Brain Glial Cell Tumor Classification through Ensemble Deep Learning with APCGAN Augmentation

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Янв. 5, 2025

Classification of brain tumor plays a vital role in medical imaging for accurate diagnosis, treatment, and monitoring. Deep learning approaches have gained significant traction this industry because their ability to extract relevant features from images. The research suggests employing an ensemble classifier with weighted voting mechanism categorize glial cell malignancies such as Astrocytoma, Glioblastoma multiforme, Oligodendroglioma, Ependymoma. proposed technique employs three main classifiers: Convolutional Neural Network (CNN), Long Short Term Memory (C-LSTM), + Conditional Random Fields (DCNN+CRF). algorithms require huge amount input data avoid overfitting. Adaptive Progressive Generative Adversarial Networks (APCGANs) are used produce realistic artificial images efficiently train the methodology. Overall, method strategy consistently outperforms other tested (CNN, C-LSTM, DCNN+CRF). Ensemble attained accuracy 99.4 %, recall - 99.1%, precision- 98.0%, F1-score 99.2%. demonstrates superior performance accurately classifying tumors, making it promising algorithm analysis tasks.

Язык: Английский

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A Systematic Comparative Study on the use of Machine Learning Techniques to Predict Lung Cancer and its Metastasis to the Liver: LCLM-Predictor Model

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Янв. 11, 2025

Lung cancer is one of the major causes deaths with thousands affected patients who have developed liver metastasis, complicating treatment and further prognosis. Early predictions lung metastasis may greatly improve patient outcomes since clinical interventions will be instituted in time. This paper compares performance different machine learning models including Decision Tree Classifiers, Logistic Regression, Naïve Bayes, K-Nearest Neighbors, Support Vector Machines Gaussian Mixture Models toward best set techniques for prediction. The applied dataset includes various features, such as respiratory symptoms biochemical markers, development stronger predictive performance. were cross-validated using testing validation aimed at generalizing whole model reliability generating both train test data. results generated are gauged metrics accuracy, precision, recall, F1-score, area under ROC curve. Results obtained revealed that KNN also showed accuracy strong classification performance, especially early-stage metastasis. present study a comparison models, which hence denotes potential these decision-making suggests application to diagnostic tools early detection cancer. provides very useful guide applicable use oncology helps pave way future research would focused on optimization integration into healthcare systems produce better management survival rates.

Язык: Английский

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Social and Cognitive Predictors of Collaborative Learning in Music Ensembles

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Янв. 13, 2025

There have been many attempts to find ways make music education more relevant and useful for pupils. Learning theories, performance-based learning, contract-learning, discovery-learning, cooperative daily clocking, stage practice, music-focused required elective courses are all part of the implementation these methods. Since high vocational students tend lower GPAs, it is imperative that they discover strategies enhance their academic performance. Reform, rather than relying on theoretical frameworks, should be grounded practical, innovative human actions. Both instructors pupils possess capacity comprehend what learnt, according humanistic perspective. This paper provides evidence collaborative learning beneficial first-year practice in a popular program at Chinese institution. The work small, diverse groups. Data was collected analyzed from over course one year with grades 4-6.. Collaboration powerful tool has applications, including but not limited degree programs, which implemented this using machine techniques. It zeroed down seven important characteristics, had obvious applications educational process. Another online could use method predict students' performance, real-time tracking progress risk dropping out, after adjusted capture features corresponding different contexts. also applied other management platforms.

Язык: Английский

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Understanding and Analysing Causal Relations through Modelling using Causal Machine Learning

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Фев. 11, 2025

The study of causal inference has gained significant attention in artificial intelligence (AI) and machine learning (ML), particularly areas such as explainability, automated diagnostics, reinforcement learning, transfer learning.. This research applies techniques to analyze student placement data, aiming establish cause-and-effect relationships rather than mere correlations. Using the DoWhy Python library, follows a structured four-step approach—Modeling, Identification, Estimation, Refutation—and introduces novel 3D framework (Data Correlation, Causal Discovery, Domain Knowledge) enhance modeling reliability. discovery algorithms, including Peter Clark (PC), Greedy Equivalence Search (GES), Linear Non-Gaussian Acyclic Model (LiNGAM), are applied construct validate robust model. Results indicate that internships (0.155) academic branch selection (0.148) most influential factors placements, while CGPA (0.042), projects (0.035), employability skills (0.016) have moderate effects, extracurricular activities (0.004) MOOCs courses (0.012) exhibit minimal impact. underscores significance reasoning higher education analytics highlights effectiveness ML real-world decision-making. Future work may explore larger datasets, integrate additional educational variables, extend this approach other disciplines for broader applicability.

Язык: Английский

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Rainfall Forecasting in India Using Combined Machine Learning Approach and Soft Computing Techniques : A HYBRID MODEL

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Янв. 9, 2025

Accurate rainfall prediction in India is crucial for agriculture, water management, and disaster preparedness, particularly due to the reliance on southwest monsoon. This paper examines historical trends from 1901 2022, highlighting significant anomalies changes identified through Pettitt test. The effectiveness of advanced machine learning techniques explored Artificial Neural Network-Multilayer Perceptron (ANN-MLP) enhancing forecasting accuracy compared with statistical methods. By integrating important climate variables—temperature, humidity, wind speed, precipitation into ANN-MLP model, its ability capture complex nonlinear relationships demonstrated. Additionally, analysis employs geo-statistical techniques, specifically Kriging, visualize spatial-temporal variability across different regions India. findings emphasize potential modern computational methods overcome traditional challenges, ultimately improving decision-making agricultural planning resource management face variability.

Язык: Английский

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Prediction of Postpartum Depression With Dataset Using Hybrid Data Mining Classification Technique

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Янв. 12, 2025

Postpartum Depression is a condition or state which usually affects the woman immediately after child birth. The birth of baby not only brings delighted emotions such as excitement, but also fear and anxiety may sometimes lead to depression. It period physical, emotional behavioral changes that happen in some delivery. Apart from chemical changes, there are many factors affect during pregnancy period. If PPD identified treated at earlier stages, it serious issues for mother child. therefore vital importance sift through any early stage prevent consequences. objective this study find out presence without getting worse. Data mining plays an important role health care industry with successful outcome. helps hidden patterns, trends anomalies large dataset make predictions. proposed system combined classification technique prediction postpartum depression uses Support vector machine, Artificial Neural Network Hybrid classifier algorithm produce best result.

Язык: Английский

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Enhanced hybrid classification model algorithm for medical dataset analysis

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Фев. 26, 2025

The medical industry generates a significant volume of data that requires effective machine learning models to make accurate predictions for public healthcare. Current Machine Learning (ML) techniques have limitations in feature extraction and classifier accuracy. In this paper using diabetes dataset classification, address these issues, propose novel algorithm enhances Hybrid Classification Model approach by integrating advanced methods tailored high-dimensional data. To handle Missing Values (MV) outliers, hybrid imputation combines K-Nearest Neighbor (KNN) Multivariate Imputation Chained Equations (MICE) is initially used preprocess the datasets. Feature (FE) performed Deep Extraction techniques, including Convolutional Neural Networks (CNNs) Autoencoders, followed Fusion create comprehensive set. For Selection (FS), introduce an Advanced Ensemble method employing Genetic Algorithm-Based (GAFS), Multi-Objective Evolutionary Algorithm (MOEA), Relief-Based Methods identify most relevant features. Finally, classification achieved through incorporating Classifier with Stacked Generalization (Stacking), Boosting, Bagging Network (NN) Enhancements attention mechanisms (AM) Transfer (TL). This integrated robustness accuracy classification. Comparing suggested current methods, experimental outcomes show considerable improvement (A), sensitivity (S), specificity (SP), reduced execution time (ET).

Язык: Английский

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