Vidya Jyothi Institute of Technology (VJIT) is a college in Hyderabad, India, established in 1999 by A.P. Jithender Reddy and V. Purushottam Reddy. It is located on the way to Chilkur Balaji Temple from Mrugavani National Park.
This study investigates the mechanical, tribological, microstructural, and physical characteristics of Al7075–Si3N4 composites reinforced with Si3N4 particles derived from red matta rice husk. Four composite specimens B1, B2, B3, and B4 were developed with reinforcement levels of 1 vol
Image gradient estimation is the utmost step in most image processing applications such as edge detection, feature extraction and other higher-level vision tasks, but derivative based operators also tend to increase impulse noise, resulting in unsteady gradient fields, and spurious edge responses. The proposed work presents a gradient stabilization model which utilizes the Gaussian pre-smoothing that follows gradient computation to eliminate noise extremity whilst maintaining structural data. The method is assessed with a salt and pepper noise which is controlled with a multi-scale Gaussian strategy (σ = 1.0, 1.5, 2.0) to determine the trade-off between denoising performance and feature preservation. Experimental results clearly reveal that both the quality of perceptions and gradient stability are improved substantially. Peak Signal-to-Noise Ratio (PSNR) rises to 29.07 dB at the higher levels of smoothing, compared to 18.03 dB of the noisy image and Structural Similarity (SSIM) rises to 0.8305 from 0.3073. The gradient variance (GV) declines to 0.0192 from 0.0445, which means that edge structures are better stabilized. The proposed method attained its highest possible Edge Preservation Index (EPI) of 0.7997 and the Gradient Signal-to-Noise Ratio (GSNR) of 2.38 among scales. The best trade-off between noise suppression and structural fidelity is obtained with σ = 1.5. The investigational outcomes undoubtedly illustrates that the Gaussian pre-smoothing process remarkably improves the robustness of gradientbased feature detection performance in impulse noise degraded imaging environs.
An innovative software-driven solution is designed to optimize traffic flow and expedite the transit of emergency vehicles through congested urban areas. The system leverages advanced machine learning models for both audio and image processing to dynamically adjust traffic signals and prioritize the swift passage of emergency vehicles. The audio processing module utilizes a 1D convolutional neural network (1DCNN) and the Librosa library to detect ambulance sirens in real-time audio data, triggering the activation of the visual processing module. The visual module, powered by the YOLOv5 object detection framework, identifies ambulances in live video feeds, prompting immediate adjustments to traffic signals. Extensive testing demonstrates the system’s efficacy in prioritizing emergency vehicles, with key metrics including signal transition times, ambulance detection accuracy, and overall traffic flow improvement. The holistic integration of audio and visual processing and dynamic traffic signal adjustment positions this solution as a robust and adaptable approach to addressing challenges in emergency vehicle navigation within urban traffic scenarios.
Abstract In this paper, a mixed-integer linear programming strategy is used to assess the economic viability of vehicle-to-grid electric bus fleets, considering advanced battery degradation models for thermal and calendar effects. The authors analyze uncontrolled charging, smart charging, and vehicleto-grid charging and show that while smart charging is cost-saving, vehicle-to-grid charging offers a higher economic potential than smart charging despite increased battery degradation. Moreover, under favourable market conditions, vehicle-to-grid charging revenue offsets not only the cost of energy but also the additional costs of degradation, making vehicle-to-grid charging highly profitable for investors. This paper offers a key decision-making tool for vehicle operators and demonstrates the need for advanced degradation modelling to reveal vehicle-to-grid's real economic potential.
Every year, many people around the world are progressively affected by the devastating conditions of health problems such as heart disease, respiratory infections, neurological dysfunction, cognitive stress, cancer, stroke, diabetes, etc., which lead to severe health complications and associated abnormalities. Thus, early health analytics are crucial, as they enable timely intervention with targeted therapies, potentially providing immediate relief and sustained long-term benefits that may slow disease progression. Due to the complex pathophysiological processes and heterogeneous clinical trials in various health conditions, there is a need for highly sensitive, multimodal biomarkers and effective investigative approaches to accurately detect and monitor patient health outcomes. Therefore, machine learning algorithms with various categories and techniques are considered for predicting outcomes, including prognosis, risk assessment, patient stratification, and disease monitoring. The flow of the proposed work is divided into three stages, as the first stage defines the importance of healthcare with case studies, followed by the traditional Machine Learning (ML) algorithms, traditional Deep Learning (DL) approaches, and modern DL techniques (TabNet and AutoInt) in the second stage. Finally, the experiments are implemented to justify the results. This work highlights the grouping of modalities by integrating molecular protein, chemical, and genetic biomarkers with emerging ML features. The results indicate a significant improvement in predicting the accuracy using the proposed methodology.
