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, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 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.

Language: Английский

---

Novel Architecture For EEG Emotion Classification Using Neurofuzzy Spike Net

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 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.

Language: Английский

---

Enhancing Secure Image Transmission Through Advanced Encryption Techniques

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 9, 2025

Secure image transmission over the Internet has become a critical issue as digital media increasingly vulnerable and multimedia technologies progress rapidly. The use of traditional encryption methods to protect content is often not sufficient, so more sophisticated strategies are required. As part this paper, an autoencoder-based chaotic logistic map combined with convolutional neural networks (CNNs) encrypt images. result optimizing CNN feature extraction, maps ensure strong while maintaining picture quality reducing computational costs. In addition Mean Squared Errors (MSE), entropy, correlation coefficients, Peak Signal-to-Noise Ratios (PSNRs), method shows higher performance. providing increased security, adaptability, effectiveness, results prove resilient many types attacks. study, CNNs systems improve data communication, transmission.

Language: Английский

---

Innovative Computational Intelligence Frameworks for Complex Problem Solving and Optimization

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 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.

Language: Английский

---

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, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 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.

Language: Английский

---

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

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 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.

Language: Английский

---

GreenGuard CNN-Enhanced Paddy Leaf Detection for Crop Health Monitoring

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Feb. 10, 2025

The GreenGuard: CNN-Enhanced Paddy Leaf Detection for Crop Health Monitoring initiative will create multiple future-oriented results. processing of agricultural imagery becomes revolutionized through the combination median filtering and Exponential Tsallis entropy Gaussian Mixture model (ExTS-GMM) advanced techniques initially. essential preprocessing operation delivers better quality data to Convolutional Neural Network (CNN) classifier which results in optimal performance outcomes. simple integration CNN classifiers launch an innovative age that more accurate efficient paddy leaf detection images. Deep learning features a enable it uncover complex structural details found both normal sick specimens. classifier's aptitude creates pathway execute precise assessment group into appropriate categories while extended database information rapidly. Effective implementation "GreenGuard" reshape conventional field crop health monitoring systems modern standards. Modern stakeholders can make choices about pest management along with disease control irrigation schedules because timely assessments from implemented system. new capabilities generated this empowerment system major yield growth enhance food safety protocols as well promote sustainable farming throughout farms globally.

Language: Английский

---

Renyi Entropy Predictive Data Mining And Weighted Xavier Deep Neural Classifier For Heart Disease Prediction

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Feb. 16, 2025

During the past few years, Frequent Pattern Mining (FPM) has received interest of several researchers that necessitate extracting items from transactions, and sequences datasets, clarifying heart disease diagnosis materializes commonly, recognizing specific arrangements. In this era with healthcare involving significant evolutions, unforeseeable movement enormous amount data concerning classification lead way to new issues in FPM, such as space time complexity. However, most research work concentrates on identifying patterns relating transpires frequently, where within every transaction were known a priori. To address present scenario, selecting predominant or frequent is essential using relevant FPM models. The primary objective enhance mining results reduce misclassification rate Cardiovascular Disease (CVD) dataset samples. This proposes novel method called Renyi Entropy Homogenized Weighted Xavier-based Deep Neural Classifier (REHWX-DNC) for prediction. tackle first challenge, Entropy-based (RE-FPM) algorithm proposed, which filters low-quality features function. handle second issue, HWX-DNC model designed assist minimizing by employing Swish activation A CVD synthesis can be analyzed obtain accuracy study, REGEX-DNC improved compared state-of-the-art methods. Some indicators, including prediction accuracy, time, level, F1-total, are considered calculate predictor, checking REHWX-DNC proposed efficient trustworthy predicting disease.

Language: Английский

---

Metaheuristic-Driven Optimization for Efficient Resource Allocation in Cloud Environments

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 7, 2025

Intrusion Detection Systems (IDS) play a pivotal role in safeguarding networks against evolving cyber threats. This research focuses on enhancing the performance of IDS using deep learning models, specifically XAI, LSTM, CNN, and GRU, evaluated NSL-KDD dataset. The dataset addresses limitations earlier benchmarks by eliminating redundancies balancing classes. A robust preprocessing pipeline, including normalization, one-hot encoding, feature selection, was employed to optimize model inputs. Performance metrics such as Precision, Recall, F1-Score, Accuracy were used evaluate models across five attack categories: DoS, Probe, R2L, U2R, Normal. Results indicate that XAI consistently outperformed other achieving highest accuracy (91.2%) Precision (91.5%) post-BAT optimization. Comparative analyses confusion matrices protocol distributions revealed dominance DoS attacks highlighted specific challenges with R2L U2R study demonstrates effectiveness optimized detecting complex attacks, paving way for adaptive solutions.

Language: Английский

---

Enhanced Bone Cancer Diagnosis through Deep Learning on Medical Imagery

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Feb. 12, 2025

Bone cancer, especially osteosarcoma, is an aggressive tumor with a highly complex histopathologic appearance that imposes considerable diagnostic difficulties. Although practical and efficient, traditional methods current deep learning models have class imbalance, fused pixel intensity distributions, tissue heterogeneity hinder efficiency. These problems emphasize the demand of more sophisticated frameworks specifically address distinct properties bone cancer histopathology images. To overcome these shortcomings, in this study proposes framework, IBCDNet, to alleviate limitations. Inspired by cutting-edge improvements architecture (e.g., like attention, residual connections, proposed Intelligent Learning-Based Cancer Detection (ILB-BCD) algorithm), framework combines different features from both public private datasets efficient way. This allows for strong feature extraction, better imbalanced data, thus precise classification. The model obtains state-of-the-art results 98.39% on Osteosarcoma Tumor Assessment Dataset, outperforming powerful baseline ResNet50, DenseNet121, InceptionV3. further affirms its robustness respective precision (97.8%), recall (98.1%), F1-score (98.0%) which shows remarkable improvement We present cost-effective scalable real-world clinical applications assist pathologists early detection accurate diagnosis cancer. Those important gaps identified addressed research contribute progress towards AI-driven healthcare global goals medicine enhanced patient outcomes.

Language: Английский

---