Learning Classification Models Research Articles

Fabric Defect Detection Method Based on Multi-scale Fusion Attention Mechanisms

Abstract Fabric defect detection is extremely important for the development of the textile industry, but the existing traditional image processing algorithms are not good enough to detect fabric defects, and the detection efficiency and accuracy of the classical deep learning model is not satisfactory, so this paper proposes an improved fabric defect detection method based on multi-scale fusion of attention mechanism YOLOv7-PCBS. Based on the YOLOv7 network structure, some of the standard convolutions of the backbone network are replaced with Partial Convolution (PConv) modules, which reduces the amount of network computation and improves the network detection speed; add Coordinate Attention (CA) to enhance the ability of extracting the positional features of tiny defects in fabrics; reconfiguration of the SPPCSPC module to improve small target detection; optimization of Bidirectional Feature Pyramid Network (BiFPN) and design of Tiny- BiFPN for simple and fast multi-scale feature fusion; finally, a novel loss function SIoU with angular loss is introduced to facilitate the fitting of the true and predicted frames and enhance the accuracy of defect prediction. The results show that the algorithm achieves a mAP value of 94.4% on the detection of defects in solid-colored fabrics of six denim materials, which is an improvement of 15.1% compared to the original YOLOv7 algorithm, while the model achieves a frame rate of 59.5 per second. Compared with other traditional deep learning algorithms SSD and Faster-RCNN, the detection accuracies are improved by 21.6% and 15.2%, and the FPS values are improved by 78.1% and 101.0%, respectively. Therefore, the YOLOv7-PCBS fabric defect detection algorithm proposed in this paper makes the fabric defect detection results more accurate while realizing lightweight, which provides an important technical reference for the subsequent improvement of textile quality.

Grading of diabetic retinopathy using a pre-segmenting deep learning classification model: Validation of an automated algorithm.

To validate the performance of autonomous diabetic retinopathy (DR) grading by comparing a human grader and a self-developed deep-learning (DL) algorithm with gold-standard evaluation. We included 500, 6-field retinal images graded by an expert ophthalmologist (gold standard) according to the International Clinical Diabetic Retinopathy Disease Severity Scale as represented with DR levels 0-4 (97, 100, 100, 103, 100, respectively). Weighted kappa was calculated to measure the DR classification agreement for (1) a certified human grader without, and (2) with assistance from a DL algorithm and (3) the DL operating autonomously. Using any DR (level 0 vs. 1-4) as a cutoff, we calculated sensitivity, specificity, as well as positive and negative predictive values (PPV and NPV). Finally, we assessed lesion discrepancies between Model 3 and the gold standard. As compared to the gold standard, weighted kappa for Models 1-3 was 0.88, 0.89 and 0.72, sensitivities were 95%, 94% and 78% and specificities were 82%, 84% and 81%. Extrapolating to a real-world DR prevalence of 23.8%, the PPV were 63%, 64% and 57% and the NPV were 98%, 98% and 92%. Discrepancies between the gold standard and Model 3 were mainly incorrect detection of artefacts (n = 49), missed microaneurysms (n = 26) and inconsistencies between the segmentation and classification (n = 51). While the autonomous DL algorithm for DR classification only performed on par with a human grader for some measures in a high-risk population, extrapolations to a real-world population demonstrated an excellent 92% NPV, which could make it clinically feasible to use autonomously to identify non-DR patients.

Towards accurate screening and prevention for PTSD (2-ASAP): protocol of a longitudinal prospective cohort study

BackgroundEffective preventive interventions for PTSD rely on early identification of individuals at risk for developing PTSD. To establish early post-trauma who are at risk, there is a need for accurate prognostic risk screening instruments for PTSD that can be widely implemented in recently trauma-exposed adults. Achieving such accuracy and generalizability requires external validation of machine learning classification models. The current 2-ASAP cohort study will perform external validation on both full and minimal feature sets of supervised machine learning classification models assessing individual risk to follow an adverse PTSD symptom trajectory over the course of 1 year. We will derive these models from the TraumaTIPS cohort, separately for men and women.MethodThe 2-ASAP longitudinal cohort will include N = 863 adults (N = 436 females, N = 427 males) who were recently exposed to acute civilian trauma. We will include civilian victims of accidents, crime and calamities at Victim Support Netherlands; and who were presented for medical evaluation of (suspected) traumatic injuries by emergency transportation to the emergency department. The baseline assessment within 2 months post-trauma will include self-report questionnaires on demographic, medical and traumatic event characteristics; potential risk and protective factors for PTSD; PTSD symptom severity and other adverse outcomes; and current best-practice PTSD screening instruments. Participants will be followed at 3, 6, 9, and 12 months post-trauma, assessing PTSD symptom severity and other adverse outcomes via self-report questionnaires.DiscussionThe ultimate goal of our study is to improve accurate screening and prevention for PTSD in recently trauma-exposed civilians. To enable future large-scale implementation, we will use self-report data to inform the prognostic models; and we will derive a minimal feature set of the classification models. This can be transformed into a short online screening instrument that is user-friendly for recently trauma-exposed adults to fill in. The eventual short online screening instrument will classify early post-trauma which adults are at risk for developing PTSD. Those at risk can be targeted and may subsequently benefit from preventive interventions, aiming to reduce PTSD and relatedly improve psychological, functional and economic outcomes.

Pengaruh Model Pembelajaran Flipped Classroom Terhadap Literasi Numerasi Siswa Kelas VIII SMP Swasta Gajah Mada

Mathematics plays an important role in everyday life, including in the development of human thinking power, science, and technology, although it seems unreal and abstract. This study aims to determine the Effect of the Flipped Classroom Learning Model on Numeracy Literacy of Class VIII Students of Gajah Mada Private Middle School. The methodology of this study uses a quantitative quasi-experimental research type with a posttest in two classes. The population of this study were students of class VIII A, VIII B, VIII C at Gajah Mada Private Middle School with a total of 80 students. The sample in this study was class VIII B, and VIII C. The data sampling technique was carried out using the cluster random sampling technique. Data analysis was carried out with a posttest in both classes. Based on the results of the study, it can be concluded that "The Effect of the Flipped Classroom Learning Model on Numeracy Literacy of Class VIII Students of Gajah Mada Private Middle School". The data of this study are quantitative data obtained from the numeracy literacy test. Data analysis using the t-test. The results of the data analysis show that the ability of numeracy literacy in the Flipped Classroom learning model is higher than numeracy literacy without using the Flipped Classroom learning model. Thus, the flipped class learning model has an effect on students' numeracy literacy.

A Machine Learning and Deep Learning-Based Account Code Classification Model for Sustainable Accounting Practices

Accounting account codes are created within a specific logic framework to systematically and accurately record a company’s financial transactions. Currently, accounting reports are processed manually, which increases the likelihood of errors and slows down the process. This study aims to use image processing techniques to predict cash codes in accounting reports, automate accounting processes, improve accuracy, and save time. Deep learning embeddings from Inception V3, SqueezeNet, VGG-19, VGG-16, Painters, and DeepLoc networks were utilized in the feature extraction phase. A total of six learning algorithms, namely Logistic Regression, Gradient Boosting, Neural Network, kNN, Naive Bayes, and Stochastic Gradient Descent were employed to classify the images. The highest accuracy rate of 99.2% was achieved with the combination of the Inception V3 feature extractor and the Neural Network classifier. The results demonstrate that image processing methods significantly reduce error rates in accounting records, accelerate processes, and support sustainable accounting practices. This indicates that image processing techniques have substantial potential to contribute to digital transformation in accounting, helping businesses achieve their sustainability goals.

Prediction and feature selection of low birth weight using machine learning algorithms

Background and aimsThe birth weight of a newborn is a crucial factor that affects their overall health and future well-being. Low birth weight (LBW) is a widespread global issue, which the World Health Organization defines as weighing less than 2,500 g. LBW can have severe negative consequences on an individual’s health, including neonatal mortality and various health concerns throughout their life. To address this problem, this study has been conducted using BDHS 2017–2018 data to uncover important aspects of LBW using a variety of machine learning (ML) approaches and to determine the best feature selection technique and best predictive ML model.MethodsTo pick out the key features, the Boruta algorithm and wrapper method were used. Logistic Regression (LR) used as traditional method and several machine learning classifiers were then used, including, DT (Decision Tree), SVM (Support Vector Machine), NB (Naïve Bayes), RF (Random Forest), XGBoost (eXtreme Gradient Boosting), and AdaBoost (Adaptive Boosting), to determine the best model for predicting LBW. The model’s performance was evaluated based on the specificity, sensitivity, accuracy, F1 score and AUC value.ResultsResult shows, Boruta algorithm identifies eleven significant features including respondent’s age, highest education level, educational attainment, wealth index, age at first birth, weight, height, BMI, age at first sexual intercourse, birth order number, and whether the child is a twin. Incorporating Boruta algorithm’s significant features, the performance of traditional LR and ML methods including DT, SVM, NB, RF, XGBoost, and AB were evaluated where LR, had a specificity, sensitivity, accuracy and F1 score of 0.85, 0.5, 85.15% and 0.915. While the ML methods DT, SVM, NB, RF, XGBoost, and AB model’s respective accuracy values were 85.35%, 85.15%, 84.54%, 81.18%, and 84.41%. Based on the specificity, sensitivity, accuracy, F1 score and AUC, RF (specificity = 0.99, sensitivity = 0.58, accuracy = 85.86%, F1 score = 0.9243, AUC = 0.549) outperformed the other methods. Both the classical (LR) and machine learning (ML) models’ performance has improved dramatically when important characteristics are extracted using the wrapper method. The LR method identified five significant features with a specificity, sensitivity, accuracy and F1 score of 0.87, 0.33, 87.12% and 0.9309. The region, whether the infant is a twin, and cesarean delivery were the three key features discovered by the DT and RF models, which were implemented using the wrapper technique. All three models had the identical F1 score of 0.9318. However, “child is twin” was recognized as a significant feature by the SVM, NB, and AB models, with an F1 score of 0.9315. Ultimately, with an F1 score of 0.9315, the XGBoost model recognized “child is twin” and “age at first sex” as relevant features. Random Forest again beat the other approaches in this instance.ConclusionsThe study reveals Wrapper method as the optimal feature selection technique. The ML method outperforms traditional methods, with Random Forest (RF) being the most effective predictive model for Low-Birth-Weight prediction. The study suggests that policymakers in Bangladesh can mitigate low birth weight newborns by considering identified risk factors.

Comparison of Electrocardiogram between Dilated Cardiomyopathy and Ischemic Cardiomyopathy Based on Empirical Mode Decomposition and Variational Mode Decomposition.

The clinical manifestations of ischemic cardiomyopathy (ICM) bear resemblance to dilated cardiomyopathy (DCM), yet their treatments and prognoses are quite different. Early differentiation between these conditions yields positive outcomes, but the gold standard (coronary angiography) is invasive. The potential use of ECG signals based on variational mode decomposition (VMD) as an alternative remains underexplored. An ECG dataset containing 87 subjects (44 DCM, 43 ICM) is pre-processed for denoising and heartbeat division. Firstly, the ECG signal is processed by empirical mode decomposition (EMD) and VMD. And then, five modes are determined by correlation analysis. Secondly, bispectral analysis is conducted on these modes, extracting corresponding bispectral and nonlinear features. Finally, the features are processed using five machine learning classification models, and a comparative assessment of their classification efficacy is facilitated. The results show that the technique proposed provides a better categorization for DCM and ICM using ECG signals compared to previous approaches, with a highest classification accuracy of 98.30%. Moreover, VMD consistently outperforms EMD under diverse conditions such as different modes, leads, and classifiers. The superiority of VMD on ECG analysis is verified.

Spectral data driven machine learning classification models for real time leaf spot disease detection in brinjal crops

This study presents the development and evaluation of machine learning models for detecting leaf spot disease in brinjal crops using spectral sensor data. The spectral reflectance of diseased and healthy tissues was recorded across nine wavelength bands (F1: 415 nm, F2: 445 nm, F3: 480 nm, F4: 515 nm, F5: 555 nm, F6: 590 nm, F7: 630 nm, F8: 680 nm, and F9: NIR-750 nm). The data revealed distinct spectral signatures, particularly between F5 (555 nm) and F9 (NIR), where diseased tissues consistently showed lower reflectance compared to healthy tissues. Two machine learning algorithms, Decision Tree (DT) and Support Vector Machine (SVM), were employed to classify the spectral data. The DT model achieved a maximum testing accuracy of 88.2 %, with a Gini index and a depth of 4 as optimal hyperparameters. The confusion matrix indicated that the DT model correctly identified 883 diseased instances and 667 healthy cases, while misclassifying 213 healthy tissues as diseased and 25 diseased tissues as healthy. The SVM model, configured with a cost parameter of 10.0 and a tolerance of 0.01, outperformed the DT model, achieving a testing accuracy of 92.4 %. The SVM model correctly classified 99.3 % of diseased instances and 94.1 % of healthy cases. The results demonstrate the potential of spectral sensor data combined with ML algorithms for precise disease detection, facilitating targeted pesticide application, and reducing input costs. The high accuracy of the SVM model underscores its utility in agricultural disease management, enabling early intervention and enhancing crop health monitoring. Future research may explore integrating multiple sensors and advanced feature extraction methods to further improve the efficiency and accuracy of these systems.

Lightweight Detection of Train Underframe Bolts Based on SFCA-YOLOv8s

Improving the accuracy and detection speed of bolt recognition under the complex background of the train underframe is crucial for the safety of train operation. To achieve efficient detection, a lightweight detection method based on SFCA-YOLOv8s is proposed. The underframe bolt images are captured by a self-designed track-based inspection robot, and a dataset is constructed by mixing simulated platform images with real train underframe bolt images. By combining the C2f module with ScConv lightweight convolution and replacing the Bottleneck structure with the Faster_Block structure, the SFC2f module is designed for feature extraction to improve detection accuracy and speed. It is compared with FasterNet, GhostNet, and MobileNetV3. Additionally, the CA attention mechanism is introduced, and MPDIoU is used as the loss function of YOLOv8s. LAMP scores are used to rank the model weight parameters, and unimportant weight parameters are pruned to achieve model compression. The compressed SFCA-YOLOv8s model is compared with models such as YOLOv5s, YOLOv7, and YOLOX-s in comparative experiments. The results indicate that the final model achieves an average detection accuracy of 93.3% on the mixed dataset, with a detection speed of 261 FPS. Compared with other classical deep learning models, the improved model demonstrates superior performance in detection effectiveness, robustness, and generalization. Even in the absence of sufficient real underframe bolt images, the algorithm enables the trained network to better adapt to real environments, improving bolt recognition accuracy and detection speed, thus providing technical references and theoretical support for subsequent related research.

Research on fiber OTDR signal recognition and classification based on the ViT model

In the field of event recognition for phase-sensitive optical time-domain reflectometry (-OTDR), convolutional neural networks (CNNs) have been the mainstream tool. However, Transformer models, with their self-attention mechanism, have provided a new perspective for image recognition tasks. This paper proposes a -OTDR event recognition method based on the Transformer model and experimentally demonstrates its significant advantages over CNNs. The method utilizes an attention-based feature extraction approach to recognize distributed fiber optic sensing signals in a phase-sensitive optical time-domain reflectometer (-OTDR). In perimeter defense applications, various interferences and noise severely affect the recognition of -OTDR sensing signals, leading to false alarms, making accurate signal recognition both challenging and necessary. Extracting signal features and using machine learning classification models to recognize signals has been a research hotspot in recent years. The effectiveness of feature extraction is crucial to classification performance. This project introduces a temporal-spatial dual attention mechanism into the feature extraction of fiber optic sensing signals to improve the accuracy and robustness of signal recognition.

Opportunities and limits of image-based plant stress phenotyping: detecting plant salt stress status using machine learning techniques

Abstract Plant stress phenotyping is a useful tool to facilitate targeted interventions and optimize management practices of plants growing in stressful conditions. However, various technical challenges still need to be overcome, though, and there is a clear need for investigating functional traits that can be used as a proxy for stress prediction, especially for abiotic stresses. This experimental work leveraged machine learning classification models to detect salt stress in two populations of a non-model species, combining image-based approaches (i.e. both manual and automated) and minimal morpho-physiological/biochemical analyses. A small set of specific features, combining malondialdehyde content with other non-destructive image-derived traits, such as Chroma Difference and Chroma Ratio indices, was able to distinguish non-stressed from stressed plants (2-class model; precision: 0.91) as well as stress intensity (3-class model; precision: 0.84). This flexible approach can be adapted to different plant developmental stages and leaf shape/morphology. In the future, the robustness and reliability of the models should be tested in other species and other abiotic stresses, such as drought.

Upaya Meningkatkan Hasil Belajar Peserta Didik PAK dengan Model PBL pada Materi Yesus Mengajarkan Pengampunan Kelas V Fase C SD Negeri 5 Cinta Maju

This study aims to improve learning outcomes in Catholic Religious Education and Budi Pekerti subjects by using the Problem Based Learning (PBL) learning model for class V SD Negeri 5 Cinta Maju in the 2024/2025 school year. This type of research is Classroom Action Research (PTK) which is carried out in two cycles with 1 meeting in each cycle. Each cycle consists of several stages, namely planning, implementation, action, observation and reflection. The first and second cycles discussed the material Jesus Teaches Forgiveness. This study aims to determine whether there is an increase in student learning outcomes with the help of the PBL learning model on the material of Jesus Teaches Forgiveness in class V in the subject of Catholic Religious Education and ethics at SD Negeri 5 Cinta Maju. This PTK also aims to determine how much the increase in learning achievement targets with the help of the PBL method on the material Jesus Teaches Forgiveness in class V in Catholic religious education and character education subjects at SD Negeri 5 Cinta Maju. Based on the results of observations of student learning activities, teacher activities, and student learning outcomes tests that the Problem Based Learning learning model can improve the activities and learning outcomes of Catholic Religious Education and Ethics in class V Phase C students of SD Negeri 5 Cinta Maju, Sitiotio District, Samosir Regency.

Meningkatkan Kemampuan Menceritakan Kembali Kisah Tokoh-Tokoh Perjanjian Lama Melalui Kombinasi Metode Show And Tell Dan Model Pembelajaran Problem-Based Learning (PBL) Pada Peserta Didik Fase A Kelas 2 SDN 077285 Afia

This study aims to improve students' ability to retell the stories of Old Testament characters through a combination of the Show and Tell method and the Problem-Based Learning (PBL) learning model in class 2 of SDN 077285 Afia. The Show and Tell method provides students with the opportunity to express their understanding of the material in a creative way, while PBL stimulates critical and collaborative thinking skills through solving real problems. This study uses a quantitative approach with a classroom action design, which involves data collection through observation, testing, and interviews. The results show that the combination of these two methods is significant in improving students' ability to retell the stories, as well as increasing student engagement and motivation to learn. This study concludes that the application of the combination of the Show and Tell and PBL methods is effective in learning, especially in the context of religious education.

Peningkatan Hasil Belajar Pendidikan Agama Katolik melalui Model PBL Berbantuan Media Audio Visual di Kelas III SDN 101802 Namorambe

This study aims to determine the improvement of learning outcomes by using the based learning model in class IV of SDN 101802 Namorambe. This study used the Classroom Action Research (CAR) method, the population of this study were Catholic students in class III of SDN 101802 Namorambe totaling 8 students consisting of 4 males and 4 females. Sampling using purposive sampling and the sample used was class III totaling 8 students. The results of this study indicate that student learning outcomes using the based learning model are included in the very good category seen that the average post-test score of the best students at the cycle I stage was 65,6 then increased to 86,5 in the post-test cycle II and the smallest students at the Cycle 1 stage were 50 then increased to 79. This shows that there is a significant positive influence of the use of based learning on student learning outcomes in the subject of Catholic Religious Education in class III of SDN 101802 Namorambe.

Transforming Motor Imagery Analysis: A Novel EEG Classification Framework Using AtSiftNet Method

This paper presents an innovative approach for the Feature Extraction method using Self-Attention, incorporating various Feature Selection techniques known as the AtSiftNet method to enhance the classification performance of motor imaginary activities using electrophotography (EEG) signals. Initially, the EEG signals were sorted and then denoised using multiscale principal component analysis to obtain clean EEG signals. However, we also conducted a non-denoised experiment. Subsequently, the clean EEG signals underwent the Self-Attention feature extraction method to compute the features of each trial (i.e., 350×18). The best 1 or 15 features were then extracted through eight different feature selection techniques. Finally, five different machine learning and neural network classification models were employed to calculate the accuracy, sensitivity, and specificity of this approach. The BCI competition III dataset IV-a was utilized for all experiments, encompassing the datasets of five volunteers who participated in the competition. The experiment findings reveal that the average accuracy of classification is highest among ReliefF (i.e., 99.946%), Mutual Information (i.e., 98.902%), Independent Component Analysis (i.e., 99.62%), and Principal Component Analysis (i.e., 98.884%) for both 1 and 15 best-selected features from each trial. These accuracies were obtained for motor imagery using a Support Vector Machine (SVM) as a classifier. In addition, five-fold validation was performed in this paper to assess the fair performance estimation and robustness of the model. The average accuracy obtained through five-fold validation is 99.89%. The experiments’ findings indicate that the suggested framework provides a resilient biomarker with minimal computational complexity, making it a suitable choice for advancing Motor Imagery Brain–Computer Interfaces (BCI).

Peningkatan Hasil Belajar Pendidikan Agama Katolik Melalui Model PBL di Kelas IV SD Negeri 1 Tualang

This study aims to determine the improvement of learning outcomes by using the based learning model in class IV of SD Negeri 1 Tualang. This study used the Classroom Action Research (CAR) method, the population of this study were Catholic students in class IV of SD Negeri 1 Tualang totaling 9 students consisting of 3 males and 6 females. Sampling using purposive sampling and the sample used was class IV totaling 9 students. The results of this study indicate that student learning outcomes using the based learning model are included in the very good category seen that the average post-test score of the best students at the cycle I stage was 85 then increased to 90 in the post-test cycle II and the smallest students at the Cycle 1 stage were 60 then increased to 75. This shows that there is a significant positive influence of the use of based learning on student learning outcomes in the subject of Catholic Religious Education in class IV of SD Negeri 1 Tualang

Meningkatkan Hasil Belajar PAK melalui Problem Based Learning (PBL) Tema Kemampuan dan Keterbatasanku bagi Peserta Didik Kelas VII Fase D SMP Negeri 1 Sawo

This research aims to increase student interest and learning outcomes in Catholic Religious Education subjects through the application of the Problem Based Learning (PBL) learning model for class VII students at SMP Negeri 1 Sawo. This research uses the classroom action research (PTK) method which is carried out in two cycles. The first and second cycles discuss My Abilities and Limitations. The subjects in this research were Class VII-B students at SMP Negeri 1 Sawo for the 2024/2025 academic year, consisting of 12 people. The main techniques for collecting data are carried out by means of tests and observations, while supporting techniques use interviews and documentation. The research results show that the application of the PBL model can significantly increase student interest and learning outcomes. This is shown by increased student activity in learning, increased test scores, and positive student responses to learning.

Meningkatkan Hasil Belajar Pendidikan Agama Katolik Melalui Metode Problem Based Learning Kelas VIII SMP Negeri 3 Tapian Nauli

Humans who are living creatures with reason have the potential to continue to develop. One of the human developments is Catholic religious education in schools. Learning Catholic Religious Education encourages students to become faithful individuals who are able to live and realize their faith in everyday life. The independent curriculum emphasizes the profile of Pancasila students. In this classroom action research, the researcher emphasizes the dimensions of faith in God Almighty and noble character and the dimension of critical reasoning. This study describes the process of implementing the problem-based learning (PBL) model in Catholic Religious Education learning for class VIII and analyzes its impact on student motivation and learning activities. Through observation, interviews, and document analysis, it was found that PBL succeeded in creating an active and student-centered learning environment. Students were more involved in the learning process, showed increased learning motivation, and developed collaboration skills. The evaluation results showed a significant increase in student learning outcomes after the implementation of PBL. The objectives of this study were (1) To determine the improvement in student learning outcomes through Problem Based Learning for Class VIII of SMP Negeri 3 Tapian Nauli. (2) Knowing the increase in target achievement through the Problem Based Learning model in Class VIII of SMP Negeri 3 Tapian Nauli. The interaction and communication between teachers and students and communication between students greatly supports learning activities.

Meningkatkan Hasil Belajar Pendidikan Agama Katolik dan Budi Pekerti Materi Aku Pribadi yang Unik Model Problem Based Learning (PBL) Berbantuan Audio Visual untuk Kelas X SMAS Karya Sedar Biru-Biru 2024/2025

This research was conducted at SMAS Karya Sedar Biru-Biru in Class X with the subjects of this study were 10 students. Based on the analysis of data on the initial reflection which still showed that the learning outcomes of students were still lacking, the research was conducted by implementing the Problem Based Learning (PBL) learning model in learning Unique Personal Humans to improve student learning outcomes. 1) The formulation of the problem in this study is: How to improve the learning outcomes of Catholic Religious Education on the material "Unique Personal Humans" through the Problem Based Learning model for class X students of SMAS Karya Sedar Biru-Biru Academic Year 2024/2025 2) How much is the increase in student learning outcomes after implementing the Problem Based Learning model for class X students of SMAS Karya Sedar Biru-Biru Academic Year 2024/2025? The purpose of this study was to determine the Improvement in Learning Outcomes of Unique Personal Humans by implementing the Problem Based Learning model for class X students of SMAS Karya Sedar Biru-Biru. Data collection techniques were observation and testing. The data analysis technique used was qualitative descriptive technique. According to the results of the research that has been conducted, the following conclusions were obtained: Learning activities using the Problem Based Learning model can improve student learning outcomes on the Unique Human Personality material from cycle I by 71% to 90% in cycle II. The increase that occurred from cycle I to cycle II was 19%

Automobile exterior emotional design method based on deep learning and multiple views imagery integrating calculation

In the context of the increasingly dynamic experience economy, users’ emotional experience with automobile has become a leading factor in consumption, making automobile exterior crucial carriers of emotional needs and design expression. However, users’ emotion expression is from three-dimensional automobile entities, with a single perspective potentially causing deviations. Thus, this paper presents a novel automobile exterior emotion design method based on deep learning and multiple views imagery integrating calculation. This approach enables a seamless transition from users’ emotional needs to automobile exterior design. First, we explore users’ emotional evaluation of automobile exterior using the semantic differential method. The improved model of ResNet101-Attention mechanism was used to predict users’ emotion. Additionally, based on above emotional evaluation values, this study proposed a calculation method of multiple views automobile imagery integrating using the Analytic Hierarchy Process and Entropy Weight, calculate the automobile imagery weights of different views, and obtain the final automobile exterior emotional imagery. Finally, the Captum toolkit and the LoRA fine-tuning training technique are used to construct an automobile exterior design optimization generation model, producing a design that meets users’ emotion needs. By comparing the results obtained using both manual and automatic evaluations, and comparing to classical deep learning models. we verify the model’s validity and the advantages of the proposed method. This intelligent method assists designers in quickly identifying users’ responses for automobile design and provides automobile design that meet their emotional needs