ROC Value Research Articles

CDT1 is a Potential Therapeutic Target for the Progression of NAFLD to HCC and the Exacerbation of Cancer

Aims: This study aimed to identify potential therapeutic targets in the progression from non-alcoholic fatty liver disease (NAFLD) to hepatocellular carcinoma (HCC), with a focus on genes that could influence disease development and progression. Background: Hepatocellular carcinoma, significantly driven by non-alcoholic fatty liver disease, represents a major global health challenge due to late-stage diagnosis and limited treatment options. This study utilizes bioinformatics to analyze data from GEO and TCGA, aiming to uncover molecular biomarkers that bridge NAFLD to HCC. Through identifying critical genes and pathways, our research seeks to advance early detection and develop targeted therapies, potentially improving prognosis and personalizing treatment for NAFLD-HCC patients. Objective: Identify key genes that differ between NAFLD and HCC; Analyze these genes to understand their roles in disease progression; Validate the functions of these genes in NAFLD to HCC transition. Methods: Initially, we identified a set of genes differentially expressed in both NAFLD and HCC using second-generation sequencing data from the GEO and TCGA databases. We then employed a Cox proportional hazards model and a Lasso regression model, applying machine learning techniques to the large sample data from TCGA. This approach was used to screen for key disease-related genes, and an external dataset was utilized for model validation. Additionally, pseudo-temporal sequencing analysis of single-cell sequencing data was performed to further examine the variations in these genes in NAFLD and HCC. Results: The machine learning analysis identified IGSF3, CENPW, CDT1, and CDC6 as key genes. Furthermore, constructing a machine learning model for CDT1 revealed it to be the most critical gene, with model validation yielding an ROC value greater than 0.80. The single-cell sequencing data analysis confirmed significant variations in the four predicted key genes between the NAFLD and HCC groups. Conclusion: Our study underscores the pivotal role of CDT1 in the progression from NAFLD to HCC. This finding opens new avenues for early diagnosis and targeted therapy of HCC, highlighting CDT1 as a potential therapeutic target.

A Framework for Prediction of Type II Diabetes through Ensemble Stacking Model

In order to prevent long term complications of diabetes its early diagnosis is crucial. With Increasing advances in Artifical Intelligence (AI) and Machine Learning(ML) researchers are increasingly focusing on using them for early diagnosis of diseases.AI and ML has significant potential for early prediction of type 2 diabetes. In this article we have described a ML based framework for prediction of type 2 diabetes -Improved Ensemble Learning with Dimensionality Reduction Model (IELDR) and discussed its result. An IELDR algorithm is an Auto encoder-based feature extraction method with ensemble learning. The experiments were carried out using the LS_diabetes dataset. LS_diabetes dataset containing 374 records with 35 features related to lifestyle and stress. Accuracy, precision, specificity, sensitivity, f1 score, roc and Mathew correlation coefficient (MCC) were measured. After this results were tested and validated using Diabetes_2019 dataset and PIMA diabetes dataset. The IELDR model showed results in terms accuracy, precision, specificity, sensitivity, f1 score, roc and Mathew correlation coefficient (MCC) of 98.67%, 95.24%, 100%, 98.18%, 97.56%, 99.09% and 0.97 respectively. In comparison with PIMA diabetes dataset, LS_diabetes dataset showed an accuracy, precision, sensitivity, specificity, f1-score,roc and mcc value by 17.96%,13.15% 40.22%,5.59%,28.38%,22.09% and 0.4 respectively. The IELDR model achieved the best result on the LS_diabetes dataset showed an accuracy, sensitivity, roc and mcc value improved by 1.82%, 1.58%, 3.01%and 0.04 % compared to the Diabetes_2019 dataset .This proposed IELDR system predicts the risk of type 2 diabetes in a healthy person based on the person’s current lifestyle pattern. This system can be helpful for early prediction of type2 diabetes.

Uncovering early predictors of cerebral palsy through the application of machine learning: a case–control study

ObjectiveCerebral palsy (CP) is a group of neurological disorders with profound implications for children’s development. The identification of perinatal risk factors for CP may lead to improved preventive and therapeutic...

Association of serum alpha-fetoprotein (AFP) with indirect prognosis in gastric cancer

Aims: Alpha-fetoprotein (AFP) is a glycoprotein secreted from the yolk sac and hepatocytes during fetal life. Elevated levels in adults are considered markers for yolk sac tumors, hepatocellular carcinoma and gastric cancer. There is still no consensus on the definition of AFP-secreting gastric cancers, but AFP-secreting gastric cancers have been reported to have a worse prognosis. In this study, AFP levels in gastric cancers with high AFP were compared with pathology results and evaluated in terms of indirect prognosis. Methods: Patients with gastric cancer operated in the General Surgery Clinic of Ankara Bilkent City Hospital between 2019 and 2023 were retrospectively screened. Among the screened patients, those whose information could not be reached and those with incomplete information were excluded and 126 patients were included in the study. Serum AFP levels of these patients were compared with pathology results, stages and tumor sizes. Results: The mean age of the patients in our study was 66±11 years, 42 (33.6%) were female and 84 (66.7%) were male. Of the 126 patients analyzed in the study, 107 (84.9%) were AFP-negative and 19 (15.1%) were AFP-positive. There was no significant difference in AFP levels between all patients in terms of liver metastasis, peritoneal metastasis, disease stage, perineural invasion and tumor location. However, serum AFP levels were significantly higher in patients with lymphovascular invasion and lung metastases (p=0.042; p=0.024). In the analysis for serum AFP level in the presence of lymphovascular invasion, ROC value was 2.35 ng/ml, sensitivity 58.2%, specificity 55.9%, area under the curve (AUC) 0.618, 95% confidence interval (95% CI) 0.512-0.723 and p=0.028. Conclusion: Lymphovascular invasion is a poor prognostic factor in gastric cancer. In this study, a significant correlation was found between serum AFP value and lymphovascular invasion. In gastric cancers without AFP secretion, a serum AFP value above 2.35 ng/ml may be indirectly associated with poor prognosis because it predicts lymphovascular invasion.

#959 Developing and validating a risk score model for prediction of AKI in non-ICU hospitalized patients

Abstract Background and aim : Acute kidney injury (AKI) is an increasingly common and potentially catastrophic complication in hospitalized patients. Several risk score models have been designed for intensive care unit (ICU) patients. Aim in this work was to establish a new risk prediction score for AKI patients in general wards. Methods All hospitalized ward patients from the first of July 2019 to 31st of December 2019 who developed AKI 24 hours after admission were included. To create a new prediction score model of AKI in hospitalized patients we used the data collected from 107 patients who developed AKI. We used our prospective cohort to develop and validate this prediction score model of AKI in patients admitted to general wards. Results Only 107 patients of 10,243 (1%) developed AKI 24 hours after admission to the general wards. Mortality rate was 26.2%. A score model of 15 points, based on a combination of clinical and laboratory data, was developed for prediction of AKI. We demonstrated that a cutoff value ≥ 4 out of 15 was a predictor of Acute Kidney Injury in non-ICU hospitalized patients. The area under the receiver operating characteristic (AUC ROC) value of the score model was 0.950, 95% CI (confidence interval) 0.922–0.978 and the P-value <0.001 with sensitivity of 94.39 and specificity of 81.43. Applying the score model in another group of patients (validation group n = 122) showed that the AUC ROC value in the validation group was 0.826. Conclusion We developed and validated a new risk score model with a cutoff value ≥ 4 out of 15 for prediction of Acute Kidney Injury in non-ICU hospitalized patients with a good sensitivity and specificity. It will help the early prediction of Acute Kidney Injury in non-ICU patients.

Machine learning algorithm predicts urethral stricture following transurethral prostate resection

PurposeTo predict the post transurethral prostate resection(TURP) urethral stricture probability by applying different machine learning algorithms using the data obtained from preoperative blood parameters.MethodsA retrospective analysis of data from patients who underwent bipolar-TURP encompassing patient characteristics, preoperative routine blood test outcomes, and post-surgery uroflowmetry were used to develop and educate machine learning models. Various metrics, such as F1 score, model accuracy, negative predictive value, positive predictive value, sensitivity, specificity, Youden Index, ROC AUC value, and confidence interval for each model, were used to assess the predictive performance of machine learning models for urethral stricture development.ResultsA total of 109 patients’ data (55 patients without urethral stricture and 54 patients with urethral stricture) were included in the study after implementing strict inclusion and exclusion criteria. The preoperative Platelet Distribution Width, Mean Platelet Volume, Plateletcrit, Activated Partial Thromboplastin Time, and Prothrombin Time values were statistically meaningful between the two cohorts. After applying the data to the machine learning systems, the accuracy prediction scores for the diverse algorithms were as follows: decision trees (0.82), logistic regression (0.82), random forests (0.91), support vector machines (0.86), K-nearest neighbors (0.82), and naïve Bayes (0.77).ConclusionOur machine learning models’ accuracy in predicting the post-TURP urethral stricture probability has demonstrated significant success. Exploring prospective studies that integrate supplementary variables has the potential to enhance the precision and accuracy of machine learning models, consequently progressing their ability to predict post-TURP urethral stricture risk.

Enhancing infectious diseases early warning: A deep learning approach for influenza surveillance in China

ObjectiveThis study aimed to develop a universally applicable, feedback-informed Self-Excitation Attention Residual Network (SEAR) model. This model dynamically adapts to evolving disease trends and surveillance system changes, accommodating various scenarios. Thereby enhancing the effectiveness of early warning systems. MethodsSurveillance data on influenza-like illness (ILI) was collected from various regions including Northern China, Southern China, Beijing, and Yunnan. The reproduction number (Rt) was estimated to determine the threshold for issuing warnings. The Self-Excitation Attention Residual Network (SEAR) was devised employing deep learning algorithms and was trained, validated, and tested. The SEAR model's efficacy was assessed based on five metrics: accuracy rate, recall rate, F1 score, confusion matrix, and the receiver operating characteristic curve. ResultsWith an advance warning set at three days, the SEAR model outperformed five primary models − logistic regression, support vector machine, random forest, Extreme Gradient Boosting, and Long Short-Term Memory model − in all five evaluation metrics. Notably, the model's warning performance declined with an increase in the early warning value and the number of warning days, albeit maintaining a ROC value over 0.7 in all scenarios. ConclusionThe SEAR model demonstrated robust early warning performance for influenza in diverse Chinese regions with high accuracy and specificity. This novel model, augmenting traditional systems, supports widespread application for respiratory disease outbreak monitoring. Future evaluations could incorporate alternative indicators, with the model continuously updating through data feedback, thus enhancing its universal applicability. Ongoing optimization, using iterative feedback and expert judgment, heralds a transformative approach to surveillance-based early warning strategies.

Assessment of gully erosion susceptibility using four data-driven models AHP, FR, RF and XGBoosting machine learning algorithms

Gully erosion is a significant global threat to socioeconomic and environmental sustainability, making it a widespread natural hazard. Developing spatial models for gully erosion is crucial for local governance to effectively implement mitigation measures and promote regional development. This study applied two machine learning (ML) models, RF and XGB, alongside an AHP-based multi-criteria decision method and FR bivariate statistics, to assess gully erosion susceptibility (GES) in the Kangsabati River basin in eastern India's Chotonagpur plateau fringe. A GIS database was created, incorporating recorded gully erosion incidents and 20 conditioning variables, which were evaluated for multicollinearity. These variables served as predictive factors for assessing gully erosion presence in the study area. The models' performance was evaluated using metrics such as RMSE, MAE, specificity, sensitivity, and accuracy. The XGB model outperformed the others, achieving a predictive accuracy of 90.22%. The study found that approximately 6.56% of the Kangsabati catchment is highly susceptible to gully erosion, with 12.39% moderately susceptible and 81.05% not susceptible. The XGB model had the highest ROC value of 85.5 during testing, indicating its superiority over the FR (ROC ​= ​81.7), AHP (ROC ​= ​79.8), and RF (ROC ​= ​83.8) models. These findings highlight the XGB model's efficacy and potential for large-scale GES mapping.

Machine learning framework develops neutrophil extracellular traps model for clinical outcome and immunotherapy response in lung adenocarcinoma.

Neutrophil extracellular traps (NETs) are novel inflammatory cell death in neutrophils. Emerging studies demonstrated NETs contributed to cancer progression and metastases in multiple ways. This study intends to provide a prognostic NETs signature and therapeutic target for lung adenocarcinoma (LUAD) patients. Consensus cluster analysis performed by 38 reported NET-related genes in TCGA-LUAD cohorts. Then, WGCNA network was conducted to investigate characteristics genes in clusters. Seven machine learning algorithms were assessed for training of the model, the optimal model was picked by C-index and 1-, 3-, 5-year ROC value. Then, we constructed a NETs signature to predict the overall survival of LUAD patients. Moreover, multi-omics validation was performed based on NETs signature. Finally, we constructed stable knockdown critical gene LUAD cell lines to verify biological functions of Phospholipid Scramblase 1 (PLSCR1) in vitro and in vivo. Two NETs-related clusters were identified in LUAD patients. Among them, C2 cluster was provided as "hot" tumor phenotype and exhibited a better prognosis. Then, WGCNA network identified 643 characteristic genes in C2 cluster. Then, Coxboost algorithm proved its optimal performance and provided a prognostic NETs signature. Multi-omics revealed that NETs signature was involved in an immunosuppressive microenvironment and predicted immunotherapy efficacy. In vitro and in vivo experiments demonstrated that knockdown of PLSCR1 inhibited tumor growth and EMT ability. Besides, cocultural assay indicated that the knockdown of PLSCR1 impaired the ability of neutrophils to generate NETs. Finally, tissue microarray (TMA) for LUAD patients verified the prognostic value of PLSCR1 expression. In this study, we focus on emerging hot topic NETs in LUAD. We provide a prognostic NETs signature and identify PLSCR1 with multiple roles in LUAD. This work can contribute to risk stratification and screen novel therapeutic targets for LUAD patients.

Optimal timing of SARS-CoV-2 vaccination prior to cardiovascular surgery under cardiopulmonary bypass.

mRNA vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) became common. We investigated the optimal timing for inoculation against SARS-COV-2 in the candidates for cardiac surgery under cardiopulmonary bypass (CPB). In 100 patients with preoperative vaccination, who underwent CPB surgery between July 2021 and February 2022, the IgG against the receptor binding domain (RBD-IgG), with a threshold of >100 binding antibody unit (BAU)/mL for adequate immunity, was measured. The vaccines, including 87 BNT162b2 (Pfizer/BioNTech) and 13 mRNA-1273 (Moderna), were inoculated at 98.8 ± 59.4 days before surgery. The median RBD-IgG titers before surgery, 1 day after surgery, and 1 month after surgery were 166.8, 100.0, and 84.0 BAU/mL, respectively. The standby interval (SBI) from the vaccination to the surgery showed a significantly negative correlations with the RBD-IgG titer before the surgery (p < 0.001). A cut-off SBI for RBD-IgG >100 BAU/mL before surgery was <81 days with a sensitivity of 76%, specificity of 62%, and area under ROC value of 0.73 (p = 0.03). The patients with SBI <81 days (n = 48) had significantly higher RBD-IgG (>100 BAU/mL) than those with SBI ⩾81 days (n = 52) at all perioperative periods. Although 40% of the RBD-IgG titers reduce 1 day after CPB surgery, the patients who received the SARS-COV-2 vaccination within an 81-day window prior to the surgery maintained a desirable RBD-IgG level, even up to 1 month after surgery. It may be important to schedule the surgery no later than 81 days after the vaccination.

Constructing an innovative system of management and education in colleges and universities based on artificial intelligence technology

Abstract At this stage, the information literacy of teachers and students is constantly improving, and the “digital campus” represented by artificial intelligence is landing in various regions and schools. This paper accurately constructs a college management and education innovation system based on artificial intelligence technology, including institutional mechanisms, a one-stop service platform, and an early warning mechanism. The early warning mechanism has an early warning model constructed. The construction of an academic early warning model for students is based on multivariate linear regression. A model for early warning of student activity safety was constructed using multiclassification logistic regression. The impact of fusing the two models was evaluated, as well as the impact of practical application. The ROC value of the first-level warning is 0.9533, and the ROC value of the second-level warning is 0.9428, and both values are close to 1, which verifies that the model can be practically applied. By analyzing the F1-score value through practical application, the average value of the F1-score throughout the semester was as high as 84.68%, and the number of warnings of the early warning model was higher than the number of feedback from teachers. It has been verified that the early warning model in this study can effectively and efficiently manage student activities and academic anomalies in colleges and universities.

Heart Disease Diagnosis Using Decision Trees with Feature Selection Method

The advancement in treating medical data grows significantly daily. An accurate data classification model can help determine patient disease and diagnose disease severity in the medical domain, thus easing doctors' treatment burdens. Nonetheless, medical data analysis presents challenges due to uncertainty, the correlations between various measurements, and the high dimensionality of the data. These challenges burden statistical classification models. Machine Learning (ML) and data mining approaches have proven effective in recent years in gaining a deeper understanding of the importance of these aspects. This research adopts a well-known supervised learning classification model named a Decision Tree (DT). DT is a typical tree structure consisting of a central node, connected branches, and internal and terminal nodes. In each node, we have a decision to be made, such as in a rule-based system. This type of model helps researchers and physicians better diagnose a disease. To reduce the complexity of the proposed DT, we explored using the Feature Selection (FS) method to design a simpler diagnosis model with fewer factors. This concept will help reduce the data collection stage. A comparative analysis has been conducted between the developed DT and other various ML models, such as Logistic Regression (LR), Support Vector Machine (SVM), and Gaussian Naive Bayes (GNB), to demonstrate the effectiveness of the developed model. The results of the DT model establish a notable accuracy of 93.78\% and an ROC value of 0.94, which beats other compared algorithms. The developed DT model provided promising results and can help diagnose heart disease

Classification of Congestive Heart Failure Using Artificial Neural Network Based on Higher-Order Moments Detrended Fluctuation Analysis of Heart Rate Variability

Coronary heart disease or cardiovascular disease is a kind of heart disease that affects the heart and all the blood vessels in the body caused by a build-up of plaque in a person's arteries and can result in a stroke or heart attack. According to the Indonesian Ministry of Health, (Depkes RI) coronary heart disease is the main and first cause of all deaths by heart disease, which account for 26.4% among all other causes. Considering the severity of the condition, it is necessary to detect this kind of heart disease to reduce the number of deaths from heart disease. Heart disease detection can be performed by checking the Heart Rate Variability (HRV) signal. HRV assessment is carried out by analyzing short-term and long-term Electrocardiogram (ECG) records. To analyze HRV signals, a Higher-Order Moments Detrended Fluctuation Analysis feature extraction method is proposed to see the non-linear structure of HRV data. The input for Higher-Order Moments Detrended Fluctuation Analysis is an ECG signal which has been converted into an HRV signal. The output of the feature extraction shows that the value of kurtosis-based fluctuation function has a statistical significance in order to differentiate the HRV of heart disease patients and that of normal subjects. These values are then used as the input for classification using an Artificial Neural Network. The output of the classification is the classification of patients with heart disease and normal subjects. The results of the Higher-Order Moments Detrended Fluctuation Analysis feature extraction classification yield the best accuracy of 71.43% with a ROC value of 0.774 which can be categorized as a pretty good classification.

Computational 3D Modeling-Based Identification of Inhibitors Targeting Cysteine Covalent Bond Catalysts for JAK3 and CYP3A4 Enzymes in the Treatment of Rheumatoid Arthritis.

This work aimed to find new inhibitors of the CYP3A4 and JAK3 enzymes, which are significant players in autoimmune diseases such as rheumatoid arthritis. Advanced computer-aided drug design techniques, such as pharmacophore and 3D-QSAR modeling, were used. Two strong 3D-QSAR models were created, and their predictive power was validated by the strong correlation (R2 values > 80%) between the predicted and experimental activity. With an ROC value of 0.9, a pharmacophore model grounded in the DHRRR hypothesis likewise demonstrated strong predictive ability. Eight possible inhibitors were found, and six new inhibitors were designed in silico using these computational models. The pharmacokinetic and safety characteristics of these candidates were thoroughly assessed. The possible interactions between the inhibitors and the target enzymes were made clear via molecular docking. Furthermore, MM/GBSA computations and molecular dynamics simulations offered insightful information about the stability of the binding between inhibitors and CYP3A4 or JAK3. Through the integration of various computational approaches, this study successfully identified potential inhibitor candidates for additional investigation and efficiently screened compounds. The findings contribute to our knowledge of enzyme-inhibitor interactions and may help us create more effective treatments for autoimmune conditions like rheumatoid arthritis.

QSAR-driven screening uncovers and designs novel pyrimidine-4,6-diamine derivatives as potent JAK3 inhibitors

This study presents a robust and integrated methodology that harnesses a range of computational techniques to facilitate the design and prediction of new inhibitors targeting the JAK3/STAT pathway. This methodology encompasses several strategies, including QSAR analysis, pharmacophore modeling, ADMET prediction, covalent docking, molecular dynamics (MD) simulations, and the calculation of binding free energies (MM/GBSA). An efficacious QSAR model was meticulously crafted through the employment of multiple linear regression (MLR). The initial MLR model underwent further refinement employing an artificial neural network (ANN) methodology aimed at minimizing predictive errors. Notably, both MLR and ANN exhibited commendable performance, showcasing R2 values of 0.89 and 0.95, respectively. The model's precision was assessed via leave-one-out cross-validation (CV) yielding a Q2 value of 0.65, supplemented by rigorous Y-randomization. , The pharmacophore model effectively differentiated between active and inactive drugs, identifying potential JAK3 inhibitors, and demonstrated validity with an ROC value of 0.86. The newly discovered and designed inhibitors exhibited high inhibitory potency, ranging from 6 to 8, as accurately predicted by the QSAR models. Comparative analysis with FDA-approved Tofacitinib revealed that the new compounds exhibited promising ADMET properties and strong covalent docking (CovDock) interactions. The stability of the new discovered and designed inhibitors within the JAK3 binding site was confirmed through 500 ns MD simulations, while MM/GBSA calculations supported their binding affinity. Additionally, a retrosynthetic study was conducted to facilitate the synthesis of these potential JAK3/STAT inhibitors. The overall integrated approach demonstrates the feasibility of designing novel JAK3/STAT inhibitors with robust efficacy and excellent ADMET characteristics that surpass Tofacitinib by a significant margin.Communicated by Ramaswamy H. Sarma

Smart nano generation of transgenic algae expressing white spot syndrome virus in shrimps for inner ear-oral infection treatments using the spotted hyena optimizer (SHO)-Long short-term memory algorithm

Nanotechnology offers a promising avenue to amplify the effectiveness and precision of using transgenic algae in managing WSSV in shrimp by possibly crafting nano-carriers for targeted therapeutic agent delivery or modifying algae cells at a molecular level. Leveraging the capabilities of nano-scale interventions, this study could explore innovative means to manipulate cellular processes, control biological interactions, and enhance treatment efficacy while minimizing undesirable impacts in aquatic environments. The White Spot Syndrome Virus (WSSV) is a double-stranded DNA virus with a tail and rod form that belongs to theNimaviridaefamily. There is no workable way to manage this illness at the moment. This research proposes a new model based on the Long Short-Term Memory (LSTM) and Spotted Hyena Optimizer (SHO) method to control the inner ear-oral infection, utilizing transgenic algae (Chlamydomonas reinhardtii). It is pretty tricky to modify the weight matrix in LSTM. The output will be more accurate if the weight of the neurons is exact. Histological examinations and nested polymerase chain reaction (PCR) testing were performed on the challenged shrimp every 4 h to assess the degree of white spot disease. The SHO-LSTM has shown the highest accuracy and Roc value (98.12% and 0.93, respectively) and the lowest error values (MSE = 0.182 and MAE = 0.48). The hybrid optimized model improves the overall inner ear-oral linked neurological diseases detection ratio. Additionally, with the slightest technical complexity, it effectively controls the forecast factors required to anticipate the ENT. Algal cells were found to be particularly well-suited for inner ear-oral infections, and shrimps fed a transgenic line had the best survival ratio in WSSV infection studies, with 87% of the shrimp surviving. This shows that using this line would effectively stop the spread of WSSV in shrimp populations.

Speech Emotion Recognition (SER) dengan Metode Bidirectional LSTM

Emotions are a part of humans as a form of response to experienced events. Emotion analysis or known as speech emotion recognition (SER) is a field many researchers are interested in because voice recognition systems can assist in criminal investigations, monitoring, and detection of potentially dangerous events, and assisting the health care system. Therefore, this study proposes the detection of SER using the Bidirectional Long short-term memory (Bi-LSTM) model approach. The dataset used was scraped on the YouTube platform. The dataset is manually labeled then feature extraction is performed using the Mel Frequency Cepstral Coefficients (MFCC). The experiment using the Bi-LSTM method has an AUC ROC value of 0.97 and an f1-score value of 0.878. Based on these results, it can be concluded that the performance of the proposed method succeeded in predicting SER better than other comparison methods. This model also proved to be more precise in classifying human voices based on four types of emotions, namely happy, sad, angry, and neutral.

Identification and optimization of TDP1 inhibitors from anthraquinone and chalcone derivatives: consensus scoring virtual screening and molecular simulations

Virtual screening aims to identify and rank compounds with drug/lead-like properties based on their affinity for the protein target. We developed a methodology that integrates structure- and ligand-based screening approaches to enhance hit rates against the TDP1 protein within a database of anthraquinone and chalcone derivatives, followed by evaluation of prioritized compounds through molecular simulations. This technique is particularly useful for training set imbalances. Four screening methods were used: QSAR, pharmacophore, shape similarity, and docking. Each method was individually trained to score compounds, and the scores were fused to create parallel Z-score fusion. The QSAR models exhibited satisfactory R2 values (0.84 to 0.75), whereas the pharmacophoric and shape similarity models demonstrated excellent performance (ROC:0.82–0.88). Docking enrichment analysis identified 6N0D as the optimal TDP1 crystal structure (ROC = 0.73). Remarkably, the consensus scoring method surpassed other screening methods, achieving the highest ROC value of 0.98. Docking screening prioritized compounds with binding modes resembling the co-crystallized ligands, whereas MMGBSA, consensus, and docking produced dynamic simulations that were as stable as the co-crystallized ligands. Additionally, the QSAR-selected compounds exhibited binding modes similar to those of commercially available TDP1 inhibitors. In this study, a strong correlation was found between the inhibitory concentrations and binding energy values of commercialized TDP1 inhibitors, indicating that the top-ranked compounds are expected to have potent inhibitory effects in the nano-/micromolar range. The results of this study establish that consensus scoring can be used as an adaptable mainstay virtual screening methodology, pending subsequent experimental validation for affirmation. Communicated by Ramaswamy H. Sarma

Exploratory Data Analysis for Textile Defect Detection

The capacity to recognize anomalies in real-world visual data is essential for many computer vision uses. New approaches and ideas in unsupervised defective garments identification require data for training and evaluation. Understanding the constraints of the currently employed approach of human inspection is crucial for improving clothing quality. Uses for digital image processing in the textile sector are suggested. This method proposes a novel quantitative measuring strategy by fusing digital image processing with the Lab view platform. As this study progresses, it becomes clear that the FLDA yields the best results, with 95% accuracy, while the Hoeffiding Tree yields the lowest results, with 60% accuracy. When compared to other models, the FLDA's precision of 0.96 is the best you'll find, while the Hoeffiding Tree's is the lowest at 0.62. The FLDA provides the best result, with a recall value of 0.95, while the Hoeffiding Tree shows the lowest result, with a recall value of 0.60. The FLDA yields the best results (0.90 kappa value), whereas the Hoeffiding Tree yields the worst (0.20 kappa value).The FLDA exhibits the best results, with an F-Measure value of 0.95, while the Hoeffiding Tree displays the lowest results, with an F-Measure value of 0.58. The FLDA provides the best results, with an MCC value of 0.91, while the Hoeffiding Tree displays the worst results, with an MCC value of 0.22. The FLDA yields the best results (0.98 ROC value), whereas the Decision Table produces the worst results (0.69 ROC value). The best prediction accuracy is shown by the FLDA, at 0.98 of the PRC value, while the worst is shown by the Decision Table, at 0.67. The MAE is lowest (0.07) for the FLDA and highest (0.39) for the Hoeffiding Tree. The MAE deviation of the Bayes Net is 0.19. The best result is shown by the FLDA, with an RMSE of 0.22, while the largest RMSE deviation is found in the Hoeffiding Tree, at 0.62. The RMSEdeviation for Bayes Net is 0.41. The finest RAE is shown by the FLDA, at 13.39%, while the largest RAE deviation is 78.28% for the Hoeffiding Tree. The Bayes Net explains 38.74% of the variation in RAE. The best result is shown by the FLDA, with an RRSE of 44.36%; the largest RRSE variation is shown by the Hoeffiding Tree, with 123.99%. When compared to other models, the IBK's preparation time of 0 seconds is by far the shortest. While the Bayes Net completes its task in 0.03 seconds, FLDA can take up to 0.17 seconds. The FLDA model is found to have superior performance in this study.

Prediksi Kemampuan Pembayaran Klien Home Credit Menggunakan Model Random Forest, Decision Tree, Dan Logistic Regression

Home Credit is a global financial company that provides consumer loan services. The purpose of this research is to predict the ability of clients to pay in order to make it easier for companies to provide loans or not. Not being careful in analyzing lending will cause credit risk. So to reduce these risks, the company needs an analysis to predict the client's repayment ability to determine whether to pay or not as a reference for the company in providing credit loans. By using the previous member criteria data, predictions of the smoothness of payments can be made using data mining. The data mining techniques used are Random Forest Classifier, Decision Tree Classifier, and Logistic Regression Classifier. The models used are Random Forest, Decision Tree, Logistic Regression, which determine the likelihood or opportunity based on the data of previous members, and the results. The criteria used consist of the ten best features selected based on the results of best feature importance. The evaluation results of the random forest model are able to predict the ability to pay home credit clients with a high level of test accuracy score of 0.9967, ROC value of 0.9967, recall value of 1.00 compared to the other two models.