Multiple Classification Models Research Articles

A deep learning based multi-model approach for predicting drug-like chemical compound’s toxicity

Ensuring the safety and efficacy of chemical compounds is crucial in small-molecule drug development. In the later stages of drug development, toxic compounds pose a significant challenge, losing valuable resources and time. Early and accurate prediction of compound toxicity using deep learning models offers a promising solution to mitigate these risks during drug discovery. In this study, we present the development of several deep-learning models aimed at evaluating different types of compound toxicity, including acute toxicity, carcinogenicity, hERG_cardiotoxicity (the human ether-a-go-go related gene caused cardiotoxicity), hepatotoxicity, and mutagenicity. To address the inherent variations in data size, label type, and distribution across different types of toxicity, we employed diverse training strategies. Our first approach involved utilizing a graph convolutional network (GCN) regression model to predict acute toxicity, which achieved notable performance with Pearson R 0.76, 0.74, and 0.65 for intraperitoneal, intravenous, and oral administration routes, respectively. Furthermore, we trained multiple GCN binary classification models, each tailored to a specific type of toxicity. These models exhibited high area under the curve (AUC) scores, with an impressive AUC of 0.69, 0.77, 0.88, and 0.79 for predicting carcinogenicity, hERG_cardiotoxicity, mutagenicity, and hepatotoxicity, respectively. Additionally, we have used the approved drug dataset to determine the appropriate threshold value for the prediction score in model usage. We integrated these models into a virtual screening pipeline to assess their effectiveness in identifying potential low-toxicity drug candidates. Our findings indicate that this deep learning approach has the potential to significantly reduce the cost and risk associated with drug development by expediting the selection of compounds with low toxicity profiles. Therefore, the models developed in this study hold promise as critical tools for early drug candidate screening and selection.

A novel space–spectrum array tile probability random-forest model enhances LULC mapping accuracy on Google Earth Engine: An experiment in Ordos, China

The rapid renewal of land use and land cover (LULC) maps using remote-sensing technologies constitutes a sine qua non for judicious land resource management at both regional and national scales. Existing research conducted on the Google Earth Engine (GEE) platform has overwhelmingly focused on pixel-based LULC classification techniques, often neglecting the role of spatial context via neighbouring valuable pixel information. Remarkably, little attention has been paid to the amalgamation of 3 × 3 neighbouring pixels into a three-dimensional space–spectrum array that can emulate the functionalities of object-based image analysis. In this study, we developed a novel integrated model consisting of a space–spectrum array (SSA) model based on 3 × 3 neighbouring pixels, a tile model based on random forest, and a multiple probabilistic classification model (SSA-TPRF) on the GEE platform to generate a LULC map with high overall accuracy (OA) for Ordos in 2020. Three bimonthly median value images were synthesised and feature collections, including spectral bands and vegetation indices, were constructed. Five experimental groups (EXP1–EXP5) were used to assess the different model combinations. Subsequent validation procedures employed abundant reference samples and compared the results with those of the three extant LULC mapping products. The results showed that EXP2, which was grounded in the tile-based model, yielded an OA of 87.53%, surpassing that of EXP1 (84.99%), which employed a traditional overall model. Furthermore, EXP3, which integrated the multiple probabilistic classification model with the traditional overall model, exhibited an OA of 85.19%, exceeding that of EXP1. A comparison of the five experimental groups using the four regional spatial subtlety features revealed that the EXP5, employing the SSA-TPRF model, successfully decreased the salt and pepper noise. The OA of six tile sizes ranging from 10 km to 100 km were compared, and the highest OA (88.35%) was achieved at a tile size of 25 km. The resultant LULC map in Ordos, derived from the SSA-TPRF model, showed superior OA compared with the extant LULC products. This study thus contributes to a versatile and scalable model within the GEE framework, offering avenues for facile adaptation and recurrent application across disparate geographical locations and temporal settings. The adaptability of this model is particularly advantageous for developing nations and regions typified by diverse landscapes, thereby catalysing the iterative updating of LULC maps through advanced remote-sensing paradigms.

Heterogeneous effects of hospital competition on inpatient quality: an analysis of five common diseases in China

BackgroundMany countries has introduced pro-competition policies in the delivery of healthcare to improve medical quality, including China. With the increasing intensity of competition in China's healthcare market, there are rising concerns among policymakers about the impact of hospital competition on quality. This study investigated heterogeneous effects of hospital competition on inpatient quality.MethodsWe analyzed the inpatient discharge dataset and selected chronic obstructive pulmonary disease (COPD), ischemic stroke, pneumonia, hemorrhagic stroke, and acute myocardial infarction (AMI) as representative diseases. A total of 561,429 patients in Sichuan Province in 2017 and 2019 were included. The outcomes of interest were in-hospital mortality and 30-day unplanned readmissions. The Herfindahl–Hirschman Index was calculated using predicted patient flows to measure hospital competition. To address the spatial correlations of hospitals and the structure of the dataset, the multiple membership multiple classification model was employed for analysis.ResultsAmid intensifying competition in the hospital market, our study discerned no marked statistical variance in the risk of inpatient quality across most diseases examined. Amplified competition exhibited a positive correlation with heightened in-hospital mortality for both COPD and pneumonia patients. Elevated competition escalated the risk of 30-day unplanned readmissions for COPD patients, while inversely affecting the risk for AMI patients.ConclusionsThere is the heterogeneous impact of hospital competition on quality across various diseases in China. Policymakers who intend to leverage hospital competition as a tool to enhance healthcare quality must be cognizant of the possible influences of it.

Industrial Fault Detection Employing Meta Ensemble Model Based on Contact Sensor Ultrasonic Signal.

Ultrasonic diagnostics is the earliest way to predict industrial faults. Usually, a contact microphone is employed for detection, but the recording will be contaminated with noise. In this paper, a dataset that contains 10 main faults of pipelines and motors is analyzed from which 30 different features in the time and frequency domains are extracted. Afterward, for dimensionality reduction, principal component analysis (PCA), linear discriminant analysis (LDA), and t-distributed stochastic neighbor embedding (t-SNE) are performed. In the subsequent phase, recursive feature elimination (RFE) is employed as a strategic method to analyze and select the most relevant features for the classifiers. Next, predictive models consisting of k-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT), Gaussian Naive Bayes (GNB), and Support Vector Machine (SVM) are employed. Then, in order to solve the classification problem, a stacking classifier based on a meta-classifier which combines multiple classification models is introduced. Furthermore, the k-fold cross-validation technique is employed to assess the effectiveness of the model in handling new data for the evaluation of experimental results in ultrasonic fault detection. With the proposed method, the accuracy is around 5% higher over five cross folds with the least amount of variation. The timing evaluation of the meta model on the 64 MHz Cortex M4 microcontroller unit (MCU) revealed an execution time of 11 ms, indicating it could be a promising solution for real-time monitoring.

Efficient Email Spam Classification with N-gram Features and Ensemble Learning

In this paper, we present an innovative approach to enhancing email spam classification using N-gram features, TF-IDF weighting, SMOTE oversampling, and ensemble learning techniques such as Decision Trees, Random Forests, and Ensemble Extra Trees. Our methodology involves preprocessing the dataset to extract N-gram features, applying TF-IDF weighting to highlight important terms, and addressing class imbalance through SMOTE. We then train and evaluate multiple classification models and find that the Ensemble Extra Trees algorithm outperforms others in terms of accuracy, precision, recall, and F1-score. Our experiments on benchmark datasets confirm the efficacy of our approach, showcasing significant improvements in spam detection accuracy and highlighting the potential of ensemble learning for email spam classification. This research contributes to the advancement of spam filtering technologies, providing a robust and efficient solution for accurately identifying and categorizing spam emails.

Research on Sound Source Localization of Multiple Fixed Targets Based on Machine Learning and Distributed Arrays

This paper studies a sound source localization method of multiple fixed targets based on machine learning and distributed arrays. In an outdoor open field, a three-line array was applied to collect array data and calculate latency characteristics. Then multiple classification models were established and trained. Finally, the locations of the sound source points were predicted by those models, in which the support vector machine (SVM), the nearest node (KNN), and the naive Bayesian model achieved 100% localization accuracy. Compared to the conventional method, this method has three significant advantages: First, it does not rely on the microphone channel order and does not need to be calibrated in advance, which simplifies the localization process; Second, it can fulfill high accuracy requirements, especially suitable for the scene of multiple fixed targets; Third, it has the advantage of incremental learning, as the times of localization rises, the training set is continuously enriched and the localization results become more precise.

Prior-guided individualized thalamic parcellation based on local diffusion characteristics.

Comprising numerous subnuclei, the thalamus intricately interconnects the cortex and subcortex, orchestrating various facets of brain functions. Extracting personalized parcellation patterns for these subnuclei is crucial, as different thalamic nuclei play varying roles in cognition and serve as therapeutic targets for neuromodulation. However, accurately delineating the thalamic nuclei boundary at the individual level is challenging due to intersubject variability. In this study, we proposed a prior-guided parcellation (PG-par) method to achieve robust individualized thalamic parcellation based on a central-boundary prior. We first constructed probabilistic atlas of thalamic nuclei using high-quality diffusion MRI datasets based on the local diffusion characteristics. Subsequently, high-probability voxels in the probabilistic atlas were utilized as prior guidance to train unique multiple classification models for each subject based on a multilayer perceptron. Finally, we employed the trained model to predict the parcellation labels for thalamic voxels and construct individualized thalamic parcellation. Through a test-retest assessment, the proposed prior-guided individualized thalamic parcellation exhibited excellent reproducibility and the capacity to detect individual variability. Compared with group atlas registration and individual clustering parcellation, the proposed PG-par demonstrated superior parcellation performance under different scanning protocols and clinic settings. Furthermore, the prior-guided individualized parcellation exhibited better correspondence with the histological staining atlas. The proposed prior-guided individualized thalamic parcellation method contributes to the personalized modeling of brain parcellation.

Identifying failure types in cyber-physical water distribution networks using machine learning models

Abstract Water cyber-physical systems (CPSs) have experienced anomalies from cyber-physical attacks as well as conventional physical and operational failures (e.g., pipe leaks/bursts). In this regard, rapidly distinguishing and identifying a facing failure event from other possible failure events is necessary to take rapid emergency and recovery actions and, in turn, strengthen system's resilience. This paper investigated the performance of machine learning classification models – support vector machine (SVM), random forest (RF), and artificial neural networks (ANNs) – to differentiate and identify failure events that can occur in a water distribution network (WDN). Datasets for model features related to tank water levels, nodal pressure, and water flow of pumps and valves were produced using hydraulic model simulation (WNTR and epanetCPA tools) for C-Town WDN under pipe leaks/bursts, cyber-attacks, and physical attacks. The evaluation of accuracy, precision, recall, and F1-score for the three models in failure type identification showed the variation of their performances depending on the specific failure types and data noise levels. Based on the findings, this study discussed insights into building a framework consisting of multiple classification models, rather than relying on a single best-performing model, for the reliable classification and identification of failure types in WDNs.

Utilizing nullomers in cell-free RNA for early cancer detection.

Early detection of cancer can significantly improve patient outcomes; however, sensitive and highly specific biomarkers for cancer detection are currently missing. Nullomers are the shortest sequences that are absent from the human genome but can emerge due to somatic mutations in cancer. We examine over 10,000 whole exome sequencing matched tumor-normal samples to characterize nullomer emergence across exonic regions of the genome. We also identify nullomer emerging mutational hotspots within tumor genes. Finally, we provide evidence for the identification of nullomers in cell-free RNA from peripheral blood samples, enabling detection of multiple tumor types. We show multiple tumor classification models with an AUC greater than 0.9, including a hepatocellular carcinoma classifier with an AUC greater than 0.99.

Individual- vs. Multiple-Objective Strategies for Targeted Sentiment Analysis in Finances Using the Spanish MTSA 2023 Corpus

Multitarget sentiment analysis extracts the subjective polarity of text from multiple targets simultaneously in a given context. This approach is useful in finance, where opinions about different entities affect the target differently. Examples of possible targets are other companies and society. However, typical multitarget solutions are resource-intensive due to the need to deploy multiple classification models for each target. An alternative to this is the use of multiobjective training approaches, where a single model is capable of handling multiple targets. In this work, we propose the Spanish MTSACorpus 2023, a novel corpus for multitarget sentiment analysis in finance, and we evaluate its reliability with several large language models for multiobjective training. To this end, we compare three design approaches: (i) a Main Economic Target (MET) detection model based on token classification plus a multiclass classification model for sentiment analysis for each target; (ii) a MET detection model based on token classification but replacing the sentiment analysis models with a multilabel classification model; and (iii) using seq2seq-type models, such as mBART and mT5, to return a response sequence containing the MET and the sentiments of different targets. Based on the computational resources required and the performance obtained, we consider the fine-tuned mBART to be the best approach, with a mean F1 of 80.300%.

‘Do not parade your ignorance’: Negation as a power tool of toxic geek masculinity

Catering to a primarily male user population, Reddit is often said to be a breeding ground for toxic language use, which has led to the development of multiple computational classification models for the automated identification of toxicity in online conversations. Since negation can play a key role in cyberbullying, functioning as a popular stance marker in the dialogical exchange of dominating views in the digital sphere, it should not be ignored. Likewise, its use as a rhetorical strategy deserves more linguistic attention. Described as Engagement resources of disclaim in Appraisal theory, negated constructions may be employed by Reddit users to disalign themselves from divergent perspectives through emotion-driven ad hominem attacks, not only resulting in a potentially detrimental impact on the users’ mental health, but also perpetuating toxic power relationships. Therefore, this paper examines the use of negation, in combination with various other interpersonal resources, within the Redditors’ dialogical interactions, to explore its possible motivations and role in shaping the online discourse. A computer-assisted appraisal analysis was conducted on 2637 most upvoted comments and replies (49,866 words), extracted from the subreddit of r/AustralianPolitics between 11–14 April and 7–10 May 2022, during the period leading up to the so-called federal climate change election in Australia. The study shows evidence of a seemingly dominant negation pattern characterised by negative judgement and its findings tentatively suggest that some of the male Australian users may use negation as a discursive strategy driven by ideology and emotion. This raises the possibility that the language use encountered on r/AustralianPolitics may be influenced by covert toxic geek masculinity.

A Few-Shot Semi-Supervised Learning Method for Remote Sensing Image Scene Classification

Few-shot scene classification methods aim to obtain classification discriminative ability from few labeled samples and has recently seen substantial advancements. However, the current few-shot learning approaches still suffer from overfitting due to the scarcity of labeled samples. To this end, a few-shot semi-supervised method is proposed to address this issue. Specifically, semi-supervised learning method is used to increase target domain samples; then we train multiple classification models using the augmented samples. Finally, we perform decision fusion of the results obtained from the multiple models to accomplish the image classification task. According to the experiments conducted on two real few-shot remote sensing scene datasets, our proposed method achieves significantly higher accuracy (approximately 1.70% to 4.33%) compared to existing counterparts.

Predictive modeling for technology convergence: A patent data-driven approach through technology topic networks

Anticipating technology convergence is crucial for driving innovation and gaining a competitive advantage. Developing a method for forecasting the convergence of previously unrelated technology fields is necessary. Previous studies explored technology convergence using patents but focused on technology classes. However, these prior studies lacked clear insights because they relied on ambiguous technology classes. Therefore, this study seeks to define technology topics with precise technological meanings and develop model predictions using machine learning through technology topic networks to anticipate technology convergence potential. Specifically, it explores the use of text mining techniques to gain insights into technology topics, highlighting best practices and our research findings. This study constructs a network of technology topics derived from patent documents and maps their interactions using a topic network approach. Technology topic networks are constructed through dynamic topic modeling, and features extracted from these networks are used as input for training multiple classification models. By incorporating new link results and leveraging technological influence, the proposed approach can accurately anticipate technology convergence. The results of this research can have significant implications for firms seeking to gain a competitive advantage through innovation and technology convergence and contribute to the establishment of a next-generation R&D planning system.

Improving diabetes disease patients classification using stacking ensemble method with PIMA and local healthcare data

Diabetes mellitus, a chronic metabolic disorder, continues to be a major public health issue around the world. It is estimated that one in every two diabetics is undiagnosed. Early diagnosis and management of diabetes can also prevent or delay the onset of complications. With the help of a variety of machine learning and deep learning models, stacking algorithms, and other techniques, our study's goal is to detect diseases early. In this study, we propose two stacking-based models for diabetes disease classification using a combination of the PIMA Indian diabetes dataset, simulated data, and additional data collected from a local healthcare facility. We use both the classical and deep neural network stacking ensemble methods to combine the predictions of multiple classification models and improve classification accuracy and robustness. In the evaluation protocol, we used both the train-test and cross-validation (CV) techniques to validate our proposed model. The highest accuracy is obtained by stacking ensemble with three NN architectures, resulting in an accuracy of 95.50 %, precision of 94 %, recall of 97 %, and f1-score of 96 % using 5-fold CV on simulation study. The stacked accuracy obtained from ML algorithms for the Pima Indian Diabetes dataset is 75.03 % using the train-test split protocol, while the accuracy obtained from the CV protocol is 77.10 % on the stacked model. The range of performance scores that outperformed the CV protocol 2.23 %–12 %. Our proposed method achieves a high accuracy range from 92 % to 95 %, precision, recall, and F1-score ranges from 88 % to 96 % using classical and deep neural network (NN)-based stacking method on the primary dataset. The proposed dataset and ensemble method could be useful in the early detection and treatment of diabetes, as well as in the advancement of machine learning and data analysis techniques in the healthcare industry.

Automated Machine Learning Analysis of Patients With Chronic Skin Disease Using a Medical Smartphone App: Retrospective Study.

Rapid digitalization in health care has led to the adoption of digital technologies; however, limited trust in internet-based health decisions and the need for technical personnel hinder the use of smartphones and machine learning applications. To address this, automated machine learning (AutoML) is a promising tool that can empower health care professionals to enhance the effectiveness of mobile health apps. We used AutoML to analyze data from clinical studies involving patients with chronic hand and/or foot eczema or psoriasis vulgaris who used a smartphone monitoring app. The analysis focused on itching, pain, Dermatology Life Quality Index (DLQI) development, and app use. After extensive data set preparation, which consisted of combining 3 primary data sets by extracting common features and by computing new features, a new pseudonymized secondary data set with a total of 368 patients was created. Next, multiple machine learning classification models were built during AutoML processing, with the most accurate models ultimately selected for further data set analysis. Itching development for 6 months was accurately modeled using the light gradient boosted trees classifier model (log loss: 0.9302 for validation, 1.0193 for cross-validation, and 0.9167 for holdout). Pain development for 6 months was assessed using the random forest classifier model (log loss: 1.1799 for validation, 1.1561 for cross-validation, and 1.0976 for holdout). Then, the random forest classifier model (log loss: 1.3670 for validation, 1.4354 for cross-validation, and 1.3974 for holdout) was used again to estimate the DLQI development for 6 months. Finally, app use was analyzed using an elastic net blender model (area under the curve: 0.6567 for validation, 0.6207 for cross-validation, and 0.7232 for holdout). Influential feature correlations were identified, including BMI, age, disease activity, DLQI, and Hospital Anxiety and Depression Scale-Anxiety scores at follow-up. App use increased with BMI >35, was less common in patients aged >47 years and those aged 23 to 31 years, and was more common in those with higher disease activity. A Hospital Anxiety and Depression Scale-Anxiety score >8 had a slightly positive effect on app use. This study provides valuable insights into the relationship between data characteristics and targeted outcomes in patients with chronic eczema or psoriasis, highlighting the potential of smartphone and AutoML techniques in improving chronic disease management and patient care.

Exploring the visual distinguishability and topic autocorrelation of murals unearthed in China from the spatial perspective

Murals unearthed in China have outstanding regional characteristics and one of the largest period spans in scale and variety. To explore the visual distinguishability and topic autocorrelation of murals unearthed in China from the spatial perspective, multiple classification models are employed to classify murals unearthed in China through visual features. Then, the k-means is employed to mine topics, and they are analysed through topic intensities (TIs), Moran’s Index (MI) and spatial topic concentration degrees (STCDs). In addition, the characteristics of topic distribution and evolution are summarised and revealed in the spatial dimension. From a spatial perspective, it verifies the distinguishability of visual features of murals through ViT_BOW_GNB, and the precision of this model is 98.17%. Thirteen topics are clustered through k-means, and the distribution of mural topics is spatial autocorrelation according to MI. Besides, the topic evolves from the political centre to the surrounding area, and the topics with high intensities are highly concentrated in spatial. This study reveals the spatial characteristics of the mural at the level of visual features and semantics, which facilitates the digital management, conservation and knowledge discovery of cultural heritage resources.

Research on white feather broiler health monitoring method based on sound detection and transfer learning

Livestock-vocalization-based health monitoring methods have the advantage of being contactless and non-invasive. In this study, the authors proposed a white feather broiler health monitoring method based on sound detection and transfer learning. The health of white feather broilers in the breeding area was determined by judging the frequency of coughs in the white feather broiler audios, and a quantitative evaluation index, cough rate, was newly proposed. Thus, the white feather broiler health monitoring problem is transformed into the multi-classification problem in machine learning, and training a reliable classification model becomes critical. First, large numbers of labelled white feather broiler audios were collected. Through signal filtering, sub-frame processing, pulse extraction and endpoint detection, the white feather broiler audios were transformed into labelled frame signals. Next, sixty sound features in multiple domains were extracted, and the labelled frame signals were converted into labelled data, thus created the data set. Then, multiple non-linear classification models were trained, the best performing classification model was performed parameter optimization, and a reliable classification model was obtained. Finally, the classification model was used to identify the sound types that appear sequentially in the unfamiliar white feather broiler audio. Based on this, the cough rate of the audio was calculated, and the health of the white feather broilers in the source breeding area was determined. In practice, there are differences in the vocalizations of white feather broilers at different days-old, and training classification models to identify white feather broiler audios of each day-old would result in a large resource loss. To address this problem, the authors considered setting white feather broilers of every three days-old as a collective, and attempted to train applicable classification models, respectively. Specifically, one of the collectives was selected, large number of labelled white feather broiler audios were collected, the master data set was created, and the conventional classification model was trained. It is based on the parameter-optimized random forest, which achieved a classification accuracy of 91.25%. For the other collectives, a small number of labelled white feather broiler audios were collected, and slave data sets were created, respectively. The improved TrAdaBoost multi-classification algorithm was used to filter instances from the master data set to each slave data set, the transfer data sets were created, and the transfer classification models were trained, respectively. They achieved a classification accuracy of approximately 83%. In addition, the definition of identification accuracy was newly proposed. Test results show that, the average classification accuracy achieved by the conventional classification model was 88.93%, and the average classification accuracy achieved by the transfer classification models was approximately 82%. This indicates the strong generalization ability of the classification models. The identification accuracies achieved by several classification models were all 100%. It demonstrates the feasibility and practicality of the method proposed in this paper.

Automated machine learning-based model for predicting benign anastomotic strictures in patients with rectal cancer who have received anterior resection

BackgroundBenign anastomotic strictures (BAS) significantly impact patients' quality of life and long-term prognosis. However, the current clinical practice lacks accurate tools for predicting BAS. This study aimed to develop a machine-learning model to predict BAS in patients with rectal cancer who have undergone anterior resection. MethodsData from 1973 patients who underwent anterior resection for rectal cancer were collected. Multiple machine learning classification models were integrated to analyze the data and identify the optimal model. Model performance was evaluated using receiver operator characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves. The Shapley Additive exPlanation (SHAP) algorithm was utilized to assess the impact of various clinical characteristics on the optimal model to enhance the interpretability of the model results. ResultsA total of 10 clinical features were considered in constructing the machine learning model. The model evaluation results indicated that the random forest (RF)model was optimal, with the area under the test set curve (AUC: 0.888, 95% CI: 0.810–0.965), accuracy: 0.792, sensitivity: 0.846, specificity: 0.791. The SHAP algorithm analysis identified prophylactic ileostomy, operative time, and anastomotic leakage as significant contributing factors influencing the predictions of the RF model. ConclusionWe developed a robust machine-learning model and user-friendly online prediction tool for predicting BAS following anterior resection of rectal cancer. This tool offers a potential foundation for BAS prevention and aids clinical practice by enabling more efficient disease management and precise medical interventions.

A Study on Influential Features for Predicting Best Answers in Community Question-Answering Forums

The knowledge provided by user communities in question-answering (QA) forums is a highly valuable source of information for satisfying user information needs. However, finding the best answer for a posted question can be challenging. User-generated content in forums can be of unequal quality given the free nature of natural language and the varied levels of user expertise. Answers to a question posted in a forum are compiled in a discussion thread, concentrating also posterior activity such as comments and votes. There are usually multiple reasons why an answer successfully fulfills a certain information need and gets accepted as the best answer among a (possibly) high number of answers. In this work, we study the influence that different aspects of answers have on the prediction of the best answers in a QA forum. We collected the discussion threads of a real-world forum concerning computer programming, and we evaluated different features for representing the answers and the context in which they appear in a thread. Multiple classification models were used to compare the performance of the different features, finding that readability is one of the most important factors for detecting the best answers. The goal of this study is to shed some light on the reasons why answers are more likely to receive more votes and be selected as the best answer for a posted question. Such knowledge enables users to enhance their answers which leads, in turn, to an improvement in the overall quality of the content produced in a platform.

A Comprehensive Study on the Use of Multiple Classification Models for the Type Classification Problem of Unknown Artifacts

The early glass was often made into bead-shaped ornaments in the West Asian and Egyptian regions and introduced to China. The ancient glass in China was made locally after absorbing its technology, and the appearance of glass products is similar to that of foreign ones, but the chemical composition is different. Therefore, it is a very meaningful task to analyze and identify the composition of ancient glass products. In this paper, based on the known chemical composition data of excavated artifacts, the statistical pattern of whether the artifacts are weathered or not is visualized by using frequency distribution charts, and the interference of random errors on the results is tested through a chi-square test. Subsequently, the two major categories of high potassium glass and lead-barium glass are classified, while the problem of classifying the types of unknown artifacts is solved. In this paper, a scientific, reasonable, and systematic system of glass-type classification is established, the prediction is carried out on this data set, and more accurate results are obtained with a correct rate of 90%, which proves that the model has a strong generalization and practical significance.