively focusing on relevant regions or features within infected regions in an image. This paper provides details about various types of attention mechanisms and explores the utilization of these techniques for the machine learning solutions created by researchers for image segmentation, feature extraction, object detection, and classification for efficient plant disease identification. Experiments are conducted on three models: MobileNetV2, EfficientNetV2, and ShuffleNetV2, to assess the effectiveness of attention modules. For this, Squeeze and Excitation layers, the Convolutional Block Attention Module, and transformer modules have been integrated into these models, and their performance has been evaluated using different metrics. The outcomes show that adding attention modules enhances the original models' functionality.

Interpretable machine learning-based text classification method for construction quality defect reports

Efficient identification and remediation of construction defects are critical for ensuring the quality and success of engineering projects. However, the complexity of construction environments poses challenges to this endeavor. Current research predominantly relies on statistical and causal analyses of defect detection reports, yet these methods are time-consuming and error-prone due to the unstructured nature of such reports. To address this, machine learning techniques have been applied to classify defect texts rapidly and accurately. However, existing studies primarily focus on model performance enhancement, neglecting interpretability and the effect of imbalanced data. This study introduces RF-SMOTE, an oversampling technique based on Random Forest (RF), to address the limitations of traditional methods like SMOTE. Comparative analyses demonstrated the efficacy of RF-SMOTE in mitigating imbalanced data effects. Further, the application of SHAP-based interpretability methods in construction management decision-making was explored, filling gaps in existing research. Contributions include providing interpretable machine learning solutions, discussing the effect of imbalanced data, and proposing SHAP-based application scenarios.

Code-Free Machine Learning Solutions for Microscopy Image Processing: Deep Learning.

In recent years, there has been a significant expansion in the realm of processing microscopy images, thanks to the advent of machine learning techniques. These techniques offer diverse applications for image processing. Currently, numerous methods are used for processing microscopy images in the field of biology, ranging from conventional machine learning algorithms to sophisticated deep learning artificial neural networks with millions of parameters. However, a comprehensive grasp of the intricacies of these methods usually necessitates proficiency in programming and advanced mathematics. In our comprehensive review, we explore various widely used deep learning approaches tailored for the processing of microscopy images. Our emphasis is on algorithms that have gained popularity in the field of biology and have been adapted to cater to users lacking programming expertise. In essence, our target audience comprises biologists interested in exploring the potential of deep learning algorithms, even without programming skills. Throughout the review, we elucidate each algorithm's fundamental concepts and capabilities without delving into mathematical and programming complexities. Crucially, all the highlighted algorithms are accessible on open platforms without requiring code, and we provide detailed descriptions and links within our review. It's essential to recognize that addressing each specific problem demands an individualized approach. Consequently, our focus is not on comparing algorithms but on delineating the problems they are adept at solving. In practical scenarios, researchers typically select multiple algorithms suited to their tasks and experimentally determine the most effective one. It is worth noting that microscopy extends beyond the realm of biology; its applications span diverse fields such as geology and material science. Although our review predominantly centers on biomedical applications, the algorithms and principles outlined here are equally applicable to other scientific domains. Furthermore, a number of the proposed solutions can be modified for use in entirely distinct computer vision cases.

Increasing access to legal information with unsupervised solutions

AbstractAccess to justice is a significant area of legal research, especially for Socio-Legal studies. The main research topics of this area are economic or class differences, gender inequalities, or national and ethnic differences in access to justice. However, there is a less discussed aspect of access to justice: the differences between access to legal information and the differences in user groups in terms of comprehending and processing legal information. This is an important topic because there are significant differences among people's abilities to process and understand legal texts, depending on whether we are dealing with a lawyer who is an expert in the given field, a non-expert lawyer, or a citizen with a low or zero (legal) educational level. The paper argues that unsupervised machine learning solutions can help even out these differences. It presents different unsupervised solutions, mainly clustering and topic modelling, which can help to increase access to legal information. Then we present a case study in which we examine these unsupervised tools in the processing of resolutions of the Central Bank in Hungary and anonymized court decisions. The paper argues that these tools can reveal the hidden contextual regularities in unstructured legal texts, facilitating the search for legal texts even for non-legal-experts.

STUCK PIPE MITIGATION: FINDINGS, ANALYSIS, AND MACHINE LEARNING SOLUTIONS

The occurrence of stuck pipes during drilling presents significant challenges, leading to nonproductive time (NPT) and potential damage to equipment. This paper delves into the reasons behind, effects of, and methods for addressing incidents of stuck pipes in drilling actions. Extensive literature review and analysis reveal the prevalence of stuck pipe events and their diverse underlying causes, ranging from differential sticking to mechanical jamming. Additionally, the study identifies key drilling parameters and signals indicative of potential stuck pipe incidents, emphasizing the importance of early detection and intervention to prevent adverse outcomes. Proposing the utilization of machine learning algorithms and statistical models for predictive analytics as a proactive strategy to mitigate the risk of stuck pipe incidents. The paper discusses the limitations of traditional statistical models and highlights the efficacy of machine learning methods, such as artificial neural networks (ANNs) and support vector machines (SVMs), in accurately modeling drilling processes. Furthermore, practical machine learning models using real-time drilling data are presented as effective tools for early detection and timely intervention in stuck pipe incidents. The study also describes various software tools used to assess and mitigate risks in drilling operations. Overall, this paper presents prospects for improving operational efficiency and safety of drilling operations using advanced analytical methods and software solutions. Keywords: stuck pipe, type of stuck, drillstring immobilization, nonproductive time, machine learning in drilling, risk analysis software.

The Influence of Supervision by the Principal and Motivation on the Performance of Elementary School Teachers During Pandemic

Online learning is promoted as a learning solution in elementary schools during the Covid 19 pandemic. Teachers are required to be able to adapt and improve performance progressively for the optimization of learning with the support of supervision and work motivation. This study aims to analyze the effect of supervision by school principals and work motivation on the performance of elementary school teachers during the Covid 19 pandemic. This research is a quantitative research with a correlational survey involving 125 samples of elementary school teachers in the Cluster I Singawinata Teacher Working Group of Purwakarta district, West Java, Indonesia. The results showed that supervision by the principal (X1) had a significant effect on teacher performance (Y). Then work motivation (X2) has a significant effect on teacher performance (Y). And finally, supervision by the principal (X1) and work motivation (X2) have a significant effect on the performance of elementary school teachers (Y) by 46.5%. School principals need to increase supervision to teachers continuously and systematically so that teachers have the motivation to improve their competence so that they can show good performance in improving the quality of learning during the Covid 19 pandemic.

Smart infrastructure design: Machine learning solutions for securing modern cities

In the realm of securing smart cities against emerging cyber threats, this research encompasses three distinct yet interconnected initiatives. First, a pioneering data architecture design leveraging CycleGAN is proposed to counteract False Data Injection Attacks (FDIA). By cyclically transforming data distributions, CycleGAN ensures the integrity and reliability of smart city information, fortifying against malicious manipulations. Second, an innovative Internet of Things (IoT) concept is introduced, aiming to enhance real-time monitoring and context-aware threat detection within Intrusion Detection Systems (IDS). Harnessing the wealth of data generated by IoT devices, this concept provides a comprehensive understanding of network activities, fostering adaptive responses to emerging threats. Lastly, the research delves into the development of a novel IDS hyperparameter adjusting system. This system integrates the strengths of Biogeography-Based Optimization (BBO) and Whale Optimization Algorithm (WOA) to fine-tune IDS configuration parameters. Drawing inspiration from biogeographical principles and collaborative whale behavior, the hybrid optimization system balances exploration and exploitation, adapting the IDS to diverse network environments. Together, these initiatives represent a holistic approach to fortifying smart cities through cutting-edge data architecture, IoT-driven threat detection, and optimized IDS configurations, contributing to the resilience and cybersecurity of modern urban landscapes.

Enhancing federated learning robustness through randomization and mixture

Protecting data privacy is a significant challenge in machine learning (ML), and federated learning (FL) has emerged as a decentralized learning solution to address this issue. However, FL is vulnerable to poisoning attacks, which control and interrupt the learning process to substantially increase the error rate of the system. The aggregation algorithm’s robustness is crucial to prevent such attacks; however previous Byzantine-robust algorithms have exhibited vulnerability to maliciously crafted model updates. We propose three different aggregation functions namely: Switch, Layered-Switch and Weighted FedAvg. Our proposed methods involve switching between aggregation functions. We evaluate the effectiveness of these methods against a model poisoning attack to demonstrate their robustness. To observe the performance of our proposed aggregation functions, we utilized them within a federated learning framework to classify the CIFAR10, CIFAR10 and Fashion-MNIST datasets. The simulation results demonstrate that all of these methods exhibit greater robustness against the employed adversarial attack. Our approach achieves an enhancement in performance during a poisoning attack, surpassing previous Byzantine-robust algorithms by 5%, 15% and 25% on the MNIST, CIFAR10 and CIFAR100 datasets, respectively. Among the proposed aggregation functions, Weighted FedAvg has shown the highest success rate on the datasets utilized in this research.

Empowering precise advertising with Fed-GANCC: A novel federated learning approach leveraging Generative Adversarial Networks and group clustering.

In the realm of targeted advertising, the demand for precision is paramount, and the traditional centralized machine learning paradigm fails to address this necessity effectively. Two critical challenges persist in the current advertising ecosystem: the data privacy concerns leading to isolated data islands and the complexity in handling non-Independent and Identically Distributed (non-IID) data and concept drift due to the specificity and diversity in user behavior data. Current federated learning frameworks struggle to overcome these hurdles satisfactorily. This paper introduces Fed-GANCC, an innovative federated learning framework that synergizes Generative Adversarial Networks (GANs) and Group Clustering. The framework incorporates a user data augmentation algorithm predicated on adversarial generative networks to enrich user behavior data, curtail the impact of non-uniform data distribution, and enhance the applicability of the global machine learning model. Unlike traditional approaches, our framework offers user data augmentation algorithms based on adversarial generative networks, which not only enriches user behavior data but also reduces the challenges posed by non-uniform data distribution, thereby enhancing the applicability of the global machine learning (ML) model. The effectiveness of Fed-GANCC is distinctly showcased through experimental results, outperforming contemporary methods like FED-AVG and FED-SGD in terms of accuracy, loss value, and receiver operating characteristic (ROC) indicators within the same computing time. Experimental results vindicate the effectiveness of Fed-GANCC, revealing substantial enhancements in accuracy, loss value, and receiver operating characteristic (ROC) metrics compared to FED-AVG and FED-SGD given the same computational time. These outcomes underline Fed-GANCC's exceptional prowess in mitigating issues such as isolated data islands, non-IID data, and concept drift. With its novel approach to addressing the prevailing challenges in targeted advertising such as isolated data islands, non-IID data, and concept drift, the Fed-GANCC framework stands as a benchmark, paving the way for future advancements in federated learning solutions tailored for the advertising domain. The Fed-GANCC framework promises to offer pivotal insights for the future development of efficient and advanced federated learning solutions for targeted advertising.

Feature Engineering Versus Deep Learning for Scene Recognition: A Brief Survey

Scene recognition is an important computer vision task that has evolved from the study of the biological visual system. Its applications range from video surveillance, autopilot systems, to robotics. The early works were based on feature engineering that involved the computation and aggregation of global and local image descriptors. Several popular image features such as SIFT, SURF, HOG, ORB, LBP, KAZE, etc. have been proposed and applied to the task with successful results. Features can be either computed from the entire image on a global scale, or extracted from local sub-regions and aggregated across the image. Suitable classifier models are deployed that learn to classify these features. This review paper analyzes several of these handcrafted features that have been applied to the scene recognition task over the past decades, and tracks the transition from the traditional feature engineering to deep learning which forms the current state of the art in computer vision. Deep learning is now deemed to have overtaken feature engineering in several computer vision applications. Deep convolutional neural networks and vision transformers are the current state of the art for object recognition. However, scenes from urban landscapes are bound to contain similar objects posing a challenge to deep learning solutions for scene recognition. In our study, a critical analysis of feature engineering and deep learning methodologies for scene recognition is provided, and results on benchmark scene datasets are presented, concluding with a discussion on challenges and possible solutions that may facilitate more accurate scene recognition.

Trusting My Predictions: On the Value of Instance-Level Analysis

Machine Learning solutions have spread along many domains, including critical applications. The development of such models usually relies on a dataset containing labeled data. This dataset is then split into training and test sets and the accuracy of the models in replicating the test labels is assessed. This process is often iterated in a cross-validation procedure for obtaining average performance estimates. But is the average of the predictive performance on test sets enough for assessing the trustfulness of a Machine Learning model? This paper discusses the importance of knowing which individual observations of a dataset are more challenging than others and how this characteristic can be measured and used in order to improve classification performance and trustfulness. A set of strategies for measuring the hardness level of the instances of a dataset is surveyed and a Python package containing their implementation is provided.

Automated real-time prediction of geological formation tops during drilling operations: an applied machine learning solution for the Norwegian Continental Shelf

Accurate prediction of geological formation tops is a crucial task for optimizing hydrocarbon exploration and production activities. This research investigates and conducts a comprehensive comparative analysis of several advanced machine learning approaches tailored for the critical application of geological formation top prediction within the complex Norwegian Continental Shelf (NCS) region. The study evaluates and benchmarks the performance of four prominent machine learning models: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest ensemble method, and Multi-Layer Perceptron (MLP) neural network. To facilitate a rigorous assessment, the models are extensively evaluated across two distinct datasets - a dedicated test dataset and a blind dataset independent for validation. The evaluation criteria revolve around quantifying the models' predictive accuracy in successfully classifying multiple geological formation top types. Additionally, the study employs the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm as a baseline benchmarking technique to contextualize the relative performance of the machine learning models against a conventional clustering approach. Leveraging two model-agnostic feature importance analysis techniques - Permutation Feature Importance (PFI) and Shapley Additive exPlanations (SHAP), the investigation identifies and ranks the most influential input variables driving the predictive capabilities of the models. The comprehensive analysis unveils the MLP neural network model as the top-performing approach, achieving remarkable predictive accuracy with a perfect score of 0.99 on the blind validation dataset, surpassing the other machine learning techniques as well as the DBSCAN benchmark. However, the SVM model attains superior performance on the initial test dataset, with an accuracy of 0.99. Intriguingly, the PFI and SHAP analyses converge in consistently pinpointing depth (DEPT), revolution per minute (RPM), and Hook-load (HKLD) as the three most impactful parameters influencing model predictions across the different algorithms. These findings underscore the potential of sophisticated machine learning methodologies, particularly neural network-based models, to significantly enhance the accuracy of geological formation top prediction within the geologically complex NCS region. However, the study emphasizes the necessity for further extensive testing on larger datasets to validate the generalizability of the high performance observed. Overall, this research delivers an exhaustive comparative evaluation of state-of-the-art machine learning techniques, offering critical insights to guide the optimal selection, development, and real-world deployment of accurate and reliable predictive modeling strategies tailored for hydrocarbon exploration and reservoir characterization endeavors in the NCS.Graphical abstract

Deep Generative Models for Data Synthesis and Augmentation in Machine Learning

The increasing trend in deep generative modelling, which offers data scarcity and diversity solutions in machine learning, is one of the most recent developments in the field. The data-oriented approaches drive the need for the quality and variety of data sets that could be prevented due to privacy issues and limited resources. Generative deep models, basically GANs (Generative Adversarial Networks) and VAEs (Variational Automatic Encoders), appear to be the most reliable approach to the synthesis and augmentation of the data. These models employ deep learning to basically learn all by itself from raw data without anyone teaching it, which is the basis of modern artificial intelligence. Accuracy issues between overfitting and poor generalization emphasize the need for smart solutions to the problem of data shortness. Deep generative modelling works based on the data distribution, which the model learns by itself and enables a realistic sample generator. The study reviews the proficiency and complexity of GANs, VAEs, and WGANs, comparing the WGANs' capabilities with the former two. Techniques of data augmentation, e.g., repositioning, rotation, and adding Gaussian noise to the dataset, will greatly increase the diversity of the data. Regardless of the training time, all models showcase competitive inference performance and, as a result, may be satisfactorily used in real-time operations. The insights obtained shed light on ways to improve machine learning and artificial intelligence through brain data synthesis, model training, and computational efficiency.