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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Novel Architecture For EEG Emotion Classification Using Neurofuzzy Spike Net

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

Опубликована: Янв. 7, 2025

Emotion recognition from Electroencephalogram (EEG) signals is one of the fastest-growing and challenging fields, with a huge prospect for future application in mental health monitoring, human-computer interaction, personalized learning environments. Conventional Neural Networks (CNN) traditional signal processing techniques have usually been performed EEG emotion classification, which face difficulty capturing complicated temporal dynamics inherent uncertainty signals. The proposed work overcomes challenges using new architecture merging Spiking (SNN) Fuzzy Hierarchical Attention Membership (FHAM), NeuroFuzzy SpikeNet (NFS-Net). NFS-Net takes advantage SNNs' event-driven nature signals, are treated independently as asynchronous, spike-based events like biological neurons. It allows patterns data high precision, rather important correct recognition. local spiking feature SNNs encourages sparse coding, making whole system computational power energy highly effective it very suitable wearable devices real-time applications.

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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Innovative Computational Intelligence Frameworks for Complex Problem Solving and Optimization

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

Опубликована: Янв. 9, 2025

The rapid advancement of computational intelligence (CI) techniques has enabled the development highly efficient frameworks for solving complex optimization problems across various domains, including engineering, healthcare, and industrial systems. This paper presents innovative that integrate advanced algorithms such as Quantum-Inspired Evolutionary Algorithms (QIEA), Hybrid Metaheuristics, Deep Learning-based models. These aim to address challenges by improving convergence rates, solution accuracy, efficiency. In context a framework was successfully used predict optimal treatment plans cancer patients, achieving 92% accuracy rate in classification tasks. proposed demonstrate potential addressing broad spectrum problems, from resource allocation smart grids dynamic scheduling manufacturing integration cutting-edge CI methods offers promising future optimizing performance real-world wide range industries.

Язык: Английский

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Stability Analysis and Numerical Approach to Chemotherapy Model for the Treatment of Lung Cancer

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

Опубликована: Март 4, 2025

This paper introduces and examines a mathematical model aimed at understand- ing the efficacy of chemotherapy in treating lung cancer. Through utilization differential equations, we delve into intricate interplay between healthy cells, tumor damaged impact chemotherapy. Our analytical deductions are substantiated through extensive numerical simulations, revealing profound effectiveness curbing progression. Addition- ally, stability analysis is discussed simulations suggested for that have presented. These findings not only contribute significantly to realm cancer research but also hold substantial promise therapeutic advancements. Moreover, insights gleaned from this study poised enrich educational endeavors pertaining modeling, thereby fostering deeper understanding its underlying dynamics treatment strategies.

Язык: Английский

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