Performance Of Classifier Research Articles

Optimized systolic array filters with noise classification for extracting FECG

To preserve the lives of both mother and foetus during the early phases of childbirth, heart abnormality diagnosis is essential. In remote places where understanding of maternal care is limited, the death rate caused by carelessness or a failure to detect abnormalities is still a problem. The signal will contain various external or internal noise sources to isolate the key components of the foetal heart rate from the mother's belly during labour. The likelihood that the genuine signal would be misinterpreted and result in a false report will increase in the presence of such noise sources. Although there are many software programs available for extracting the QRS features from foetal ECG signals, it is unavoidable that specialized hardware is required for a significant reduction in both area and power. This paper's main goal is to extract the QRS complex using LDA, then improve Social Spider classifier performance using the suggested TAODV as a distance metric calculator, and then compare against existing methods to discover sounds that are distorting the normal heart rate. Systolic array filter with suggested Glitch Avoidance Circuit employing MUX is simulated using Cadence Virtuoso in 65nm technology to remove noise from the observed QRS complex. Over 100 records with the necessary examples from MIT-BIH Arrhythmia were used in the simulations, and it was discovered that MATLAB 2010b was used to adopt a unique technique for classifying noise. The suggested TAODV-based SSA classifier's accuracy is 96.8%, whereas the accuracy of a filter with a glitch avoidance circuit is 96.13%. The primary benefit of these strategies comprises cutting-edge hardware and computational solutions.

Linearithmic and unbiased implementation of DeLong’s algorithm for comparing the areas under correlated ROC curves

Receiver operating characteristic (ROC) analysis is a widespread framework in two-group classification problems. The area under the ROC curve (AUC) has been a popular figure of merit to summarize the performance of binary classifiers. Because of its simplicity of implementation, DeLong’s algorithm (DLA) has been widely used in practice. However, DLA suffers two drawbacks of time inefficiency and biasedness. The authors’ previous work has overcome the first drawback. Specifically, the time complexity of DLA is reduced from the quadratic order down to a linearithmic one, by exploiting the relationship between the Heaviside function and the mid-ranks of samples. Unfortunately, the second drawback of DLA remains unaddressed, to the best of our knowledge. Motivated by such unsatisfactory situation, in this paper, the authors correct the bias of DLA with the assistance of an efficient implementation of Kendall’s tau (KT). The overall computational load of the improved DLA (being strictly unbiased) is in linearithmic time. Theoretical analyses and numerical results verified the proposed algorithmic findings in this work.

Deep learning based joint fusion approach to exploit anatomical and functional brain information in autism spectrum disorders

Background:The integration of the information encoded in multiparametric MRI images can enhance the performance of machine-learning classifiers. In this study, we investigate whether the combination of structural and functional MRI might improve the performances of a deep learning (DL) model trained to discriminate subjects with Autism Spectrum Disorders (ASD) with respect to typically developing controls (TD).Material and methodsWe analyzed both structural and functional MRI brain scans publicly available within the ABIDE I and II data collections. We considered 1383 male subjects with age between 5 and 40 years, including 680 subjects with ASD and 703 TD from 35 different acquisition sites. We extracted morphometric and functional brain features from MRI scans with the Freesurfer and the CPAC analysis packages, respectively. Then, due to the multisite nature of the dataset, we implemented a data harmonization protocol. The ASD vs. TD classification was carried out with a multiple-input DL model, consisting in a neural network which generates a fixed-length feature representation of the data of each modality (FR-NN), and a Dense Neural Network for classification (C-NN). Specifically, we implemented a joint fusion approach to multiple source data integration. The main advantage of the latter is that the loss is propagated back to the FR-NN during the training, thus creating informative feature representations for each data modality. Then, a C-NN, with a number of layers and neurons per layer to be optimized during the model training, performs the ASD-TD discrimination. The performance was evaluated by computing the Area under the Receiver Operating Characteristic curve within a nested 10-fold cross-validation. The brain features that drive the DL classification were identified by the SHAP explainability framework.ResultsThe AUC values of 0.66±0.05 and of 0.76±0.04 were obtained in the ASD vs. TD discrimination when only structural or functional features are considered, respectively. The joint fusion approach led to an AUC of 0.78±0.04. The set of structural and functional connectivity features identified as the most important for the two-class discrimination supports the idea that brain changes tend to occur in individuals with ASD in regions belonging to the Default Mode Network and to the Social Brain.ConclusionsOur results demonstrate that the multimodal joint fusion approach outperforms the classification results obtained with data acquired by a single MRI modality as it efficiently exploits the complementarity of structural and functional brain information.

Sub-alpine shrub classification using UAV images: Performance of human observers vs DL classifiers

In recent years, the automatic analysis of natural environment images acquired with unmanned aerial vehicles (UAV) has rapidly gained popularity. UAVs are specially important in mountainous forests where access is difficult and large areas need to be surveyed. In Zao mountains in northeastern Japan, regenerated fir saplings are competing with sub-alpine vegetation shrubs after a severe fir tree mortality caused by bark beetle infestation. A detailed survey of vegetation distribution is key to improve our understanding of species succession and the influence of climate change in that process. To that end, we evaluated the suitability of deep-learning-based automatic image classification of UAV images in order to map sub-alpine vegetation succession in large areas and the potential of fir regeneration.In order to assess the contribution of this technology in this research field, we first conducted an observer study to assess the difficulty for humans of the task of classifying vegetation from images. Afterwards, we compared the observers' accuracy to four state-of-the art deep learning networks for automatic image classification. The best observer accuracy of 55% demonstrates the limitations of species classification using only images. Furthermore, a detailed analysis of the sources of error showed that even though humans could differentiate between deciduous and evergreen species with an accuracy of 96%, identifying the correct species within each group proved much more challenging. In contrast, deep learning networks achieved accuracy values in the range of 70–80% for species classification, clearly demonstrating capabilities beyond human experts. Our experiments also indicated that the performance of these networks was significantly influenced by the similarity between the datasets used to fine-tune them and evaluate them. This fact highlights the importance of building publicly available images databases to further improve the results.Nevertheless, the results presented in this paper show that the analysis of UAV-acquired with deep learning networks can usher in a new type of large-scale study, spanning tenths or even hundreds of hectares with high spatial resolution (of a few cms per pixel), providing the ability to assess challenging vegetation dynamics problems that go beyond the ability of conventional fieldwork methodologies.

Applying Minority Range to Gini Index to Handle Imbalanced Dataset in Decision Tree Classifiers

The Class imbalance Problem is a common problem in Machine Learning where the number of instances in one class is significantly lower than the other, this can lead to biased classification models where the majority class dominates and the minority class is mis- misclassified. Decision Tree Classifiers are commonly used for classification tasks due to their simplicity and in interpretability. However, the class imbalance Problem can negatively impact the performance of decision tree Classifiers [1]. In this paper, we discuss a new approach to training a decision tree classifier that is an improvement over a pre-existing approach. We also provide experiments to prove that the proposed method is an improvement over the preexisting metrics

The Credit Card Anti-fraud Detection Model in the Context of Dynamic Integration Selection Algorithm

Because of its close relationship with information technology, every change in the field of information technology will have an important impact on the means, time and space distribution of the final fraud of the credit card network. The rapid development and popularization of artificial intelligence (AI) software based on Deepfake technology has greatly promoted the intelligent transformation of credit card network fraud. Through the use of advanced hardware and software equipment, criminals have iterated on their own fraud tools, from the use of traditional phone calls and text messages to the use of the latest AI software. Therefore, for the problem of credit card fraud detection with missing data set labels and highly unbalanced category distribution, A credit card approval anomaly detection model DES-HBOS (Dynamic Ensemble Selection based on Histogram-based Outlier Score) is proposed. Firstly, the unsupervised anomaly detection algorithm is used to construct the false label of the training set customer. Then, the customer capability area to be measured is determined, and the classifier performance is evaluated according to Pearson correlation coefficient. Finally, a set of optimal classifiers is selected to integrate the test customers. Experiments on real credit card customer data sets show that DES-HBOS has a higher Recall and can identify more fraudulent customers than other 6 classical anomaly detection models. Comparative experiments were carried out on 4 unbalanced data sets, and the experimental results showed that DES-HBOS had stronger anomaly detection ability than HBOS.

Detection of violence incitation expressions in Urdu tweets using convolutional neural network

The popularity and widespread use of social media are constantly generating unmonitored data, spreading unwanted content such as hate speech and expressions that incite violence. Automatic detection of violence incitation is a challenging task and to the best of our knowledge, Urdu language has been completely neglected. Therefore, a robust framework is proposed for identifying expressions exhibiting violence incitation in Urdu tweets. The potentials of the semantic, word embeddings, and language models are explored to learn contextualized representations of the violence incitation in Urdu tweets. In addition, the strength of the 1-Dimensional Convolutional Neural Network (1D-CNN) is exploited by tunning its parameters on the newly proposed annotated Urdu corpus. The annotated dataset consists of 4808 tweets manually collected from Pakistani Twitter accounts. The performance of 1D-CNN with word uni-gram, Urdu Bidirectional Encoder Representations from Transformer (Urdu-BERT), and Urdu- Robustly Optimized BERT Approach (Urdu-RoBERTa) models is compared to fine-tuned Urdu-RoBERTa, Bidirectional Long short-term memory (BiLSTM), Convolutional BiLSTM (CBi-LSTM), and six state-of-the-art Machine Learning (ML) models. The results reveal that the 1D-CNN with word uni-gram model shows benchmark performance by demonstrating 89.84% accuracy and 89.80% macro f1-score. Furthermore, it outperforms all comparable models and achieves 89.76% f1-score for the violence class, and 89.84% f1-score for not-violence class identification. The uniqueness of the proposed model is evaluated using MARS shine-through and MARS occlusion metrics and the CNN model outperformed the others. The MARS metrics facilitate evaluation and visualization of the classifier performance in terms of capturing unique true positive samples that are not predicted by other models. The findings of the proposed framework are very supportive for further investigation in this domain.

HiTaxon: a hierarchical ensemble framework for taxonomic classification of short reads.

Whole microbiome DNA and RNA sequencing (metagenomics and metatranscriptomics) are pivotal to determining the functional roles of microbial communities. A key challenge in analyzing these complex datasets, typically composed of tens of millions of short reads, is accurately classifying reads to their taxa of origin. While still performing worse relative to reference-based short-read tools in species classification, ML algorithms have shown promising results in taxonomic classification at higher ranks. A recent approach exploited to enhance the performance of ML tools, which can be translated to reference-dependent classifiers, has been to integrate the hierarchical structure of taxonomy within the tool's predictive algorithm. Here, we introduce HiTaxon, an end-to-end hierarchical ensemble framework for taxonomic classification. HiTaxon facilitates data collection and processing, reference database construction and optional training of ML models to streamline ensemble creation. We show that databases created by HiTaxon improve the species-level performance of reference-dependent classifiers, while reducing their computational overhead. In addition, through exploring hierarchical methods for HiTaxon, we highlight that our custom approach to hierarchical ensembling improves species-level classification relative to traditional strategies. Finally, we demonstrate the improved performance of our hierarchical ensembles over current state-of-the-art classifiers in species classification using datasets comprised of either simulated or experimentally derived reads. HiTaxon is available at: https://github.com/ParkinsonLab/HiTaxon.

TBscreen: A passive cough classifier for tuberculosis screening with a controlled dataset.

Recent respiratory disease screening studies suggest promising performance of cough classifiers, but potential biases in model training and dataset quality preclude robust conclusions. To examine tuberculosis (TB) cough diagnostic features, we enrolled subjects with pulmonary TB (N = 149) and controls with other respiratory illnesses (N = 46) in Nairobi. We collected a dataset with 33,000 passive coughs and 1600 forced coughs in a controlled setting with similar demographics. We trained a ResNet18-based cough classifier using images of passive cough scalogram as input and obtained a fivefold cross-validation sensitivity of 0.70 (±0.11 SD). The smartphone-based model had better performance in subjects with higher bacterial load {receiver operating characteristic-area under the curve (ROC-AUC): 0.87 [95% confidence interval (CI): 0.87 to 0.88], P < 0.001} or lung cavities [ROC-AUC: 0.89 (95% CI: 0.88 to 0.89), P < 0.001]. Overall, our data suggest that passive cough features distinguish TB from non-TB subjects and are associated with bacterial burden and disease severity.

Exploiting machine learning to tackle peculiar consumption of electricity in power grids: A step towards building green smart cities

AbstractThe increasing demand for electricity in daily life highlights the need for Smart Cities (SC) to use energy efficiently. Both technical and Non‐Technical Losses (NTL), particularly those resulting from electricity theft, present powerful obstacles; NTL alone can reach billions of dollars. Although Machine Learning (ML) based approaches for NTL detection have been embraced by numerous utilities, there is still a lack of thorough analysis of these methods. Limited research exists on NTL identification evaluation criteria and unbalanced data management in the context of SC. This research compares ML algorithms and data balancing methods to optimize electricity consumption detection. The given research applied the 15 ML techniques of Logistic regression, Bernoulli naive Bayes, Gaussian naive Bayes, K‐Nearest Neighbour, perceptron, passive‐aggressive classifier, quadratic discriminant analysis, SGD classifier, ridge classifier, linear discriminant analysis, decision tree, nearest centroid classifier, multi‐nomial naive Bayes, complement naive Bayes and dummy classifier. While SMOTE, AdaSyn, NRAS, and CCR are considered for data balancing. AUC, F1‐score, and seven relevant performance metrics were used for comparison. We have also implemented SHapely Additive exPlanations (SHAP) for feature importance and model interpretation. Results show varying classifier performance with different balancing methods, emphasizing data preprocessing's role in NTL detection for smart grid security.

A malware detection model based on imbalanced heterogeneous graph embeddings

The proliferation of malware in recent years has posed a significant threat to the security of computers and mobile devices. Detecting malware, especially on the Android platform, has become a growing concern for researchers and the software industry. This paper proposes a new method for detecting Android malware based on unbalanced heterogeneous graph embedding. First of all, most malware datasets contain an imbalance of malicious and benign samples, since some types of malware are scarce and difficult to collect. Thus, as a result of this problem, the classification algorithm is unable to analyze the minority samples through sufficient data, resulting in poor downstream classifier performance, in light of the fact that adversarial generation networks possess the characteristic of completing data, an algorithm for generating graph structure data is presented, in which nodes are generated to simulate the distribution of minority nodes within a network topology. Then, considering that heterogeneous information networks have the characteristics of retaining rich node semantic features and mining implicit relationships, heterogeneous graphs are used to construct models for different types of entities (i.e. Apps, APIs, permissions, intents, etc.) and different meta-paths. Finally, a new method is introduced to alleviate the over-smoothing phenomenon of node information in the propagation of deep network. In the deep GCN, we first sample the leader nodes of each layer node, and then add a residual connection and an identity map in order to determine the characteristics of the high-order leader. In this paper, a self-attention-based semantic fusion method is also applied to adaptively fuse embedded representations of software nodes under different meta-paths. The test results demonstrate that the proposed IHODroid model effectively detects malicious software. In the DREBIN dataset, which consists of 123,453 Android applications and 5,560 malicious samples, the IHODroid model achieves an accuracy of 0.9360 and an F1 score of 0.9360, outperforming other state-of-the-art baseline methods.

Impact analysis of real and virtual concept drifts on the predictive performance of classifiers

In the domain of machine learning and predictive analytics, classifiers hold significant importance as they are instrumental in extracting valuable patterns and enabling precise predictions from data. However, data distributions in streams are rarely stable in real-world circumstances and may evolve over time. Concept drifts, or dynamic changes in the data, have a major impact on how well classifiers predict outcomes. Furthermore, virtual concept drifts have become equally essential when dealing with the development of virtual environments and synthetic data production tools. This study employs a range of conventional classifiers, and ensemble models to examine the effects of real and virtual concept drifts on the predictive performance of classifiers. In order to ensure the conclusions are applicable to a wide range of domains, the study will be undertaken on a variety of datasets. The generation of incremental data streams from historical records with drifting patterns will replicate real concept drifts. Synthetic data will be incorporated into the training and testing sets to introduce virtual concept drifts. The classifier models are then assessed using the benchmark metrics for performance on these datasets. Comparative evaluations are conducted to evaluate the classifiers’ robustness and adaptability in the face of real and virtual concept drifts. The results of this study will shed light on the performance of various classifiers and models in dynamic data environments while offering helpful insights to businesses for developing machine learning applications that are more resilient. The research examined the impact of real and virtual concept drifts on classifier performance, using three models: Streaming Random Patches (SRP), Adaptive Windowing (ADWIN), and Streaming Random Patches with Adaptive Windowing (SRP-ADWIN). The study found that the SRP-ADWIN ensemble model outperformed SRP and ADWIN individually, effectively adapting to changing data distributions, and showed improved predictive accuracy of 98.63% for virtual drift and 86.46% for real drift.

Investigating molecular descriptors in cell-penetrating peptides prediction with deep learning: Employing N, O, and hydrophobicity according to the Eisenberg scale.

Cell-penetrating peptides comprise a group of molecules that can naturally cross the lipid bilayer membrane that protects cells, sharing physicochemical and structural properties, and having several pharmaceutical applications, particularly in drug delivery. Investigations of molecular descriptors have provided not only an improvement in the performance of classifiers but also less computational complexity and an enhanced understanding of membrane permeability. Furthermore, the employment of new technologies, such as the construction of deep learning models using overfitting treatment, promotes advantages in tackling this problem. In this study, the descriptors nitrogen, oxygen, and hydrophobicity on the Eisenberg scale were investigated, using the proposed ConvBoost-CPP composed of an improved convolutional neural network with overfitting treatment and an XGBoost model with adjusted hyperparameters. The results revealed favorable to the use of ConvBoost-CPP, having as input nitrogen, oxygen, and hydrophobicity together with ten other descriptors previously investigated in this research line, showing an increase in accuracy from 88% to 91.2% in cross-validation and 82.6% to 91.3% in independent test.

Boosting and Comparing Performance of Machine Learning Classifiers with Meta-heuristic Techniques to Detect Code Smell

Background: Continuous modifications, suboptimal software design practices, and stringent project deadlines contribute to the proliferation of code smells. Detecting and refactoring these code smells are pivotal to maintaining complex and essential software systems. Neglecting them may lead to future software defects, rendering systems challenging to maintain, and eventually obsolete. Supervised machine learning techniques have emerged as valuable tools for classifying code smells without needing expert knowledge or fixed threshold values. Further enhancement of classifier performance can be achieved through effective feature selection techniques and the optimization of hyperparameter values. Aim: Performance measures of multiple machine learning classifiers are improved by fine tuning its hyperparameters using various type of meta-heuristic algorithms including swarm intelligent, physics, math, and bio-based etc. Their performance measures are compared to find the best meta-heuristic algorithm in the context of code smell detection and its impact is evaluated based on statistical tests. Method: This study employs sixteen contemporary and robust meta-heuristic algorithms to optimize the hyperparameters of two machine learning algorithms: Support Vector Machine (SVM) and k-nearest Neighbors (k-NN). The No Free Lunch theorem underscores that the success of an optimization algorithm in one application may not necessarily extend to others. Consequently, a rigorous comparative analysis of these algorithms is undertaken to identify the best-fit solutions for code smell detection. A diverse range of optimization algorithms, encompassing Arithmetic, Jellyfish Search, Flow Direction, Student Psychology Based, Pathfinder, Sine Cosine, Jaya, Crow Search, Dragonfly, Krill Herd, Multi-Verse, Symbiotic Organisms Search, Flower Pollination, Teaching Learning Based, Gravitational Search, and Biogeography-Based Optimization, have been implemented. Results: In the case of optimized SVM, the highest attained accuracy, AUC, and F-measure values are 98.75%, 100%, and 98.57%, respectively. Remarkably, significant increases in accuracy and AUC, reaching 32.22% and 45.11% respectively, are observed. For k-NN, the best accuracy, AUC, and F-measure values are all perfect at 100%, with noteworthy hikes in accuracy and ROC-AUC values, amounting to 43.89% and 40.83%, respectively. Conclusion: Optimized SVM exhibits exceptional performance with the Sine Cosine Optimization algorithm, while k-NN attains its peak performance with the Flower Optimization algorithm. Statistical analysis underscores the substantial impact of employing meta-heuristic algorithms for optimizing machine learning classifiers, enhancing their performance significantly. Optimized SVM excels in detecting the God Class, while optimized k-NN is particularly effective in identifying the Data Class. This innovative fusion automates the tuning process and elevates classifier performance, simultaneously addressing multiple longstanding challenges.

Quality assurance of late gadolinium enhancement cardiac magnetic resonance images: a deep learning classifier for confidence in the presence or absence of abnormality with potential to prompt real-time image optimization

BackgroundLate gadolinium enhancement (LGE) of the myocardium has significant diagnostic and prognostic implications, with even small areas of enhancement being important. Distinguishing between definitely normal and definitely abnormal LGE images is usually straightforward, but diagnostic uncertainty arises when reporters are not sure whether the observed LGE is genuine or not. This uncertainty might be resolved by repetition (to remove artifact) or further acquisition of intersecting images, but this must take place before the scan finishes. Real-time quality assurance by humans is a complex task requiring training and experience, so being able to identify which images have an intermediate likelihood of LGE while the scan is ongoing, without the presence of an expert is of high value. This decision-support could prompt immediate image optimization or acquisition of supplementary images to confirm or refute the presence of genuine LGE. This could reduce ambiguity in reports. MethodsShort-axis, phase-sensitive inversion recovery late gadolinium images were extracted from our clinical cardiac magnetic resonance (CMR) database and shuffled. Two, independent, blinded experts scored each individual slice for “LGE likelihood” on a visual analog scale, from 0 (absolute certainty of no LGE) to 100 (absolute certainty of LGE), with 50 representing clinical equipoise. The scored images were split into two classes—either “high certainty” of whether LGE was present or not, or “low certainty.” The dataset was split into training, validation, and test sets (70:15:15). A deep learning binary classifier based on the EfficientNetV2 convolutional neural network architecture was trained to distinguish between these categories. Classifier performance on the test set was evaluated by calculating the accuracy, precision, recall, F1-score, and area under the receiver operating characteristics curve (ROC AUC). Performance was also evaluated on an external test set of images from a different center. ResultsOne thousand six hundred and forty-five images (from 272 patients) were labeled and split at the patient level into training (1151 images), validation (247 images), and test (247 images) sets for the deep learning binary classifier. Of these, 1208 images were “high certainty” (255 for LGE, 953 for no LGE), and 437 were “low certainty”. An external test comprising 247 images from 41 patients from another center was also employed. After 100 epochs, the performance on the internal test set was accuracy = 0.94, recall = 0.80, precision = 0.97, F1-score = 0.87, and ROC AUC = 0.94. The classifier also performed robustly on the external test set (accuracy = 0.91, recall = 0.73, precision = 0.93, F1-score = 0.82, and ROC AUC = 0.91). These results were benchmarked against a reference inter-expert accuracy of 0.86. ConclusionDeep learning shows potential to automate quality control of late gadolinium imaging in CMR. The ability to identify short-axis images with intermediate LGE likelihood in real-time may serve as a useful decision-support tool. This approach has the potential to guide immediate further imaging while the patient is still in the scanner, thereby reducing the frequency of recalls and inconclusive reports due to diagnostic indecision.

Optimizing Credit Card Fraud Detection: Classifier Performance and Feature Selection Empowered by Grasshopper Algorithm

Optimizing Credit Card Fraud Detection: Classifier Performance and Feature Selection Empowered by Grasshopper Algorithm

Role of CKD273 in Diabetic Nephropathy: A Systematic Review

Introduction: Diabetic Nephropathy (DN) is a major cause of increased morbidity and mortality in patients with Diabetes Mellitus (DM). The development of novel biomarkers for kidney disease, especially DN, is becoming a focus of medical research. Serum creatinine and urine albumin measurements had been the gold standards for the diagnosis of DN until the advent of newer markers like Cystatin C, Kidney Injury Molecule-1, etc. Currently, there is no non invasive investigation for the prediction of DN. Nevertheless, the field of proteomics is advancing at a rapid pace, such that urinary proteomic biomarkers have found a spot in the diagnosis of DN. The role of the urinary proteomic Chronic Kidney Disease 273 (CKD273) classifier, in particular, has been demonstrated in early prediction. Aim: To highlight the performance of the CKD273 classifier in the early prediction of DN. Materials and Methods: Pubmed and Scopus databases were extensively searched using the keywords diabetes, DM, renal disease or kidney disease, proteomics or peptide, urinary markers or biomarkers. Full-text original articles conducted on humans and written in English, published between 2014 and 2022, specifically prospective and retrospective observational studies, crosssectional studies, Randomised Controlled Trials (RCT), and casecontrol studies investigating the efficacy of the urinary biomarker CKD273 in the prediction of DN were included in the review. The literature search, strictly based on Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines, led to the identification of eight relevant studies, which were included in the present review. The quality and Risk of Bias (RoB) of the selected articles were assessed using the Newcastle Ottowa scale (NOS) and Risk Of Bias 2 (ROB2) tool. Results: The selected studies proved the efficacy of the urinary proteomic classifier CKD273 as a cost-effective biomarker in predicting the decline in renal function in both normoalbuminuric diabetics and DN patients. Conclusion: Based on the articles included in the present systematic review, it has been found that CKD273 plays a vital role in the early prediction of renal dysfunction in normoalbuminuric diabetics as well as in the detection of progressing renal disease in DN patients. It has also been found to be cost-effective compared to established markers such as estimated Glomerular Filtration Rate (eGFR), Urine Albumin Excretion Rate (UAER), etc.

Efficiency of convolutional neural networks of different architecture for the task of depression diagnosis from EEG data

The purpose of this paper is to comparatively analyse the efficiency of using artificial neural networks with different convolutional and recurrent architectures in the task of depression diagnosis based on electroencephalogram (EEG) data. Open datasets were chosen as objects of the study and own EEG data of real patients with depression were collected. Methods. To solve the problem of identifying biomarkers of depressive disorder from EEG data, we used convolutional neural networks using two-dimensional or one-dimensional convolution operation, as well as hybrid models of convolutional and recurrent neural networks. To test the developed models of artificial neural networks, we selected open data sets, performed an experiment to collect our own data from real depressed patients, and merged the prepared data sets. The result of this work is analysis and comparison of the performance of different classifiers based on convolutional and recurrent neural network models. Conclusion. We show that the average accuracy of classification of depressive disorder in a test sample using cross-validation was 0.68. The results are consistent with the known results from the literature for small patient-disaggregated datasets. Although the accuracy obtained in this task is insufficient for practical application of the model, it can be argued that further research to improve the efficiency of the model is promising, as well as the need to create a sufficiently large representative dataset of depressed patients, which is an important scientific task for further construction of biophysical models of depressive disorders.

Accurate COVID-19 detection using full blood count data and machine learning

COVID-19 has spread rapidly worldwide in the past three years, triggering partial and full lockdowns globally. The successful control of the COVID-19 pandemic on a global scale depended heavily upon the accurate detection of COVID-19. However, the main diagnostic tests for COVID-19 have some significant limitations, e.g. the major nucleic acid (RT-PCR) tests while having a high sensitivity are time-consuming and require expensive equipment with the shortage of test kits in many countries. Antigen lateral flow tests have a lower sensitivity and they cannot be used during the early pandemic as well as usually more expensive than the full or complete blood count test used in this paper which can be potentially performed using a finger blood sample. The last decade has seen rapid growth of AI, particularly deep learning, which has found wide applications in medical image analysis, with results comparable to and even surpassing human expert performance. There have been several machine learning models reported for COVID-19 diagnostics or prognosis predictions, most of them based on CT and X-ray images. In this paper we have applied traditional machine learning and convolutional neural networks (CNNs) based deep learning to the blood test data obtained from hematology analyzers and demonstrated that the AI models can be used to detect COVID-19 with a high degree of accuracy (&gt;97%). The performance of different classifiers will be compared and discussed. The work should have potential applications in current COVID-19 and future pandemics.

Enhancing landslide susceptibility modelling through a novel non-landslide sampling method and ensemble learning technique

In recent years, several catastrophic landslide events have been observed throughout the globe, threatening to lives and infrastructures. To minimize the impact of landslides, the need of landslide susceptibility map is important. The study aims to extract high-quality non-landslide samples and improve the accuracy of landslide susceptibility modelling (LSM) outcomes by applying a coupled method of ensemble learning and Machine Learning (ML). The Zigui-Badong section of the Three Gorges Reservoir area (TGRA) in China was considered in the present study. Twelve influencing factors were selected as inputs for LSM, and the relationship between each causal factor and landslide spatial development was quantitatively analyzed. A total of 179 landslides have been used in the present study. About 70% of the landslide pixels were randomly considered for training, and the remaining 30% were used for validation. Logistic Regression (LR) model was applied to produce an initial susceptibility map, and the non-landslide samples were selected within the classified low-susceptibility zone. Subsequently, two ML classifiers – the Classification and Regression Tree (CART), and the Multi-Layer Perceptron (MLP), and four coupling models – the CART-Bagging, CART-Boosting, MLP-Bagging, and MLP-Boosting, were utilized for LSM. Finally, the receiver operating characteristics (ROC) curve and statistical analysis were applied for accuracy assessment. The results show that altitude and distance to rivers were the main causal factors of landslides in the study area. The LR-MLP-Boosting performed the best with an accuracy of 0.986 followed by the LR-CART-Bagging, LR-CART-Boosting, and LR-MLP-Bagging. Accuracy comparisons demonstrate that ensemble learning algorithm can notably enhance the LSM performance of ML classifiers, and the Boosting algorithm marginally outperforms the Bagging algorithm. Moreover, the LR model can effectively constrain the selection range of non-landslide samples. The non-landslide sampling method constrained by LR yields higher quality samples compared to raditional random sampling method with no constraints, which develops a more excellent LSM.