This research presents a novel hybrid framework for heart disease prediction, integrating advanced data preprocessing with ensemble learning to enhance diagnostic accuracy. The methodology begins with rigorous data cleansing to ensure reliability, followed by Synthetic Minority Over-sampling Technique (SMOTE) to balance class distribution. Outliers are identified and mitigated using the Z-score method, preserving data integrity. A unique Recursive Hybrid Feature Extraction (RHFE) strategy, combining filter and wrapper techniques, optimizes feature selection by reducing multicollinearity and enhancing model efficiency. Key predictive markers include age, chest pain type, maximum heart rate, ST depression induced by exercise, and major vessel count via fluoroscopy. The refined dataset is used to train an CatBoost -based ensemble model, achieving remarkable performance with 94.2% accuracy, 93.5% precision, 94% recall, and an outstanding ROC AUC score of 0.98. These results highlight the model's robustness and its potential for real-world clinical implementation in early heart disease detection and risk assessment

Attention Deficit Hyperactivity Disorder (ADHD) and Dyslexia are two widespread neurodevelopmental disorders affecting millions globally. Traditional diagnostic approaches, often reliant on subjective evaluation and standardized testing, can lead to delays in diagnosis and treatment. Electroencephalography (EEG), a non-intrusive technique that assesses the electrical activity occurring in the brain, provides significant insights into brain function, thanks to its high temporal resolution. With recent advancements in deep learning, it has become feasible to detect ADHD and Dyslexia by analyzing EEG signals more accurately. These technologies could improve early diagnosis, facilitating timely interventions and personalized treatment. This study presents a novel method for predicting Dyslexia and ADHD in children using EEG data alongside deep learning techniques. The research develops a predictive model capable of distinguishing between Dyslexia, ADHD, and typical development by identifying specific brain activity patterns. Our approach includes preprocessing EEG data, extracting essential features, and employing a deep learning model for classification. The results suggest that EEG signals can be successfully utilized for early detection of Dyslexia and ADHD, offering promising prospects for enhanced diagnostic and treatment approaches.

Forecasting used car prices is an important area of research. The demand for second-hand cars is increasing. This study offers a comparative analysis of different supervised Machine Learning (ML) algorithms for predicting costs. We evaluate Linear, Lasso, Ridge, XGBoost and Random Forest Regression models. Our findings show that Random Forest Regression performs well for individual car brands. It also significantly outperforms traditional regression models overall. This demonstrates the effectiveness of ensemble methods in handling complex data. We assessed each regression model’s performance using the R-Squared (R2) metric. Among all the models studied, Random Forest regression achieved the highest R² value of 0.90. Compared to earlier studies, our model considers more factors related to used cars and shows greater predictive accuracy.

Optical character recognition (OCR) technology is indispensable for converting and analyzing text from various sources into a format that is editable and searchable. Telugu handwriting presents notable challenges due to the resemblance of characters, the extensive character set, and the need to segment overlapping characters. To segment the overlapping characters, we assess the width of small characters within a word and segment the overlapping characters accordingly. This method is well suited for the segmentation of overlapping compound characters. To address the recognition of similar characters with less training periods we have used ResNet 18 and SqueezeNet models which have achieved character recognition rates of 95% and 94% respectively

Dyslexia, a neurodevelopment condition impacting reading and language abilities, presents notable difficulties in promptly identifying and implementing effective interventions Traditional methods for diagnosing dyslexia often rely on subjective assessments and standardized tests, leading to delays in recognition and support. This paper offers an extensive examination of how deep learning techniques are applied in the domain of detecting and intervening in dyslexia. The integration of deep learning algorithms into dyslexia research offers promising avenues for more accurate and timely identification of individuals at risk. By leveraging neural networks and advanced machine learning models, researchers have begun to explore novel approaches that analyze linguistic patterns, eye-tracking data, brain imaging, and behavioral markers associated with dyslexia. Furthermore, this paper discusses the potential of deep learning in tailoring personalized interventions for individuals with dyslexia. These interventions aim to adapt to the specific learning needs of each individual, providing targeted support and enhancing the effectiveness of remediation strategies. While highlighting the advancements made in utilizing deep learning for dyslexia, this review also addresses challenges, including data scarcity, model interpretability, and ethical considerations. Additionally, it proposes future research directions that emphasize collaborative efforts among researchers, educators, and technology developers to foster the development of robust and accessible tools for dyslexia assessment and intervention. © 2024 Taylor & Francis Group, London.

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