High Accuracy Value Research Articles

Predicting survival of patients with heart failure by optimizing the hyperparameters

Heart failure is a prominent global cause of mortality. Heart failure is a medical condition characterized by the heart's inability to adequately circulate blood throughout the body or meet its needs. The rising expenses associated with conventional medical treatments for heart failure diagnosis have underscored the significance of developing diagnostic systems that utilize machine learning approaches. We employed various machine learning techniques on a heart failure dataset from the literature, specifically focusing on the survival status of heart failure patients. After employing the hold-out validation technique to prevent overfitting, the survival status of patients was evaluated by utilizing GridSearchCV hyperparameter optimization on classifiers such as Naive Bayes, Support Vector Machines, Decision Trees, Random Forests, K-Nearest Neighbor, Discriminant Analysis, and Extreme Gradient Boosting. Our performance measurement results show that the Decision Trees, Support Vector Machines, and Naive Bayes algorithms are prominent algorithms for the relevant data. While Decision Tree has the highest accuracy value, Support Vector Machines gives the lowest false negative rate, a crucial metric in medical decisions. Additionally, Naive Bayes has very similar results to the Support Vector Machines algorithm. While the straightforward and effective Decision Tree model, which has minimal pre-processing and does not require input scaling, is an easy-to-use method, Support Vector Machines and Naive Bayes which has low false negative rate values may help decision-making by reducing the risk of misdiagnosis.

Theoretical and Experimental Investigation of Thermal Conductivity of Unsaturated Soils Amended with a Sustainable Biochar

This study investigates the thermal conductivity of unsaturated kaolin soil amended with biochar to promote sustainable geotechnical engineering. Biochar from agricultural waste offers the dual benefits of carbon sequestration and sustainable waste management. Experimental measurements were conducted for kaolin soil with 0% (control) and 10% biochar under varying moisture contents. Peach pit biochar increased thermal conductivity by 2–3% at 30–40% saturation and 40–50% at higher saturation as compared to the bare soil. Reed biochar decreased thermal conductivity by 1–2% at lower saturation but increased it by 55–60% at higher saturation. Applewood biochar increased thermal conductivity by 35–50% at moderate saturation, decreased beyond 50% water content, and had minimal variation at lower saturation. Further, the existing empirical models (such as Kersten and the Johansen model, Wiener’s model, and Mickley’s model) for predicting the thermal conductivity of materials were validated using the measured results of biochar-amended soils. Adding 10% biochar reduces thermal conductivity by 34.8%, and the Haigh model (2012) fits best with high accuracy and lower RMSE values than models such as Kersten and Johansen, which appears to be less reliable in case of biochar-amended soils. With an addition of biochar, the R2 values of the models decreased from a range of 0.8 to 0.9 to a range of 0.4–0.6, indicating the need for better model adaptation. Wiener bounds accurately predicted thermal conductivity at low saturation levels but varied greatly at higher ones. The most variable sample was peach pit biochar, highlighting the need to refine predictive models for material-specific differences. These findings provide a foundation for developing improved predictive models and integrating biochar into sustainable geotechnical and geothermal systems.

A robust hybrid estimation method for local bearing defect size based on analytical model and morphological analysis.

This paper presents a robust hybrid method using an analytical model and morphological analysis for estimating defect sizes on bearings, addressing inaccuracies in traditional peak-based approaches. First, a spatial contact analytical model is developed to analyze roller movement through defects. Numerical results indicate that radial defect sizes significantly impact dual-impulse intervals for accurate estimation, while axial size has minimal effect. An analysis of contact deformation, force, and vibration response reveals that the defect entry aligns before the first low-frequency peak, and the exit aligns after the first high-frequency peak, preceding the second low-frequency peak. Finally, testing of the derived model on four samples, incorporating vibration characteristics and geometry, achieves a minimum estimation error of 0.03%, demonstrating high accuracy and practical value.

Prediction of genotypic values via REML/BLUP and path analysis in maize half-sib families

Maize has significant relevance to food and economic security. The study aimed to evaluate the genetic gain between maize half-sib families, in the first cycle of recurrent selection, using the REML/BLUP methodology. Path analysis was also performed in order to understand the relationship between the agronomic traits. The experiment was conducted at the Federal Institute of Education, Science and Technology of Triangulo Mineiro – Campus Uberlandia. The agronomic traits evaluated, were: plant height (PH), ear height (EH), stem diameter (SD), ear length (EL), ear diameter (ED) and grain weight (GW). The results indicated significant differences only for the traits EL, GW and ED. A high coefficient of genetic variation (CVg) was observed for the traits EL, GW and ED, with only ED presenting a coefficient of relative variation (CVr) above one. The REML/BLUP methodology demonstrated high accuracy values and the estimates of gains were promising, with values of 24.07%, 21.88% e 18.23% for EL, GW and ED, respectively. Path analysis revealed the possibility of indirect gains in GW through EL.

An Application of Machine-Learning-Oriented Radiomics Model in Clear Cell Renal Cell Carcinoma (ccRCC) Early Diagnosis.

Aims/Background Clear cell renal cell carcinoma (ccRCC) is a common and aggressive form of kidney cancer, where early diagnosis is crucial for improving prognosis and treatment outcomes. Radiomics, which utilizes machine learning techniques, presents a promising approach in medical imaging for the early detection and characterization of such conditions. This study aims to explore the clinical utility of a machine-learning-based radiomics model in the early diagnosis of ccRCC. Methods Case data and abdominal computed tomography (CT) tumour images of patients with ccRCC were obtained from The Cancer Imaging Archive (TCIA) database. The dataset included 31 cases in the training set (19 males and 12 females, with an average age of 58.1 years) and 13 cases in the validation set (8 males and 5 females, with an average age of 69.6 years). The volume of interest (VOI) was manually delineated, slice by slice, along the tumour's edge in cross-sectional images of ccRCC. Radiomics features were extracted from each region of interest (ROI) using the "PyRadiomics" plug-in in 3D Slicer software (version 5.1.0, Massachusetts Institute of Technology and Brigham and Women's Hospital, Boston, MA, USA). Feature selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, followed by 10-fold cross-validation. The selected radiomics features were then used to construct prediction models based on two different supervised machine learning algorithms: logistic regression and random forest. The diagnostic performance of these models was evaluated using receiver operating characteristic (ROC) curves and calibration curves. Finally, clinical data were integrated with the radiomics features to enhance the prediction model. Results A total of 44 radiomics features were ultimately selected to establish the prediction model based on the training set results. Among the two machine learning models, the logistic regression model demonstrated superior diagnostic performance. An evaluation of model establishment, considering both individual radiomics features (DifferenceVariance, JointEnergy.1, JointEntropy.2, MeanAbsoluteDeviation.7, SmallAreaHighGrayLevelEmphasis.7) and clinical data, indicated that the logistic regression model was stable and exhibited strong diagnostic performance, good calibration, and clinical applicability in patients with ccRCC. When clinical data were combined with radiomics features in the model, the area under the curve (AUC) reached 0.969, with an optimal threshold of -2.290, and sensitivity and specificity values of 89.3% and 95.2%, respectively. The calibration curve also confirmed that the logistic regression model had high calibration accuracy and greater clinical application value. Conclusion This machine-learning-based radiomics prediction model demonstrated significant value in the early diagnosis of clear cell renal cell carcinoma (ccRCC).

Machine Learning-Based Cow Milk Quality Classification using Recursive Feature Elimination Cross-Validation

<p>Milk quality is of paramount importance as it directly impacts consumer health and well-being. High-quality milk is rich in essential nutrients such as calcium, protein, and vitamins, contributing to overall nutrition. Moreover, ensuring milk quality is vital for preventing the transmission of diseases and contaminants through dairy products. Therefore, research in this field is essential to guaranteeing the safety and nutritional value of milk consumed by individuals of all ages. In this paper, the design of machine learning-based grade measuring devices with recursive feature elimination with cross-validation (RFECV) is carried out as a guide in the design of a milk grade detection system. The milk is rated as low, medium, or high based on these criteria. The sensors will gather this information from the milk with the aid of the microcontroller. The algorithms utilized in this study and the results obtained from K-Nearest Neighbors (KNN) combined with the RFECV algorithm have a higher accuracy value: 17,20% better than the support vector machine (SVM) model, 25.37% better than the single K-Nearest Neighbors (KNN), and 26.37% better than the random forest (RF) model trained without RFECV. Using seven input features (pH, temperature, taste, odor, fat, turbidity, and color), the proposed model produces 96.27% accuracy.</p>

An artificial intelligence mechanism for detecting cystic lesions on CBCT images using deep learning.

The present study aimed to provide and evaluate the efficiency of an artificial intelligence mechanism for detecting cystic lesions on cone beam computed tomography (CBCT) scans. The CBCT image dataset consisted of 150 samples, including 50 cases without lesions, 50 dentigerous cysts (DC), and 50 periapical cysts (PC) based on both radiographic and histopathological diagnosis. The dataset was divided into a development set with 70 % of samples for training and validation and a final test set with the other 30 % of samples. Four images were obtained for each case, including panoramic, manually segmented panoramic, axial, and manually segmented axial images. A deep convolutional neural network (CNN) architecture was used for automatic lesion detection and diagnosing the type of cystic lesion. To increase the number of image samples and avoid overfitting, a data augmentation procedure was applied. Recall, precision, F1-score, and average precision (AP) values were measured for lesion detection performance, and sensitivity, specificity, and accuracy indicators from the confusion matrix were calculated for the lesion classification performance of the CNN model. Mean average precision, recall, and F1-score for the detection of DCs and PCs were respectively, 0.87, 0.92, and 0.89 before data augmentation, and 0.93, 0.95, and 0.93, after the augmentation process. For the classification of DCs with data augmentation, sensitivity, specificity, accuracy, and AUC values were 96.4 %, 99.5 %, 97.3 %, and 0.98, respectively, and for PCs with augmentation, these values were 89.6 %, 98.9 %, 98.1 %, and 0.94, respectively. Lastly, for no lesion samples, sensitivity, specificity, accuracy, and AUC values were 100 %, 99.1 %, 99.4 %, and 0.99, respectively, by application of data augmentation. Our developed deep learning-based CNN algorithm showed high accuracy, sensitivity, and precision values (more than 90 %) for detecting and classifying dentigerous and periapical cysts on CBCT images using data augmentation.

Development of Enriched ROA With Dilated Hybrid Network for Automatic Modulation Classification Framework in CRNs

ABSTRACTAutomatic modulation classification (AMC) is explained as accurately identifying a modulation of a received signal. AMC systems are a significant component of cognitive radio network (CRN) systems. It is difficult to perform modulation classification on an unsettled radio signal without any previous knowledge of the signal's properties. In this work, the deep learning‐aided AMC is suggested to solve the difficulties of the existing models. In the proposed approach, the modulation classification is attained by performing two steps: (a) data collection and (b) classification. Initially, the required data related to the cognitive environment is collected from online resources. Later, the garnered data are passed to the classification phase. The AMC is performed by the adaptive and dilated hybrid network (ADHN), which is the combination of a temporal convolution network (TCN) and a gated recurrent unit (GRU). The ADHN accurately classifies the modulation even in a noisy environment. The classification performance of the ADHN is further boosted by tuning the parameters of this network via the enriched remora optimization algorithm (EROA). This proposed modulation classification model is suitable for various channels. The comparative validation is performed to ensure the usefulness of the designed system via several measures. By experimental analysis, the proposed system acquires the high value of accuracy, precision, and f1‐score by 94.2, 80.2, and 86.7, respectively, when compared with classical approaches. In addition to this, other metrics are considered and obtained with more true value and less false value. Thus, it ensures the effectiveness of classifying the modulation types in CRNs.

Assessing statistical neural networks potentiality to distinguish PDO Kalamata and Molaoi olive oil varieties

Several regional areas in Greece produce high quality olive oil by cultivating certain varieties. Olive oil varieties of Kalamata and Molaoi are of special interest, since they produce extra virgin olive oil. Concretely, Kalamata is a city located in southwestern Greece. Intuitively, Molaoi is a town located in southeastern Greece. Subsequently, both geographic locations are known for their famous olive oil quality. Continually, Protected Designation of Origin (PDO) Kalamata olive oil, established by Council regulation (EC) No 510/2006, is considered an exceptional extra virgin olive oil variety. Specifically, there is a need to distinguish PDO Kalamata olive oil from other olive oil varieties is Greece such as the Molaoi olive oil, since PDO Kalamata olive oil is the main variety exported in the global olive oil market. Distinguishing PDO Kalamata from Molaoi olive oil is possible by incorporating statistical neural networks. Concretely, applying neural network experimentation enables differentiation between variations of certain chemical characteristics observed in certain geographic locations of Greece. In this paper, we use statistical neural networks to distinguish the geographical origin of PDO Kalamata olive oil compared with Molaoi olive oil based on synchronous excitation−emission fluorescence spectroscopy of provided olive oils samples evaluated in the chemical laboratory. Evaluation based on certain experimentation phase and subsequent data visualization of the adopted statistical neural networks are promising for distinguishing the samples of PDO Kalamata olive oil with high values of prediction accuracy. Such ability enables olive oil industry to assess extra virgin olive oil profitable potentiality in global market.

Pemanfaatan Chi Square dan Ensemble Tree Classifier pada Model SVM, KNN dan C4.5 dalam Penjualan Online

This research aims to assist MSMEs in overcoming problems in online sales. Currently, sellers only prepare stock without knowing how well the products are sold in their market segment. In the city of Tangerang alone, there are 222,602 MSMEs with various product categories. Therefore, besides utilizing offline sales, business actors should also engage in online sales. This research conducts feature selection using the Chi-Square method and Ensemble Tree Classifier to select the top 6 and 10 features. The SVM, KNN, and C4.5 algorithms are used to build prediction models based on the selected features. Using feature selection, it was found that the influential features are Estimated Shipping Cost, Shipping Cost Paid by Buyer, Total Product Price, and Estimated Shipping Cost Discount. The evaluation results using the three algorithms, SVM, KNN, and C4.5, indicate that the highest accuracy value is obtained when using the C4.5 model with data from the ensemble tree classifier with 6 features at 0.86%, followed by the C4.5 model with 10 features, KNN with 6 features, and KNN with 10 features, all of which source data from the ensemble tree classifier with an accuracy value of 0.85%.

Chest x-ray images: transfer learning model in COVID-19 detection.

This research aims to develop an effective algorithm for diagnosing COVID-19 in chest X-rays using the transfer learning method and support vector machines. In total, data was collected from 10 clinics, including both large city hospitals and smaller medical institutions. This ensured a diverse range of geographical and demographic information in the sample. An extensive data set was collected, including 10,000 chest X-ray images. 5000 images represent normal cases, 3993 images represent pneumonia cases, and 1007 images represent COVID-19 cases. Machine learning methods were applied to develop a classification model, and the results were compared with seven state-of-the-art models and a lightweight CNN architecture. The results showed that the proposed method achieves high accuracy values (Accuracy): 0.95 for COVID-19, 0.89 for pneumonia, and 0.92 for normal images (p < 0.05). Comparison with other models demonstrates statistically significant superiority of our method in accuracy across all three classes. The EfficientNet-B0 model surpasses our method only in accuracy for normal images with p < 0.01, confirming the advantages of our method. Our method demonstrates high sensitivity values (Sensitivity): 0.96 for COVID-19, 0.88 for pneumonia, and 0.93 for normal images (p < 0.05), outperforming most of the compared models. Correlation analysis showed Pearson coefficients of 0.92, 0.89, and 0.94 for COVID-19, pneumonia, and normal images, respectively, confirming a high degree of consistency between predicted and true class labels. In addition, the model was validated on external datasets to assess its generalizability. This validation confirmed its high level of effectiveness in a variety of clinical settings. This study confirms the importance of applying machine learning methods in medical applications and opens new perspectives for early diagnosis of infectious diseases. The practical application of the obtained results can enhance the efficiency of diagnosis and control the spread of COVID-19, as well as contribute to the development of innovative methods in medical practice.

Fourier transform InfraRed spectra analyzed by multivariate and machine learning methods in determination spectroscopy marker of prostate cancer in dried serum

Prostate cancer represents the second most prevalent form of cancer in males globally. In the diagnosis of prostate cancer, the most commonly utilised biomarker is prostate-specific antigen (PSA). It is unfortunate that approximately 25 % of men with elevated PSA levels do not have cancer, and that approximately 20 % of patients with prostate cancer have normal serum PSA levels. Accordingly, a more sensitive methodology must still be identified. It is imperative that new diagnostic methods should be non-invasive, cost-effective, rapid, and highly sensitive. Fourier transform infrared spectroscopy (FTIR) is a technique that fulfils all of the aforementioned criteria. Consequently, the present study used FTIR to assess dried serum samples obtained from a cohort of prostate cancer patients (n = 53) and a control group of healthy individuals (n = 40). Furthermore, this study proposes FTIR markers of prostate cancer obtained from serum. For this purpose, FTIR spectra of dried serum were measured and analysed using statistical, chemometric and machine learning (ML) algorithms including decision trees C5.0, Random Forest (RF), k-Nearest Neighbours (kNN) and Support Vector Machine (SVM). The FTIR spectra of serum collected from patients suffering from prostate cancer exhibited a reduced absorbance values of peaks derived from phospholipids, amides, and lipids. However, these differences were not statistically significant. Furthermore, principal component analysis (PCA) demonstrated that it is challenging to distinguish serum samples from healthy and non-healthy patients. The ML algorithms demonstrated that FTIR was capable of differentiating serum collected from both analysed groups of patients with high accuracy (values between 0.74 and 0.93 for the range from 800 cm−1 to 1800 cm−1 and around 0.70 and 1 for the range from 2800 cm−1 to 3000 cm−1), depending on the ML algorithms used. The results demonstrated that the peaks at 1637 cm−1 and 2851 cm−1 could serve as a FTIR marker for prostate cancer in serum samples. Furthermore, the correlation test indicated a clear correlation between these two wavenumbers and four of the five clinical parameters associated with prostate cancer. However, the relatively small number of samples collected only from patients over the age of 60 indicated that the results should be further investigated using a larger number of serum samples collected from a mean age range. In conclusion, this study demonstrated the potential of FTIR for the detection of prostate cancer in serum samples, highlighting the presence of distinctive spectroscopic markers associated with the analysed cancer type.

Correction of Batch Effect in Gut Microbiota Profiling of ASD Cohorts from Different Geographical Origins.

To date, there have been numerous metataxonomic studies on gut microbiota (GM) profiling based on the analyses of data from public repositories. However, differences in study population and wet and dry pipelines have produced discordant results. Herein, we propose a biostatistical approach to remove these batch effects for the GM characterization in the case of autism spectrum disorders (ASDs). An original dataset of GM profiles from patients with ASD was ecologically characterized and compared with GM public digital profiles of age-matched neurotypical controls (NCs). Also, GM data from seven case-control studies on ASD were retrieved from the NCBI platform and exploited for analysis. Hence, on each dataset, conditional quantile regression (CQR) was performed to reduce the batch effects originating from both technical and geographical confounders affecting the GM-related data. This method was further applied to the whole dataset matrix, obtained by merging all datasets. The ASD GM markers were identified by the random forest (RF) model. We observed a different GM profile in patients with ASD compared with NC subjects. Moreover, a significant reduction of technical- and geographical-dependent batch effects in all datasets was achieved. We identified Bacteroides_H, Faecalibacterium, Gemmiger_A_73129, Blautia_A_141781, Bifidobacterium_388775, and Phocaeicola_A_858004 as robust GM bacterial biomarkers of ASD. Finally, our validation approach provided evidence of the validity of the QCR method, showing high values of accuracy, specificity, sensitivity, and AUC-ROC. Herein, we proposed an updated biostatistical approach to reduce the technical and geographical batch effects that may negatively affect the description of bacterial composition in microbiota studies.

Neuro connect: Integrating data-driven and BiGRU classification for enhanced autism prediction from fMRI data

ABSTRACT Autism Spectrum Disorder (ASD) poses a significant challenge in early diagnosis and intervention due to its multifaceted clinical presentation and lack of objective biomarkers. This research presents a novel approach, termed Neuro Connect, which integrates data-driven techniques with Bidirectional Gated Recurrent Unit (BiGRU) classification to enhance the prediction of ASD using functional Magnetic Resonance Imaging (fMRI) data. This study uses both structural and functional neuroimaging data to investigate the complex brain underpinnings of autism spectrum disorder (ASD). They use an Auto-Encoder (AE) to efficiently reduce dimensionality while retaining critical information by learning and compressing important characteristics from high-dimensional data. We treat the feature-extracted data using a BiGRU model for the classification task of predicting ASD. They provide a new optimization strategy, the Horse Herd Algorithm (HHA), and show that it outperforms other established optimizers, such SGD and Adam, in order to improve classification accuracy. The model’s performance is greatly enhanced by the HHA’s novel optimization technique, which more precisely refines weight modifications made during training. The proposed ASD and EEG dataset accuracy value is 99.5%, and 99.3 compared to the existing method the proposed has a high accuracy value.

Evaluation of asphalt anti-cracking performance of SBS polymer with SCB method and deep learning

In recent years, there have been unprecedented developments in artificial intelligence. Object detection, voice recognition, face recognition etc. are some of the artificial intelligence applications. In this study, an auxiliary method for the automatic detection of cracks, one of the main deterioration problems on highways, is proposed. The crack formation of hot mix asphalts is investigated with an image processing method modeled with Attention SegNet architecture. Styrene-butadiene-styrene (SBS), the most widely used additive in bitumen modification, was used at 2 %, 3 %, and 4 % ratios to modify 50/70 bitumen. Semi-circular asphalt specimens obtained with SBS modified bitumen were subjected to a semicircular bending (SCB) test and fracture performance was investigated. The effects of different temperature, notch size and additive on crack detection performance are evaluated. In the experimental study, maximum load, fracture energy, fracture toughness (KIC) values were obtained at low temperature, and resistance values against crack propagation were obtained by applying the J-integral method at intermediate temperature. The results demonstrated that with the addition of SBS, the fracture strength and maximum load values increased at each temperature value, with the 4 % SBS mixture offering the highest performance. Moreover, the image segmentation performed with SegNet provided high accuracy and precision values for cracks. It was observed that the accuracy values of the image processing methods decreased at low temperature, while at high temperature, higher accuracy values were obtained as the cracking rate.

Prestack reservoir prediction method in ray parameter domain based on wide azimuth ocean bottom node seismic data

AbstractWide azimuth seismic data play an important role in deep reservoir prediction. According to the Paleogene clastic rock reservoir prediction, the high‐density and wide azimuth 3D seismic data acquisition of ocean bottom nodes was first carried out in a Chinese offshore oilfield in 2019. After high precision amplitude‐preserving processing, we obtained the high‐quality wide azimuth gathers. However, the research on anisotropy and reservoir prediction using wide azimuth seismic data mainly focuses on carbonate and bedrock intervals, which is not suitable for clastic rock reservoir prediction. Therefore, this paper innovatively proposes a clastic rock reservoir prediction method, which studies prestack reservoir prediction in the ray parameter domain based on wide azimuth ocean bottom node seismic data. Based on the azimuth gathers, we can obtain elastic parameters through prestack amplitude versus offset inversion, which is used to characterize the reservoir, so it is significant to obtain high precision elastic parameters in order to get highly reliable reservoir prediction results. In this paper, we develop an amplitude versus offset inversion method based on the Bayesian theory in ray parameter domain, the output of which is density, P‐wave impedance and Vp/Vs. These elastic parameters have high precision, and density data are valuable input for reservoir characterization because they are sensitive to the lithology of clastic rock reservoir at different orientations. In ray parameter domain inversion, the ray path of seismic wave propagation is considered polyline, which is more consistent with the actual situation; thus, extracted amplitudes of P gathers used in inversion are more accurate. In addition, the reflection coefficient formula in ray parameter domain has higher precision when the incident angle is large. The inversion based on the Bayesian theory can improve the stability of the inversion. Test on the actual data shows that the result of ray parameter domain inversion with a Bayesian scheme is more accurate, stable and reliable. Based on the above high precision density inversion results, an innovative wide azimuth data reservoir prediction technology based on elliptical short‐axis fitting was proposed. The actual prediction of the deep reservoir in the Bohai oilfield shows that sand thickness fitting prediction results in the short axis can best match the actual drilling sandstone thickness. The coincidence rate is 86% and the short‐axis fitting results are more in agreement with geological laws. Theoretical research and practical applications have shown that this method is feasible and effective, with high prediction accuracy, computational efficiency and strong application value.

Title Changes in Plasma Levels of Selected Matrix Metalloproteinases (MMPs) Enzymes in Patients with Osgood-Schlatter Disease (OSD).

Background: Osgood-Schlatter disease (OSD) belongs to the group of sterile bone necrosis and mainly affects athletically active children. The pathogenesis of OSD is currently not fully understood, so the purpose of this study was to evaluate the concentrations of selected matrix metalloproteinases (MMPs)-MMP-2, MMP-3, MMP-7, MMP-9, MMP-10 and MMP-26 in patients diagnosed with OSD compared to patients with diseases other than sterile bone necrosis Methods: The study group included 140 patients with OSD, while the control group contained 100 patients with knee pain unrelated to sterile bone necrosis. The MMPs tested were determined by an enzyme-linked immunosorbent assay in plasma. Results: Patients with OSD had higher concentrations of MMP-2 and MMP-9 compared to the control group. The concentrations of MMP-7, MMP-10 and MMP-26 were lower in affected children. High values of diagnostic parameters-diagnostic accuracy (AC), sensitivity (SE), specificity (SP) and area under curve (AUC)-were obtained for MMP-7, MMP-9 and MMP-26. Conclusions: The collected results convince that MMP-7, MMP-9 and MMP-26 can be consider as a differential ancillary test between OSD and other knee pain and may be involved in the pathogenesis of this condition.

A Multi-Criteria Decision Making for Employee Selection Using SAW and Profile Matching

Multi-criteria decision making process has been one of the fastest growing areas during the last decades depending on the change in the business sector. Multi-criteria decision making is the most important branch of operation research by which people make complex decision daily life. The major steps in the decision- making approach are selecting the most preferred alternative for the decision-maker, ranking alternatives in order of importance for selection problems, and screening alternatives for the final decision. Employees are an important element of a company that determines its progress. Conventional (manual) recruitment methods are vulnerable to non-technical factors, such as frequent duplication or invalid data. In this study, simple additive weighting and profile matching are proposed to solve the employee selection problem. This study was conducted at the (UPT) Career Development and Entrepreneurship Universitas Brawijaya Malang using data collected from written test selection in 2019. The effectiveness of both methods was analyzed using confusion matrix. The SAW method provides an accuracy rate of 94.7%, a precision rate of 87.5%, Recall rate of 91.3% and F-measure rate of 89.4%. On the other hand, profile matching method obtained the Accuracy rate of 90.4%, Precision rate of 81.4%, Recall rate of 81.4% and F-measure rate of 81.4%. Thus, it can be concluded that both methods have high accuracy values accompanied by high precision values when used for the selection process. This system can also effectively reduce the bias rate of the same data very well, as can be seen from the high recall and F-measure rates.

RNN-Based Monthly Inflow Prediction for Dez Dam in Iran Considering the Effect of Wavelet Pre-Processing and Uncertainty Analysis

In recent years, deep learning (DL) methods, such as recurrent neural networks (RNN). have been used for streamflow prediction. In this study, the monthly inflow into the Dez Dam reservoir from 1955 to 2018 in southwestern Iran was simulated using various types of RNNs, including long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), gated recurrent unit (GRU), and stacked long short-term memory (Stacked LSTM). It was observed that considering flow discharge, temperature, and precipitation as inputs to the models yields the best results. Additionally, wavelet transform was employed to enhance the accuracy of the RNNs. Among the RNNs, the GRU model exhibited the best performance in simulating monthly streamflow without using wavelet transform, with RMSE, MAE, NSE, and R2 values of 0.061 m3/s, 0.038 m3/s, 0.556, and 0.642, respectively. Moreover, in the case of using wavelet transform, the Bi-LSTM model with db5 mother wavelet and decomposition level 5 was able to simulate the monthly streamflow with high accuracy, yielding RMSE, MAE, NSE, and R2 values of 0.014 m3/s, 0.008 m3/s, 0.9983, and 0.9981, respectively. Uncertainty analysis was conducted for the two mentioned superior models. To quantify the uncertainty, the concept of the 95 percent prediction uncertainty (95PPU) and the p-factor and r-factor criteria were utilized. For the GRU, the p-factor and r-factor values were 82% and 1.28, respectively. For the Bi-LSTM model, the p-factor and r-factor values were 94% and 1.06, respectively. The obtained p-factor and r-factor values for both models are within the acceptable and reliable range.

Enhancing alzheimer’s diagnosis through optimized brain lesion classification in MRI with attention-driven grid feature fusion

This paper explores cognitive interface technology, aiming to tackle current challenges and shed light on the prospects of brain-computer interfaces (BCIs). It provides a comprehensive examination of their transformative impact on medical technology and patient well-being. Specifically, this study contributes to addressing challenges in classifying brain lesion images arising from the complex nature of lesions and limitations of traditional deep learning approaches. It introduces advanced feature fusion models that leverage deep learning algorithms, including the African vulture optimization (AVO) algorithm. These models integrate informative features from multiple pre-trained networks and employ innovative fusion techniques, including the attention-driven grid feature fusion (ADGFF) model. The ADGFF model incorporates an attention mechanism based on the optimized weights obtained using AVO. The objective is to improve the overall accuracy by providing fine-grained control over different regions of interest in the input image through a grid-based technique. This grid-based technique divides the image into vertical and horizontal grids, simplifying the exemplar feature generation process without compromising performance. Experimental results demonstrate that the proposed feature fusion strategies consistently outperform individual pre-trained models in terms of accuracy, sensitivity, specificity, and F1-score. The optimized feature fusion strategies, particularly the GRU-ADGFF model, further enhance classification performance, outperforming CNN and RNN classifiers. The learning progress analysis shows convergence, indicating the effectiveness of the feature fusion strategies in capturing lesion patterns. AUC-ROC curves highlight the superior discriminatory capabilities of the ADGFF-AVO strategy. Five-fold cross-validation is employed to assess the performance of the proposed models, demonstrating their accuracy, and few other accuracy-based measures. The GRU-ADGFF model optimized with AVO consistently achieves high accuracy, sensitivity, and AUC values, demonstrating its effectiveness and generalization capability. The GRU-ADGFF model also outperforms the majority voting ensemble technique in terms of accuracy and discriminative ability. Additionally, execution time analysis reveals good scalability and resource utilization of the proposed models. The Friedman rank test confirms significant differences in classifier performance, with the GRU-ADGFF model emerging as the top-performing method across different feature fusion strategies and optimization algorithms.