Daily Archives: June 13, 2025

Authors: Mohanprasath. , Student-M.E, Structural engineering

Abstract: In today’s world, concrete serves a crucial role in the development of every infrastructure and people are also starting to migrate from rural to urban areas. This situation necessitates the development of infrastructure in the urban areas, mostly in the vertical direction form of high-rise structures that utilize enormous columns, which take up more room and have a less appealing aspect. Concrete Filled Steel Tubular (CFST) columns are one of several ways developed by the building industry to address these issues. Huge areas may be used since CFST Columns minimize the size of the large columns. CFST columns are becoming common in construction, particularly for high-rise structures. The structural performance of concrete-filled CFST columns are discussed in many literatures, while CFST columns utilizing ceramic waste powder and glass fiber with replacement of reinforcement is not mentioned in any of the literatures. This study is about the performance of CFST columns with ceramic waste powder replacing cement and GI welded wire mesh replacing reinforcement and adding the glass fiber. The properties of the materials used in CFST columns is studied.

DOI:

 

Published by: vikaspatanker

Authors: Mohit Kumar

Abstract: Surgical strikes are high- precision military operation designed to neutralize specific targets while mimizing collateral damage. However, these operations are fraught with challenges such as intelligence inaccuracies, hostile terrain, real- time coordination issues, and political sensitivities. The integration of Artificial intelligence (AI) offers a transformative solution to these challenges. This paper explores the multifaceted challenges encountered during surgical strikes and examines the roles of AI in enhancing tactical precision and decision- making capabilities.

Published by: vikaspatanker

Authors: Vikash Sharma, Dr. Ramesh Patil

Abstract: The advancement of artificial intelligence (AI) has given rise to two major approaches: traditional machine learning (ML) and deep learning (DL). While traditional ML relies on feature engineering and structured learning approaches, deep learning automates feature extraction through artificial neural networks. This paper explores the differences between these methods, compares their performance across domains such as image recognition, natural language processing, and financial forecasting, and evaluates their advantages and limitations. Experimental results and literature reviews indicate that deep learning excels in handling large datasets and complex patterns, whereas traditional ML is more suitable for smaller datasets with structured features.

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: Jameer Shaikh, Dr. Usha B Shete, Dr. A. A. Khan, Dr. R. S. Deshpande

Abstract: Early and accurate detection of fruit diseases is critical for minimizing crop losses and ensuring food security. This study in- troduces a novel automated diagnostic framework that leverages hyperspectral imaging combined with deep convolutional neural networks to detect and classify common diseases affecting apples, including blotch, rot, and scab. By analyzing spectral reflectance patterns from 360 nm to 1000 nm, the proposed method identifies subtle biochemical changes in fruit tissues before visual symp- toms manifest. Extensive laboratory experiments demonstrate that the system achieves an overall classification accuracy of 93.7%, outperforming traditional RGB-based image analysis techniques. Furthermore, field trials conducted in commercial orchards validate the robustness and real-world applicability of the system, revealing a 28% reduction in false positive detections and a 35–40% potential decrease in yield losses through timely intervention. The integration of hyperspectral data with deep learning enables a cost-effective, non-destructive, and scalable solution for precision agriculture, supporting proactive crop management and sustainable farming practices.

 

 

Published by: vikaspatanker

Authors: Konka Kishan, Kondoju Prem Kumar, Shaik Feroz Pasha, Ajmeera Sagar Naik

 

Abstract: The recent growth of Deep learning has transformed the area of Image Super-Resolution (ISR) which enables the reconstruction of high-quality images from low-resolution images. The improvement of low quality images to high-quality resolution images. The complete guide provides an in-depth overview of the advanced methodologies and applications of ISR using deep learning. We discuss the basic of ISR, models and algorithms of ISR namely, Generative Adversarial Networks (GANs), Convolutional Neural Networks (CNNs) and attention-based models. We further discuss the applications of ISR such as Medical Imaging, Surveillance, Astronomy, and Autonomous driving. This compiled resource is intended to be a one-stop reference guide to the intricacies of ISR using deep learning and it's many prospects for real-world applications for researchers, practitioners, and students.

DOI: 10.61137/ijsret.vol.11.issue3.149

 

Published by: vikaspatanker

Authors: P.Lavanya, Bibhuti Bhusan Rath, A. Lashya, K. Kirankumar, B. Likitha

 

Abstract: Ensuring consistent and efficient energy harvesting from solar photovoltaic (PV) systems remains a challenge due to unpredictable environmental conditions. To address this, a hybrid Maximum Power Point Tracking (MPPT) technique is presented, combining a conventional perturb-and-adjust method with fuzzy logic-based control. This approach dynamically modifies the control signal for a step-up converter, allowing the PV array to maintain optimal power output. The system's performance was analysed using MATLAB/Simulink under both stable and varying sunlight conditions. Results confirm that the hybrid controller delivers better efficiency, quicker response to changes, and reduced output fluctuations when compared to traditional MPPT strategies. These findings highlight its potential for integration into intelligent solar energy systems.

DOI: http://doi.org/ijsret.vol.11.issue3.148

 

Published by: vikaspatanker

Authors: Ms. Neekita Singh, Dr.Jasbir Kaur, Mrs.Sandhya Thakkar

Abstract: This paper presents a comprehensive framework combining data science, geospatial analysis, and interactive visualizations to study crimes against women in India. Analyzing crime trends includes three key components: using data science, an interactive Power BI dashboard visualization [6], and a Python-based crime hotspot mapping application using Streamlit. The crime hotspot app allows users to search for any location in India and view crime data displayed on a map, potentially aiding in crime prevention by providing real- time awareness [5]. By integrating these tools, the framework offers a multi-faceted approach to crime analysis, enabling deeper insights into spatial and temporal crime patterns. The study aims to assist policymakers, law enforcement, and the public in understanding and mitigating crime.

DOI: http://doi.org/ijsret.vol.11.issue3.147

 

Published by: vikaspatanker