Brain Tumor Detection using Machine Learning for Specification and Accuracy of the Brain Tumor
A Hybrid Quantum-Classical Approach for Enhancing Machine Learning Algorithms in Noisy Environments
Credit Scoring and Approval using Intelligent Machine Learning Systems
Precision Thyroid Detection using Explainable ML
AI and Machine Learning Applications in Green Technology: Sectoral Innovations for Environmental Sustainability
A Review of Predicting Agriculture Yields Based on Machine Learning using Regression and Deep Learning for Diversified Atmosphere and Crops in India
A Comprehensive Study on Signature Recognition using Python, AI and ML
A Machine Learning Approach to Real Time Vehicle Safety System
Autistic Continuum and Pervasive Developmental Disorder Employing Machine Learning and Image Processing
A Web-Based Placement Portal with Web Data Mining for Recruiter Insights
Autistic Continuum and Pervasive Developmental Disorder Employing Machine Learning and Image Processing
A Machine Learning Approach to Real Time Vehicle Safety System
Brain Tumor Detection using Machine Learning for Specification and Accuracy of the Brain Tumor
AI and Machine Learning Applications in Green Technology: Sectoral Innovations for Environmental Sustainability
Precision Thyroid Detection using Explainable ML
Brain tumors are among the most critical and life-threatening neurological conditions, affecting individuals across all age groups. According to recent global cancer reports, over 308,000 new cases of brain and central nervous system tumors are diagnosed annually, underscoring the urgent need for early and accurate detection. Although MRI and CT scans remain the gold standard for diagnosis, manual interpretation is time-consuming and depends heavily on radiologists' expertise, resulting in subjective variability and delayed clinical decision-making. This study introduces a comprehensive AI-powered framework that integrates preprocessing, data augmentation, CNN-based tumor segmentation, and transfer-learning-based classification to automate brain tumor detection. Using U-Net for segmentation and ResNet/DenseNet architectures for classification, the proposed system demonstrates strong performance with a Dice score of 0.91, an IoU of 0.86, a classification accuracy of 96.3%, and an average F1-score of 0.95 across glioma, meningioma, and pituitary tumor types. Evaluation algorithms, deployment architecture, and workflow diagrams are provided to ensure methodological transparency. The findings confirm the significant role of AI in increasing diagnostic accuracy, reducing interpretation time, and supporting clinical decision-making. The framework offers a reliable, efficient, and scalable solution for real-world medical imaging applications.
Hybrid quantum-classical computing offers a promising pathway to harness quantum advantages while mitigating the hardware limitations of noisy intermediate-scale quantum (NISQ) devices. This work proposes a modular hybrid architecture that combines classical preprocessing, quantum-enhanced feature mapping through parameterized quantum circuits (PQCs), and adaptive classical optimization. The framework is designed to improve robustness and generalization of machine learning models in noisy environments, addressing critical issues such as overfitting, noise sensitivity, and hardware-induced errors. Experimental evaluations were conducted on benchmark datasets, including Iris, reduced-dimension MNIST, synthetic noisy XOR, Wine Quality, and Breast Cancer Wisconsin. The proposed model was tested under multiple noise models, such as Gaussian, salt-and-pepper, Poisson, and quantum-specific errors (bit-flip, phase-flip, and depolarizing), using 5-fold cross-validation and repeated trials for statistical rigor. Key performance metrics included Classification Accuracy (CA), Robustness Index (RI), and Training Stability (TS), with results reported as mean ± standard deviation and supported by paired t-tests (p < 0.05). Results demonstrate that the hybrid model consistently outperforms classical support vector machines (SVM) and standalone variational quantum classifiers (VQC) in noisy conditions, achieving smaller accuracy degradation and higher stability. The combination of quantum- enhanced feature mapping, optimizer synergy, and modular design is shown to be central to its resilience.
Credit card approval is a critical task for financial institutions that must balance the need for customer acquisition with risk management. Traditional rule-based methods typically lack the flexibility and adaptability of data-driven approaches. This paper presents a machine learning-based framework for predicting credit card approvals using various classification algorithms. Models such as logistic regression, decision trees, random forest, and gradient boosting are evaluated on a publicly available dataset. Performance is measured using accuracy, precision, recall, and F1-score. Results show that machine learning models significantly enhance approval prediction performance and offer valuable insights into feature importance.
Thyroid disorders occur due to hormonal imbalance imbalances involving triiodothyronine (T3), thyroxine (T4), and thyroid-stimulating hormone (TSH), which affect imbalances, metabolic regulation and overall body function. Early and accurate detection of thyroid dysfunction is crucial to minimize complications and ensure timely treatment. This paper presents a comparative machine learning framework for classifying thyroid diseases such as hypothyroidism, hyperthyroidism, and euthyroidism. A real-world dataset was preprocessed to remove missing values and normalized for efficient model training. Various supervised algorithms, Logistic Regression, affect logistic regression, Random Forest, logistic regression, random forest, Support Vector Machine random forest, support vector machine (SVM), Naïve Bayes, k- Nearest Neighbor support vector machine nearest neighbors (KNN), and artificial neural network nearest neighbors (ANN), were implemented using Python. The performance of each model was evaluated using metrics such as accuracy, precision, recall, and F1-score. Results show that the ANN achieved the highest accuracy of 96.75%, followed by the logistic regression and random forest models. The proposed model demonstrates that AI-based approaches can effectively classify thyroid dysfunctions, providing an efficient diagnostic support system for healthcare professionals.
The growing severity of global environmental challenges necessitates intelligent and sustainable solutions. This paper examines the role of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing green technologies across domains such as renewable energy, waste management, agriculture, and smart cities. It highlights how AI/ML enable pollution monitoring, energy optimization, waste reduction, and efficient food production, supported by real-world applications. The paper also discusses challenges related to ethics, data privacy, and equitable access, and outlines future research directions for advancing sustainable development through AI-driven innovations.
