Background: Electroencephalography (EEG) signal analysis is a rapid, low-cost, and practical method for diagnosing the early stages of dementia, including mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The extraction of appropriate biomarkers to assess a subject’s cognitive impairment has attracted a lot of attention in recent years. The aberrant progression of AD leads to cortical detachment. Due to the interaction of several brain areas, these disconnections may show up as abnormalities in functional connectivity and complicated behaviors. Methods: This work suggests a novel method for differentiating between AD, MCI, and HC in two-class and three-class classifications based on EEG signals. To solve the class imbalance, we employ EEG data augmentation techniques, such as repeating minority classes using variational autoencoders (VAEs), as well as traditional noise-addition methods and hybrid approaches. The power spectrum density (PSD) and temporal data employed in this study’s feature extraction from EEG signals were combined, and a support vector machine (SVM) classifier was used to distinguish between three categories of problems. Results: Insufficient data and unbalanced datasets are two common problems in AD datasets. This study has shown that it is possible to generate comparable data using noise addition and VAE, train the model using these data, and, to some extent, overcome the aforementioned issues with an increase in classification accuracy of 2 to 7%. Conclusion: In this work, using EEG data, we were able to successfully detect three classes: AD, MCI, and HC. In comparison to the pre-augmentation stage, the accuracy gained in the classification of the three classes increased by 3% when the VAE model added additional data. As a result, it is clear how useful EEG data augmentation methods are for classes with smaller sample numbers.

This study addresses the research problem of enhancing In-Vitro Fertilization (IVF) success rate prediction by integrating advanced machine learning paradigms with gynecological expertise. The methodology involves the analysis of comprehensive datasets from 2017 to 2018 and 2010–2016. Machine learning models, including Logistic Regression, Gaussian NB, SVM, MLP, KNN, and ensemble models like Random Forest, AdaBoost, Logit Boost, RUS Boost, and RSM, were employed. Key findings reveal the significance of patient demographics, infertility factors, and treatment protocols in IVF success prediction. Notably, ensemble learning methods demonstrated high accuracy, with Logit Boost achieving an accuracy of 96.35%. The implications of this research span clinical decision support, patient counseling, and data preprocessing techniques, highlighting the potential for personalized IVF treatments and continuous monitoring. The study underscores the importance of collaboration between gynecologists and data scientists to optimize IVF outcomes. Prospective studies and external validation are suggested as future directions, promising to further revolutionize fertility treatments and offer hope to couples facing infertility challenges.

Rail surface defect inspection is of particular importance in modern railways. Accurate and efficient surface defect detection approaches support optimized maintenance. This enables safe operation of the railway network. However, the scale and harsh working environments of the railway still pose challenges to existing manual and vision-based inspection methods. Inspired by recent advances in laser measurement and deep learning in computer vision, this paper proposes a laser-based 3D pixel-level rail surface defect detection method that combines high-precision laser measurement data with the concept of deep semantic segmentation. In the proposed method, the rail surface is first measured in 3D using a low-cost 2D laser triangulation sensor. Then, a new deep semantic segmentation network is introduced. The network is composed of a fully convolutional segmentation module and two symmetric mapping modules, which can take 3D laser measurement data as input and output 3D pixel-level defect detection results in an end-to-end manner. The modular design of the network allows the use of various segmentation modules for different applications or scenarios. Experiments on a 3D rail dataset demonstrate the feasibility of the proposed method with a pixel-level detection accuracy measured by mean Intersection over Union (mIoU) of up to 87.9%. The 3D output provides not only location and boundary information but also the 3D characterization of defects, giving an essential reference for further defect management and repair tasks.

Oocyte deformation during injection is a major cause of potential cell damage which can lead to failure in the Intracytoplasmic Sperm Injection (ICSI) operation used as an infertility treatment. Injection speed plays an important role in the deformation creation. In this paper the effect of different speeds on deformation of zebrafish embryos is studied using a specially designed experimental set-up. An analytical model is developed in order to link injection force, deformation, and injection speed. A finite element (FE) model is also developed to analyse the effect of injection speed, allowing the production of additional information that is difficult to obtain experimentally, e.g., deformation and stress fields on the oocyte. The numerical model is validated against experimental results. Experimental results indicate that by increasing the injection speed, the deformation decreases. However, higher speeds cause higher levels of injection force and force fluctuation, leading to a higher vibration during injection. For this reason, an optimum injection speed range is determined. Finally, the FE model was validated against experimental results. The FE model is able to predict the force-deformation variation during injection for different speeds. This proves to be useful for future studies investigating different injection conditions.

Intracytoplasmic sperm injection (ICSI) is an assisted reproductive technology used in infertility treatment, where a single sperm is selected and immobilised using a glass injection pipette and is inserted directly into the cytoplasm of an oocyte under the microscope. Auto-alignment of the injection pipette is a prerequisite for any proposed automated oocyte injection procedure. In this paper, an auto-alignment procedure has been proposed. This technique requires the positioning of the injection and holding pipettes in the three orthogonal axes under microscopy which is complex. The existing proposed methods are system-specific and require appropriate algorithms. In this study, twelve commonly used focusing algorithms were evaluated to verify the optimal one for ICSI application. These algorithms were assessed by measuring focusing accuracy, range, number of false maxima and the width of the curve. The focus level for each pipette is calculated by the algorithm using focus measure functions (FMA). The Fibonacci search algorithm is employed for controlling the z-axis of the motorised stage to obtain the focal plane of the injection and holding pipette. Experimental results verified that the Brenner gradient has demonstrated the highest overall performance for injection pipette focalization, while Energy of gradient has presented a highest overall performance for holding pipette focalization.