Ensemble-based techniques are deployed to yield better performance than individual classifiers. Existing ensembling approaches fail to consider data complexity during their design. This work presents ...an ensemble-based approach for resolving complex patterns in real-world classification problems. A novel Minimum Spanning Tree (MST)-based approach is employed for decomposing the original problem into subproblems with reduced data complexity, and SVM is utilized for the development of component classifiers for these subproblems. A novel dynamic ensemble-based technique is utilized to generate the outcome. This work is evaluated by using 28 datasets retrieved from the KEEL dataset repository. Additionally, statistical tests are performed to illustrate a significant difference in the performance of the proposed model compared to the state-of-art classification models and two recently proposed dynamic ensembling approaches.
•Novel Ensemble-based technique.•Divides the original problem into subproblems of lower data complexity.•Utilizes Minimum Spanning Tree for creating subproblems.•Creates models for subproblems using SVM.•Novel approach for dynamic selection of the SVM model to yield the final outcome.
•Ovarian cancer is the third leading gynaecological malignancy that leads to death in females annually.•The FDA approved drug from both generic and pharmaceutical brands but these synthetic compounds ...have many adverse effects.•Our in silico analysis suggest that dihydroquercetin (DHQ) is an effective herbal compound that is druggable, and leadlike.•DHQ has good bioavailability, medicinal chemistry with a higher solubility with no adverse side effects, and toxicity.•Our in vitro study on SK-OV-3 cell lines showcased that DHQ caused apoptosis at 250 µm.
Ovarian cancer is the third leading gynaecological malignancy that leads to death in females annually. According to the GLOBOCAN 2020 incidence cases in Asia, there were a total of 100,854 ovarian cancer incidence cases. The Food and Drug Administration (FDA) has approved some drug compounds from both the generic and pharmaceutical brands for ovarian cancer, however, these synthetic compounds have many adverse effects such as – infections, fatigue, nerve problems, alopecia (hair loss), weight loss, lack of appetite, chemobrain (forgetfulness). By exploring both the in silico and in vitro aspects, we suggest that dihydroquercetin (DHQ) or simply taxifolin is an effective herbal compound that is druggable, leadlike, has good bioavailability, medicinal chemistry with a higher solubility with no adverse side effects, and toxicity. It also showcased a greater and more stable binding affinity and energy landscape with target POTEE with minimal energy frustrations and clashes. Our study also was able to predict a positive classification model of DHQ with a Tanimoto-distance threshold (d0 = 0.8) and smoothing factor (h = 0.9). We observed that the accuracy decreases to 90% from 100% at distance = 0.7, predicted number of compounds rose from 80 to 95%, which can be worth the loss of accuracy. Furthermore, our in vitro analysis on SK-OV-3 cell lines showcased that the anticancerous activity of DHQ caused significant cancer cell death and cell proliferation at 100 µm and 250 µm solutions. The DHQ drug dosages were further verified by Calcein-AM-PI live dead cell imaging on both concentrations. We observed that at 250 µm, DHQ caused apoptosis in SK-OV-3 cells. Hence, it is concluded that DHQ reduces ovarian cell proliferation and eventually cell death (apoptosis) in ovarian cancer.
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•Human activity recognition (HAR) by Kinect camera suffers from indirect field-of-view.•A novel data fusion framework is proposed for combining Kinect data with Shimmer.•Fusing data from the two ...sensor types is for achieving fast and accurate HAR.•Incremental decision table with swarm-based feature selection is proposed as solution.•Our experiment results show HAR accuracy is improved from 23.51% to 68.35%.
Human activity recognition (HAR) by machine learning finds wide applications ranging from posture monitoring for healthcare and rehabilitation to suspicious or dangerous actions detection for security surveillance. Infrared cameras such as Microsoft Kinect and wearable sensors have been the two most adopted devices for collecting data for measuring the bodily movements. These two types of sensors generally are categorized as contactless sensing and contact sensing respectively. Due to hardware limitation, each of the two sensor types has their inherent limitations. One most common problem associating with contactless sensing like Kinect is the distance and indirect angle between the camera and the subject. For wearable sensor, it is limited in recognizing complex human activities. In this paper, a novel data fusion framework is proposed for combining data which are collected from both sensors with the aim of enhancing the HAR accuracy. Kinect is able to capture details of bodily movements from complex activities, but the accuracy is dependent heavily on the angle of view; wearable sensor is relatively primitive in gathering spatial data but reliable for detecting basic movements. Fusing the data from the two sensor types enables complimenting each other by their unique strengths. In particular, a new scheme using incremental learning with decision table coupled with swarm-based feature selection is proposed in our framework for achieving fast and accurate HAR by fusing data of two sensors. Our experiment results show that HAR accuracy could be improved from 23.51% to 68.35% in a case of almost 90 degrees slanted view of Kinect sensing while a wearing sensor is used at the same time. The swarm feature selection in general is shown to enhance the HAR performance compared to standard feature selection method. The experiment results reported here contribute to the possibilities of using hybridized sensors from the machine learning perspective.
The precise prediction of health-related issues is a significant challenge in healthcare, with heart-related diseases posing a particularly threatening global health problem. Accurate prediction and ...recommendation for heart-related diseases are crucial for timely and effective treatment solutions. The primary objective of this study is to develop a classification model capable of accurately identifying heart diseases and providing appropriate recommendations for patients. The proposed system utilizes a multilevel-based classification mechanism employing Support Vector Machines. It aims to categorize heart diseases by analyzing patient’s vital parameters. The performance of the proposed model was evaluated by testing it on a dataset containing patient records. The generated recommendations are based on a comprehensive assessment of the severity of clinical features exhibited by patients, including estimating the associated risk of both clinical features and the disease itself. The predictions were evaluated using three metrics: accuracy, specificity, and the receiver operating characteristic curve. The proposed Multilevel Support Vector Machine (MSVM) classification model achieved an accuracy rate of 94.09% in detecting the severity of heart disease. This makes it a valuable tool in the medical field for providing timely diagnosis and treatment recommendations. The proposed model presents a promising approach for accurately predicting heart-related diseases and highlights the potential of soft computing techniques in healthcare. Future research could focus on further enhancing the proposed model’s accuracy and applicability.
Medical images are playing an important role in the medical domain. A mature medical visual question answering system can aid diagnosis, but there is no satisfactory method to solve this ...comprehensive problem so far. Considering that there are many different types of questions, we propose a model called CGMVQA, including classification and answer generation capabilities to turn this complex problem into multiple simple problems in this paper. We adopt data augmentation on images and tokenization on texts. We use pre-trained ResNet152 to extract image features and add three kinds of embeddings together to deal with texts. We reduce the parameters of the multi-head self-attention transformer to cut the computational cost down. We adjust the masking and output layers to change the functions of the model. This model establishes new state-of-the-art results: 0.640 of classification accuracy, 0.659 of word matching and 0.678 of semantic similarity in ImageCLEF 2019 VQA-Med data set. It suggests that the CGMVQA is effective in medical visual question answering and can better assist doctors in clinical analysis and diagnosis.
Enhancers are short genomic regions exerting tissue-specific regulatory roles, usually for remote coding regions. Enhancers are observed in both prokaryotic and eukaryotic genomes, and their ...detections facilitate a better understanding of the transcriptional regulation mechanism. The accurate detection and transcriptional regulation strength evaluation of the enhancers remain a major bioinformatics challenge. Most of the current studies utilized the statistical features of short fixed-length nucleotide sequences. This study introduces the location information of each k-mer (SeqPose) into the encoding strategy of a DNA sequence and employs the attention mechanism in the two-layer bi-directional long-short term memory (BD-LSTM) model (spEnhancer) for the enhancer detection problem. The first layer of the delivered classifier discriminates between enhancers and non-enhancers, and the second layer evaluates the transcriptional regulation strength of the detected enhancer. The SeqPose-encoded features are selected by the Chi-squared test, and 45 positions are removed from further analysis. The existing studies may focus on selecting the statistical DNA sequence descriptors with large contributions to the prediction models. This study does not utilize these statistical DNA sequence descriptors. Then the word vector of the SeqPose-encoded features is obtained by using the word embedding layer. This study hypothesizes that different word vector features may contribute differently to the enhancer detection model, and assigns different weights to these word vectors through the attention mechanism in the BD-LSTM model. The previous study generously provided the training and independent test datasets, and the proposed spEnhancer is compared with the three existing state-of-the-art studies using the same experimental procedure. The leave-one-out validation data on the training dataset shows that the proposed spEnhancer achieves similar detection performances as the three existing studies. While spEnhancer achieves the best overall performance metric MCC for both of the two binary classification problems on the independent test dataset. The experimental data shows that the strategy of removing redundant positions (SeqPose) may help improve the DNA sequence-based prediction models. spEnhancer may serve well as a complementary model to the existing studies, especially for the novel query enhancers that are not included in the training dataset.
Gastric cancer has a high incidence rate, significantly threatening patients' health. Gastric histopathology images can reliably diagnose related diseases. Still, the data volume of histopathology ...images is too large, making misdiagnosis or missed diagnosis easy. The classification model based on deep learning has made some progress on gastric histopathology images. However, traditional convolutional neural networks (CNNs) generally use pooling operations, which will reduce the spatial resolution of the image, resulting in poor prediction results. The image feature in previous CNN has a poor perception of details. Therefore, we design a dilated CNN with a late fusion strategy (DCNNLFS) for gastric histopathology image classification. The DCNNLFS model utilizes dilated convolutions, enabling it to expand the receptive field. The dilated convolutions can learn the different contextual information by adjusting the dilation rate. The DCNNLFS model uses a late fusion strategy to enhance the classification ability of DCNNLFS. We run related experiments on a gastric histopathology image dataset to verify the excellence of the DCNNLFS model, where the three metrics Precision, Accuracy, and F1-Score are 0.938, 0.935, and 0.959.
Screening and prioritization of chemicals is essential to ensure that available evaluation capacity is invested in those substances that are of highest concern. We, therefore, recently developed ...structural similarity models that evaluate the structural similarity of substances with unknown properties to known Substances of Very High Concern (SVHC), which could be an indication of comparable effects. In the current study the performance of these models is improved by (1) separating known SVHCs in more specific subgroups, (2) (re‐)optimizing similarity models for the various SVHC‐subgroups, and (3) improving interpretability of the predicted outcomes by providing a confidence score. The improvements are directly incorporated in a freely accessible web‐based tool, named the ZZS similarity tool: https://rvszoeksysteem.rivm.nl/ZzsSimilarityTool. Accordingly, this tool can be used by risk assessors, academia and industrial partners to screen and prioritize chemicals for further action and evaluation within varying frameworks, and could support the identification of tomorrow's substances of concern.
Within this study, similarity models were extended and optimized to improve the identification of substances of potential concern. We specifically (1) accounted for differences in mode of action, (2) upgraded the sub‐models, and (3) added quantitative confidence scores. The revised similarity models have been incorporated in the online freely available ZZS similarity tool (https://rvszoeksysteem.rivm.nl/ZzsSimilarityTool), with an user‐friendly interface both enhancing interpretability and input options.
Machine learning models have been adapted in biomedical research and practice for knowledge discovery and decision support. While mainstream biomedical informatics research focuses on developing more ...accurate models, the importance of data preprocessing draws less attention. We propose the Generalized Logistic (GL) algorithm that scales data uniformly to an appropriate interval by learning a generalized logistic function to fit the empirical cumulative distribution function of the data. The GL algorithm is simple yet effective; it is intrinsically robust to outliers, so it is particularly suitable for diagnostic/classification models in clinical/medical applications where the number of samples is usually small; it scales the data in a nonlinear fashion, which leads to potential improvement in accuracy.
To evaluate the effectiveness of the proposed algorithm, we conducted experiments on 16 binary classification tasks with different variable types and cover a wide range of applications. The resultant performance in terms of area under the receiver operation characteristic curve (AUROC) and percentage of correct classification showed that models learned using data scaled by the GL algorithm outperform the ones using data scaled by the Min-max and the Z-score algorithm, which are the most commonly used data scaling algorithms.
The proposed GL algorithm is simple and effective. It is robust to outliers, so no additional denoising or outlier detection step is needed in data preprocessing. Empirical results also show models learned from data scaled by the GL algorithm have higher accuracy compared to the commonly used data scaling algorithms.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
•Adaptive fault detection framework is proposed to minimize the performance degradation caused by the transition of recipe.•Unsupervised and semi-supervised domain adaptation are used ...successively.•The performance degradation was reduced, and the resilience was improved.•Effectiveness was demonstrated using real-world data.
The fault detection and classification (FDC) model is a prediction model that utilizes the sensor data of equipment to predict whether each wafer is faulty or not in the future, which is important to achieve a high yield and reduce the cost. To construct a high-performance FDC model with deep learning, a large amount of labeled training data is required. However, in real-world semiconductor manufacturing processes, the transition of recipe leads to a change in the distribution of input sensor data, which causes performance degradation for the existing FDC model. Model retraining for the new recipe is required, but a large time period is required to acquire a large amount of labeled data for the new recipe. In this study, an adaptive fault detection framework is proposed to minimize the performance degradation caused by the transition of recipe. In this framework, immediately after the recipe transition occurs, unsupervised adaptation is employed to reduce the performance degradation. After inspection results for some new recipe wafers are acquired, semi-supervised adaptation is employed to quickly recover the performance with a small amount of labeled data. Through experiments using real-world data, we demonstrate that the proposed framework can adapt to the new recipe with a reduced performance degradation.
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