G. M. Reddy Research Foundation

The predominant means of communication is speech; however, there are persons whose speaking or hearing abilities are impaired. Communication presents a significant barrier for persons with such disabilities. The primary objective of this work is to develop an efficient solution with machine learning for the speech and hearing impairment community with the help of AI. This study helps in the development of future technologies in the enhancement of communication accessibility. In the proposed work, a machine learning approach for the American Sign Language alphabet using image detection and processing techniques are utilized. The experiment was conducted using an American Sign Language recognition dataset obtained from Kaggle, which provides images of hand Gestures of the alphabet. The dataset was divided into training and testing subsets in the ratio of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$80: 20$</tex>, where 80 % of the data was used in training the model and the remaining 20 % was designated for testing the model. The dataset was normalized using denoising filters and proceeded with Canny edge detection to highlight sharp edge features. From the edge detected image, Histogram of Oriented Gradient features were extracted to detect the letter and shape information. These extracted features are then fed into the classifiers: Support Vector Machine, Random Forest, and KNearest Neighbors. The study was analyzed by experimental results, which show that the Support Vector Machine classifier achieved the highest accuracy of 93.33 %, closely followed by Random Forest and K-Nearest Neighbors. The analysis confirms that applying edge-based preprocessing along with the Histogram of Oriented Gradients feature extraction enables an effective classification of American Sign Language alphabets. This Comparative study provides a solid foundation for developing low-complexity, high-accuracy sign language recognition systems.

With the growing emphasis on sustainability and circular economy principles in industrial sectors, the effective utilization of waste-derived and abundantly available natural resources has become imperative. Renewable lignocellulosic fibers such as jute fiber (JF), banana fiber (BaF), coconut coir (CC), and pineapple leaf fiber (PALF) serve as viable reinforcements for developing eco-friendly polymer composites. In this study, hybrid natural fiber (NF) composites were fabricated using the hand lay-up method with varying weight percentages of alkaline-treated JF, CC, BaF, and PALF. Alkaline treatment improved fiber–matrix interfacial bonding by reducing hydroxyl groups and enhancing surface roughness, which significantly contributed to improved mechanical and thermal performance. Mechanical testing revealed that the composite containing 20% JF, 20% CC, and 10% PALF exhibited superior properties, achieving a tensile strength of 85.8 MPa, flexural strength of 134.5 MPa, impact strength of 23.3 J/m2, and hardness of 72.6 Shore D. Fourier transform infrared spectroscopy (FTIR) confirmed successful alkaline treatment through reduced hydroxyl group intensity and improved fiber–matrix compatibility. Atomic force microscopy (AFM) further revealed increased surface roughness (Ra = 31.43 nm), indicating enhanced interfacial adhesion. To predict mechanical performance, machine learning (ML) regression models including linear regression, multilinear regression, decision tree, and random forest were developed using experimental data. Among these, the random forest model demonstrated the highest predictive capability, achieving R2 values of 0.968 for tensile strength, 0.939 for flexural strength, 0.941 for impact strength, and 0.962 for hardness, along with the lowest error metrics (mean absolute error and root mean squared error). These results highlight its suitability for accurate prediction of mechanical properties in fiber-reinforced composites. Furthermore, dynamic mechanical analysis (DMA) revealed enhanced viscoelastic behaviour in treated fiber composites, while thermogravimetric analysis (TGA) confirmed excellent thermal stability up to 690 °C in JF/CC/PALF-reinforced samples.

Coronary artery disease and its aftereffects are among the leading causes of death worldwide in the modern era, despite the fact that death rates can be considerably lowered with timely detection and thorough prevention. A variety of artificial intelligence methods are employed in the prediction of coronary artery disease. We provide a medical recommendation system that effectively helps the early detection and management of heart disease by merging many classifiers. Fast Fourier transformation is used in conjunction with ensemble learning techniques in electronic health systems to improve the diagnosis of heart disease and medication use. The accuracy and applicability of the proposed strategy are compared with established methods for predicting cardiac disease. The proposed system supports cardiovascular risk prediction and recommendation functions, and it is thought that the ensemble model facilitates accurate prediction and the medical suggestion process. Additionally, time series analysis of the patient's data is made simpler by rapid Fourier transformation. Furthermore, the findings that show the ensemble machine learning model outperforms the individual classifiers are explained by the assumption that the proposed system's input dataset is free of noise and missing values.