Classification Model Research Articles

The Impact of Methamphetamine on Liver Injury in Iraqi Male Addicts

Methamphetamine (METH) is a powerful stimulant that affects neurochemical processes controlling heart rate, appetite, blood pressure, body temperature, and wakefulness, making it highly susceptible to abuse. Liver enzymes such as ALT, AST, ALP, and GGT are crucial for liver function. Albumin, a protein synthesized by healthy liver cells, serves as an indicator of chronic liver disease. Additionally, hepatocytes produce bile acids, which are essential for the secretion of bile salts into the bile canaliculi. Disruption in this secretion results in the accumulation of bile salts in the canaliculi, leading to intrahepatic cholestasis. METH-induced liver toxicity involves disruptions in hepatic metabolism, oxidative stress, and increased body temperature, affecting cellular processes such as cell division and the cell cycle and potentially accelerating liver cell apoptosis. The study explores the link between liver toxicity and hepatocyte damage in Iraqi males suffering from addiction. This is a case-control study, conducted at Ibn-Rushed Psychiatric Hospital in Baghdad from July 2023 to February 2024, involved 196 males, with addiction durations exceeding 24 months with varying degrees of methamphetamine (METH) addiction. The study included 187 healthy male controls with no history of drug addiction. Participants were aged 18 to 40 years. Diagnosis was confirmed using a drug test screening card administered by a specialist. The study included liver function tests (ALT, AST, ALP, and GGT), total bilirubin, and albumin concentration assessments. Significant differences were observed between the addicts and controls, particularly a marked decrease in serum albumin concentration in the addicted males. The ROC curve classification model at various thresholds demonstrated that liver enzymes, especially ALT, ALP, and GGT, exhibited increased sensitivity to METH addiction. A histopathological examination was conducted on a deceased 38-year-old male who had a six-year history of chronic amphetamine addiction to confirm liver injury and the resulting elevation of liver enzymes. The findings of this study indicate that METH greatly affects liver function, suggested to the importance of following a preventative measures and effective treatment approaches for monitoring METH addiction progress.

Overload Alarm Prediction in Power Distribution Transformers

The growing demand for electricity puts more strain on the grid, requiring automated and proactive strategies such as overload prediction to improve grid maintenance. However, the intermittent nature of power distribution loads makes the prediction more challenging. This paper proposes a novel framework for overload alarm prediction in distribution transformers, aimed at enhancing the reliability and efficiency of grid operations. Leveraging real-world smart meter data and machine learning techniques, the proposed system develops a classification model to predict overloads for distribution transformers. Due to resource constraints, a new strategy is adopted to assess the significance of alarms based on expert observations. Subsequently, a new approach is developed to imitate the experts, leading to an automated decision-making process using random forest. Ultimately, the transfer learning strategy is utilized to predict overload alarms for distribution transformers facing data scarcity in real-world applications. The proposed system demonstrates high accuracy of overload alarm predictions, paving the way for developing more proactive grid maintenance strategies.

Making the most of errors: Utilizing erroneous classifications generated by machine-learning models of neuroimaging data to capture disorder heterogeneity.

Within-disorder heterogeneity complicates mapping the neurobiological features of psychopathology to Diagnostic and Statistical Manual of Mental Disorders conceptualizations. The present study explored the patterns of diagnostic classification errors among disorders with commonly co-occurring features to examine this heterogeneity. Classification analyses were conducted with the University of California, Los Angeles Phenomics Study database using a support-vector classifier to differentiate disorders via whole brain task-based functional connectivity, predicting that model misclassifications would provide insight about brain connectivity characteristics shared across disorders. Whether symptoms and specific brain networks could account for misclassification rates was also explored. The classification model performed better than chance (44% accuracy, p = .01) and revealed that misclassification of schizophrenia (SCZ) as bipolar disorder (BD; 38%) and BD as SCZ (36%) was symmetrical. Attention-deficit/hyperactivity disorder (ADHD) was misclassified as BD at the highest rate (46%) and higher than the converse (17%). SCZ and ADHD were misclassified least (15% SCZ as ADHD and 22% ADHD as SCZ). Considerable variance in misclassification of SCZ as BD (R2 = .83) and BD as SCZ (R2 = .71) could be accounted for by symptoms of both SCZ and BD. Permutation testing revealed disorder- and network-specific effects, with certain networks improving classification accuracy and others hindering it for specific disorders. An approach focused on classification errors replicated known disorder overlap, producing errors in the expected configuration. Further, it identified clinical and neural features within and across diagnostic categories that contribute to disorder misclassification and within-disorder heterogeneity. This approach may facilitate neurobiologically informed phenotypic differentiation within diagnostic groups. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Development and validation of a machine learning model for predicting drug-drug interactions with oral diabetes medications

Diabetes management is often complicated by comorbidities, requiring complex medication regimens that increase the risk of drug-drug interactions (DDIs), potentially compromising treatment outcomes or causing toxicity. Although machine learning (ML) models have made strides in DDI prediction, existing approaches lack specificity for oral diabetes medications and face challenges in interpretability. To address these limitations, we propose a novel ML-based framework utilizing the Simplified Molecular Input Line Entry System (SMILES) to encode structural information of oral diabetes drugs. Using this representation, we developed an XGBoost model, selecting molecular features through LASSO. Our dataset, sourced from DrugBank, included 42 oral diabetes drugs and 1,884 interacting drugs, divided into training, validation, and testing sets. The model identified 606 optimal features, achieving an F1-score of 0.8182. SHAP analysis was employed for feature interpretation, enhancing model transparency and clinical relevance. By predicting adverse DDIs, our model offers a valuable tool for clinical decision-making, aiding safer prescription practices. The 606 critical features provide insights into atomic-level interactions, linking computational predictions with biological experiments. We present a classification model specifically designed for predicting DDIs associated with oral diabetes medications, with an openly accessible web application to support diabetes management in multi-drug regimens and comorbidity settings.

AI based diagnostics product design for osteosarcoma cells microscopy imaging of bone cancer patients using CA-MobileNet V3

ObjectiveThe incidence of osteosarcoma (OS) is low, but primary malignant bone tumors rank third among the causes of death in cancer patients under the age of 20. Currently, analysis of cellular structure and tumor morphology through microscopic images remains one of the main diagnostic methods for osteosarcoma. However, this completely manual approach is tedious, time-consuming, and difficult to diagnose accurately due to the similarities in certain characteristics of malignant and benign tumors. MethodsLeveraging the potential of artificial intelligence (AI) in assessing and classifying images, this study explored a modified CA-MobileNet V3 model that was embedded into innovative microscope products to enhance the microscope’s feature extraction capabilities and help reduce misclassification during diagnosis. ResultsThe intelligent recognition model method introduced in this paper has significant advantages in retrieval and classification of osteosarcoma cells and other cell types. Compared with models such as ShuffleNet V2, EfficientNet V2, Mobilenet V3 (without transfer learning), TL-MobileNet V3 (with transfer learning), etc., the model size is only 5.33 MB, is a lightweight model, and the accuracy of the improved model reached 98.69 %. In addition, the artificial intelligence microscope (AIM) with integrated design based on this model can also help improve diagnostic efficiency. ConclusionThe innovative method of the CA-MobileNet V3 automatic classification model based on deep learning provides an efficient and reliable solution for the pathological diagnosis of osteosarcoma. This study contributes to medical image analysis and provides doctors with an accurate and valuable tool for microscopic diagnosis. It also promotes the advancement of artificial intelligence in medical imaging technology.

Comparative analysis of spectroscopic methods for rapid authentication of hazelnut cultivar and origin

Hazelnut market prices fluctuate significantly based on cultivar and provenance, making them susceptible to counterfeiting. To develop an accurate authentication method, we compared the performances of three spectroscopic methods: near infrared (NIR), handheld near infrared (hNIR), and medium infrared (MIR), on over 300 samples from various origins, cultivars, and harvest years. Spectroscopic fingerprints were used to develop and externally validate PLS-DA classification models. Both cultivar and origin models showed high accuracy in external validation. The hNIR model effectively distinguished cultivars but struggled with geographic distinctions due to lower sensitivity. NIR and MIR models showed over 93 % accuracy, with NIR slightly outperforming MIR for geographic origin. NIR proved to be a fast and suitable tool for hazelnut authentication. This study is the first to systematically compare spectroscopic tools for authenticating hazelnut cultivar and origin using the same dataset, offering valuable insights for future food authentication applications.

Detection of E. coli using bacteriophage T7 and analysis of excitation‑emission matrix fluorescence spectroscopy

Conventional detection methods require the isolation and enrichment of bacteria, followed by molecular, biochemical, or culture-based analysis. To address some of the limitations of conventional methods, this study develops a machine learning (ML) approach to analyze the excitation-emission matrix (EEM) fluorescence data generated based on bacteriophage T7 and E. coli interactions for in-situ detection of live bacteria in the presence of fresh produce homogenate. We trained classification models using various ML algorithms based on the 3-D EEM data generated with bacteria and their interactions with a T7 phage. These ML algorithms, including linear Support Vector Classifier and Random Forest, demonstrate high accuracy (>0.85) for detecting E. coli at 102 CFU/ml concentration within 6 hours. Additionally, these ML models can differentiate among different E. coli concentration levels. For example, the Gaussian Process model achieved an accuracy of 92% in detecting different concentration levels of live E. coli. Application of these ML methods to detect E. coli in spinach homogenate yielded an accuracy of 89% using the linear-SVC model. Furthermore, feature selection techniques were employed to reduce the dimensionality of the data, revealing that only six features were necessary for achieving classification accuracy (>0.85) of spinach homogenate samples containing 102 CFU/ml of E. coli. These findings highlight the potential of this novel bacterial detection methodologies, offering rapid, specific, and efficient solutions for applications in food safety and environmental monitoring.

Development of a MALDI-TOF MS model for differentiating haemorrhagic septicaemia-causing strains of Pasteurella multocida from other capsular groups.

Pasteurella multocida capsular types A, D, and F cause disease in many animal hosts, including bovine respiratory disease in cattle, which is one of the most globally significant animal diseases. Additionally, P. multocida capsular types B and E cause haemorrhagic septicaemia, a devastating disease primarily of cattle, water buffalo, and bison that develops rapidly with high mortality. Haemorrhagic septicaemia mostly occurs in developing countries and has potential to emerge elsewhere in the world. The diagnosis of haemorrhagic septicaemia currently requires recognition of compatible gross or histologic lesions and serotyping or molecular characterization of strains. In this study, we performed genomic characterization of 84 P. multocida strains, which were then used to develop and validate a matrix assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) biomarker-based method for differentiating non-haemorrhagic septicaemia strains of P. multocida from haemorrhagic septicaemia-causing strains. Haemorrhagic septicaemia strain types B:2,5, E:2,5, and B:3,4 were used to maximize diversity. Three automated classification models were generated and then used to develop an assisted model, which utilized two peaks (6419 and 7729 m/z) to accurately differentiate non-haemorrhagic septicaemia-causing strains from haemorrhagic septicaemia-causing strains of P. multocida. The assisted model performed with 98.2 % accuracy for non-haemorrhagic septicaemia strains, 100 % accuracy for classic B:2,5 and E:2,5 strains, and 84.4 % accuracy for combined haemorrhagic septicaemia-causing strains (B:2,5, E:2,5, and B:3,4) with an overall accuracy of 96.9 %. Our results suggest that MALDI-TOF MS may be used to routinely screen P. multocida isolated from diagnostic cases for initial identification of haemorrhagic septicaemia-causing strains, and to determine whether additional characterizations are warranted.

An Analysis of the Effect of Skew Rolling Parameters on the Surface Quality of C60 Steel Parts Using Classification Models

This paper presents the experimental and numerical results of a study on producing axisymmetric parts made of the C60-grade steel by skew rolling. The experimental part of this study involved conducting the skew rolling process with varying parameters, including the forming angle α, tool angle θ, chuck velocity Vu, and reduction ratio δ. Their effect on the quality of produced parts was examined and described by the roughness parameter Ra. Numerical calculations involved the use of machine learning models to predict the quality class of produced parts. The highest prediction accuracy of the results was obtained with the random forest and logistic regression models. Metrics such as precision, recall and accuracy were used to evaluate the performance of individual models. Confusion matrices and ROC curves were also employed to illustrate the performance of the classification models. The results of this study will make it possible to prevent the formation of spiral grooves on the circumference of steel parts during the rolling process.

Interpretable neural network classification model using first-order logic rules

Interpretable neural network classification model using first-order logic rules

Leaf cultivar identification via prototype-enhanced learning

Leaf cultivar identification, as a typical task of ultra-fine-grained visual classification (UFGVC), is facing a huge challenge due to the high similarity among different varieties. In practice, an instance may be related to multiple varieties to varying degrees, especially in the UFGVC datasets. However, deep learning methods trained on one-hot labels fail to reflect patterns shared across categories and thus perform poorly on this task. As an analogy to natural language processing (NLP), by capturing the co-relation between labels, label embedding can select the most informative words and neglect irrelevant ones when predicting different labels. Based on this intuition, we propose a novel method named Prototype-enhanced Learning (PEL), which is predicated on the assumption that label embedding encoded with the inter-class relationships would force the image classification model to focus on discriminative patterns. In addition, a new prototype update module is put forward to learn inter-class relations by capturing label semantic overlap and iteratively update prototypes to generate continuously enhanced soft targets. Prototype-enhanced soft labels not only contain original one-hot label information, but also introduce rich inter-category semantic association information, thus providing more effective supervision for deep model training. Extensive experimental results on 7 public datasets show that our method can significantly improve the performance on the task of ultra-fine-grained visual classification. The code is available at https://github.com/YIYIZH/PEL.

Forensic bone age assessment of hand and wrist joint MRI images in Chinese han male adolescents based on deep convolutional neural networks.

In Chinese criminal law, the ages of 12, 14, 16, and 18 years old play a significant role in the determination of criminal responsibility. In this study, we developed an epiphyseal grading system based on magnetic resonance image (MRI) of the hand and wrist for the Chinese Han population and explored the feasibility of employing deep learning techniques for bone age assessment based on MRI of the hand and wrist. This study selected 282 Chinese Han Chinese males aged 6.0-21.0 years old. In the course of our study, we proposed a novel deep learning model for extracting and enhancing MRI hand and wrist bone features to enhance the prediction of target MRI hand and wrist bone age and achieve precise classification of the target MRI and regression of bone age. The evaluation metric for the classification model including precision, specificity, sensitivity, and accuracy, while the evaluation metrics chosen for the regression model are MAE. The epiphyseal grading was used as a supervised method, which effectively solved the problem of unbalanced sample distribution, and the two experts showed strong consistency in the epiphyseal plate grading process. In the classification results, the accuracy in distinguishing between adults and minors was 91.1%, and the lowest accuracy in the three minor classifications (12, 14, and 16 years of age) was 94.6%, 91.1% and 96.4%, respectively. The MAE of the regression results was 1.24 years. In conclusion, the deep learning model proposed enabled the age assessment of hand and wrist bones based on MRI.

Modelling behavior of Crested gecko (Correlophus ciliatus) using classification algorithms

Animal behavior plays a crucial role in evolution of many species. Many studies focused on animal behavior enhance the ability to collect large and detailed data. However, this kind of data is surpassing the capability of traditional statistical methods for analysis. In this study we propose to use artificial intelligence (AI) with machine learning models (ML) as tools to study animal behavior and potentially assumed evolution patterns in their behavior. For the Crested gecko (Correlophus ciliatus), some guidelines have been published regarding the breeding of these reptiles, focusing on their behavior. However, little is known about moderating their behavior using AI and advanced ML algorithms. In this study, based on information collected from twenty individuals, we proposed building a supervised classifier model using simple Decision Tree classifier (DT), Gradient Boosting classifier (GB) and Extreme Gradient Boosting classifier (XGBoost). Our results show that the highest accuracy (above 60 %) was achieved for variables which were not complex in terms of animal behavior. The analysis presented in this study, demonstrates that it is possible to model Crested Gecko behavior using ML models.

Filtering-Assisted Airborne Point Cloud Semantic Segmentation for Transmission Lines

Point cloud semantic segmentation is crucial for identifying and analyzing transmission lines. Due to the number of point clouds being huge, complex scenes, and unbalanced sample proportion, the mainstream machine learning methods of point cloud segmentation cannot provide high efficiency and accuracy when extending to transmission line scenes. This paper proposes a filter-assisted airborne point cloud semantic segmentation for transmission lines. First, a large number of ground point clouds is identified by introducing the well-developed cloth simulation filter to alleviate the impact of the imbalance of the target object proportion on the classifier’s performance. The multi-dimensional features are then defined, and the classification model is trained to achieve the multi-element semantic segmentation of the transmission line scene. The experimental results and analysis indicate that the proposed filter-assisted algorithm can significantly improve the semantic segmentation performance of the transmission line point cloud, enhancing both the point cloud segmentation efficiency and accuracy by more than 25.46% and 3.15%, respectively. The filter-assisted point cloud semantic segmentation method reduces the volume of sample data, the number of sample classes, and the sample imbalance index in power line scenarios to a certain extent, thereby improving the classification accuracy of classifiers and reducing time consumption. This research holds significant theoretical reference value and engineering application potential for scene reconstruction and intelligent understanding of airborne laser point cloud transmission lines.

The effectiveness and factors influencing frequency of endoscopic bougie dilatation in treating postoperative anastomotic stenosis of congenital esophageal atresia

ObjectiveThis study is to evaluate the effectiveness and frequency factors of endoscopic bougie dilatation in treating postoperative anastomotic stenosis of congenital esophageal atresia (CEA).MethodsThe clinical data of patients of anastomotic stenosis with endoscopic bougie were retrospectively analyzed. According to the number of dilation times (ND), patients were divided into two groups (Group 0: ND < 3; Grooup1: ND ≥ 3), and the differences in multiple clinical data were compared. Lasso regression and Ridge regression were used to screen important variables. Classification models were built utilizing various machine learning algorithms and their performance were evaluated. Finally, Kaplan-Meier model was used to estimate the probable-time distribution of children achieving normal feeding.ResultsSeventy-five patients underwent a total of 210 times of dilation, with a median of 3 times of dilation. The overall effectiveness was 98.67% (74/75), with perforation in 2 case (0.95%), and obvious bleeding in 3 cases (1.43%). Initial diameter of bougie, final diameter of bougie, treatment pattern (Regular: dilation each 4weeks; Wait-and-see: dilation until symptoms present), age at final dilation, esophageal obstruction by food were the factors related to ND. Random Forest (RF) and Logistic regression (LR) model were excellent models for predicting ND. The median age for achieving normal eating in Group0 was 120 days (95% CI: 90–160), while it was 270 days (95% CI: 240–460) in Group1 with a statistically significant difference (P < 0.0001).ConclusionEndoscopic bougie dilatation is a safe and effective treatment for anastomotic stenosis. Selecting the appropriate bougie, using symptoms as the criterion for dilation, and minimizing the dilations under 3 times constitute a rational strategy.

What Is My Plaza for? Implementing a Machine Learning Strategy for Public Events Prediction in the Urban Square

Plazas are an essential pillar of public life in our cities. Historically, they have been seen as public fora, hosting public events that fostered trade, interaction, and debate. However, with the rise of modern urbanism, city planners considered them as part of a larger strategic development scheme overlooking their social importance. As a result, plazas have lost their function and value. In recent years, awareness has risen of the need to re-activate these public spaces to strive for social inclusion and urban resilience. Geometric and urban features of plazas and their surroundings often suggest what kinds of usage the public can make of them. In this project, we explore the application of machine learning to predict the suitability of events in public spaces, aiming to enhance urban plaza design. Learning from traditional urbanism indicators, we consider factors associated with the features of the public space, such as the number of people and the high degree of comfort, which are evolved from three subcategories: external factors, geometric shape, and design factors. We acknowledge that the predictive capability of our model is constrained by a relatively small dataset, comprising 15 real plazas in Madrid augmented digitally to 2025 fictional scenarios through self-organising maps. The article details the methods to quantify and enumerate quantitative urban features. With a categorical target variable, a classification model is trained to predict the type of event in the urban space. The model is then evaluated locally in Grasshopper by visualising a parametric verified geometry and deploying the model on other existing plazas worldwide regarding geographical proximity to Madrid, where to share or not the same cultural and environmental conditions. Despite these limitations, our findings offer valuable insights into the potential of machine learning in urban planning, suggesting pathways for future research to expand upon this foundational study.

Analysis of feature extraction techniques for sentiment analysis of tweets

Over the past few years, sentiment analysis has moved from social networking services like LinkedIn, Facebook, YouTube, Twitter, and online product-based reviews to determine public opinion or emotion using social media textual contents. The methodology includes data selection, text pre-processing, feature extraction, classification model, and result analysis. Text pre-processing is an important stage in structuring data for improved performance of our methodology. The feature extraction technique (FET) is a crucial step in sentiment analysis as it is difficult to obtain effective and useful information from highly unstructured social media data. A number of feature extraction techniques are available to extract useful features. In this work, popular feature extraction techniques including bag of words (BOW), term frequency and inverse document frequency (TF-IDF), and Word2vec are compared and analyzed for the sentiment analysis of social media contents. A method is proposed for processing text data from social media networks for sentiment analysis that uses support vector machine as a classifier. The experiments are carried on three datasets of different context namely US Airline, Movie Review, and News from Twitter. The results show that TF-IDF consistently outperformed other techniques with best accuracy of 82.33%, 92.31%, and 99.10% for Airline, Movie Review, and News datasets respectively. It is also found that the proposed method performed better than some existing methods.

Optimal feature selection for heart disease prediction using modified Artificial Bee colony (M-ABC) and K-nearest neighbors (KNN)

Heart disease is a complex and widespread illness that affects a significant number of people worldwide. Machine learning provides a way forward for early heart disease diagnosis. A classification model has been developed for the present study to predict heart disease. The attribute selection was done using a modified bee algorithm. Using the proposed model, practitioners can accurately predict heart disease and make informed decisions about patient health. In our study, we have proposed a framework based on Modified Artificial Bee Colony (M-ABC) and k-Nearest Neighbors (KNN) for predicting the optimal feature selection to obtain better accuracy. Using a modified bee algorithm, this paper focuses on identifying the optimal subset of attributes from the dataset. Specifically, during the classification-training phase, only the features that provide significant information are retained. The proposed study not only improves classification accuracy but also reduces training time for classifiers.

Harnessing Machine Learning for Effective Waste Classification and Recycling

The world produces nearly 3.5 million tons waste every day so it is important to improve waste management and recycling system. This paper defines how machine learning can classify waste, specifically using VGG16 CNN model to classify 9 types of recyclable materials such as light bulbs, paper, plastic, organic, glass, batteries, clothes, metal and e-waste .We had used nearly 8000+ images from Google Images and Dreamstime.com to test VGG16 model and compare it with another model named as Inceptionv3. But Inceptionv3 showed higher accuracy (~90%) ,and it did not work well when tested on real data. In contrast, the VGG16 model which initially had lower accuracy of (~70%), performed better when tested on real-world scenarios. We also developed a web application which not only classifies the waste but also provides the valuable information to help people how to minimize waste reduction and maximize recycling. The classified images of the waste and its respective findings will also be shared with local authorities to enhance better recycling efforts. Hence this study focuses on need to select right models for waste classification and suggests looking into another machine learning techniques in future.

Enhanced Skin Lesion Segmentation and Classification Through Ensemble Models

This study addresses challenges in skin cancer detection, particularly issues like class imbalance and the varied appearance of lesions, which complicate segmentation and classification tasks. The research employs deep learning ensemble models for both segmentation (using U-Net, SegNet, and DeepLabV3) and classification (using VGG16, ResNet-50, and Inception-V3). The ISIC dataset is balanced through oversampling in classification, and preprocessing techniques such as data augmentation and post-processing are applied in segmentation to increase robustness. The ensemble model outperformed individual models, achieving a Dice Coefficient of 0.93, an IoU of 0.90, and an accuracy of 0.95 for segmentation, with 90% accuracy on the original dataset and 99% on the balanced dataset for classification. The use of ensemble models and balanced datasets proved highly effective in improving the accuracy and reliability of automated skin lesion analysis, supporting dermatologists in early detection efforts.