Category Archives: Uncategorized

Authors: Shaunak Das, Saumyajit Maulik, Subhodeep Sengupta, Shubhajit Mridha, Sagnik Banerjee, Supriyo Mondal, Anurima Majumdar, Antara Ghosal, Koushik Pal

Abstract: Explainable Artificial Intelligence (XAI) is now quickly becoming the game changer which makes it easier to get the answer of why the complex engineering models do what they do. Explainable Artificial Intelligence (XAI) refers to the AI systems which are designed to provide clear and understandable outcomes of their decisions, predictions and actions. XAI aims to make the Artificial Intelligence system more transparent and trustworthy for the users. This study aims to dive into how using XAI can be a better and efficient option for designing and implementing the microstrip patch antenna. Microstrip patch antennas are good options for wireless network design and implementation because they are small in size and cost effective. The traditional methods of designing those antennas tend to depend on the guess and round after round of try and check which produces delays and keeps us detached from what’s going on with the trade-offs in the designing process. By using the XAI into the design of microstrip patch antennas can make a huge impact on the optimization of design by providing us clear and interpretable insights into the design parameters and their impact on the working performance of the antenna. By applying XAI techniques such as feature importance analysis, feature selection, rule based systems designers can get a deeper understanding about the design parameters, rules and regulations

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: Tanush Nayan Shenai

Abstract: Superconductivity and electromagnetism are two groundbreaking technologies that are reshaping the future of engineering across key sectors. Superconductivity, which enables the conduction of electricity without resistance at very low temperatures, is driving breakthroughs in energy efficiency and magnetic field manipulation. When combined with electromagnetic and magnetic levitation (maglev) technologies, it allows for the creation of frictionless and powerful systems that push the boundaries of speed, efficiency, and sustainability. In transportation, maglev trains are facilitating unprecedented high-speed travel, offering faster, quieter, and more environmentally friendly transportation. In aerospace, these technologies are transforming spacecraft launches, enhancing propulsion and control. Additionally, in the defense sector, electromagnetic technology powers railguns, electromagnetic aircraft launch systems (EMALS), and other advanced weapons, improving defense and attack capabilities. This paper explores the specific technologies of superconductivity and electromagnetism, examining their applications in transportation, aerospace, and military sectors. By highlighting recent advancements and addressing ongoing challenges, it reveals the potential and feasibility of these technologies in shaping the future

DOI: https://doi.org/10.5281/zenodo.15709797

Published by: vikaspatanker

Authors: Dr. Bincy K Jose

Abstract: Wireless Electric Vehicle (EV) charging offers a seamless and efficient alternative to conventional plug-in systems, enabling user convenience, automation, and compatibility with dynamic and autonomous mobility. As the global transition to electrified transportation intensifies, the need for smart, contactless energy delivery solutions becomes imperative. This paper presents a comprehensive review of wireless EV charging technologies, focusing on power transfer methods, system design, control strategies, safety standards, and real-world applications. It integrates insights from academic literature and a student-developed prototype to explore the current state, challenges, and future direction of wireless EV charging infrastructure.

DOI: http://doi.org/ijsret.vol.11.issue3.150

Published by: vikaspatanker

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