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13 articles for “Transformer boosting technique”
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Comparison Analysis of Transformer Boosting and Induced Degeneration Topology Design of LNA for Millimeter Wave Frequency Range Using Polymeric Substrates
Abstract: In this study, a comparison between transformer boosting and source degeneration LNA topologies is conducted using two polymeric substrates—Polyimide (PI) and Liquid Crystal Polymer (LCP)—for millimeter-wave (mmWave) applications. With growing interest in flexible and high-frequency electronics, polymeric materials offer unique advantages such as low dielectric constants, mechanical flexibility, and thermal stability. The analysis explores gain, return loss, and noise figure performance while evaluating the influence of dielectric properties on the …
Published in International Journal of Electrical and Communication Engineering Technology · Vol. 3, Issue 2, 2025 · pp. 1–24 Read article
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Online Skill-boost Platform (Eduverse)
Abstract: Eduverse is a cutting-edge, web-based educational platform developed to transform the way coding is taught and learned. It focuses on making programming more accessible, engaging, and effective by combining gamification techniques, AI-powered personalized tutoring, and hands-on coding challenges inspired by real-world problems. Unlike traditional learning environments that often rely on static lessons and limited interaction, Eduverse offers a dynamic and adaptive experience tailored to individual learning styles and skill levels. …
Published in Journal of Open Source Developments · Vol. 12, Issue 2, 2025 · pp. 01–06 Read article
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IOT and algorithmic intelligent motor health monitoring as well as maintenance prediction
Abstract: Manufacturing, transportation, and energy systems rely largely on industrial electric motors, and their untimely failure can result in expensive downtime, safety hazards, and decreased operational efficiency. The majority of traditional motor maintenance procedures rely on reactive methods or routine inspections, which frequently miss early-stage problems and lead to needless maintenance or unexpected breakdowns. This project offers an Intelligent Motor Health Monitoring and Predictive Maintenance System that combines Internet of Things …
Published in International Journal of Electrical Machine Analysis and Design · Vol. 4, Issue 1, 2026 · pp. 28–37 Read article
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Eye Disease Classification Using K-means Clustering Algorithm and Ensemble Classification Approach
Abstract: In this study, we present a comprehensive approach for the classification of eye diseases, specifically targeting normal, cataract, glaucoma, and diabetic retinopathy conditions. This research uses a dataset from Kaggle, which provides a wide and varied collection of retinal images to ensure good representation. The methodology encompasses advanced image processing and machine learning techniques to ensure accurate diagnosis and prediction. The preprocessing phase involves a series of image enhancement techniques …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 3, Issue 2, 2025 · pp. 15–27 Read article
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Development and implementation of Solar-Thermoelectric Hybrid Energy Harvester
Abstract: In today’s consumer-oriented and technology-driven market, researchers are increasingly emphasizing the need to harvest energy from ambient and renewable sources to support sustainable power generation and to minimize dependence on conventional energy resources such as batteries and fossil-fuel-based electricity. The rising deployment of portable electronics, wireless sensor networks, and Internet of Things (IoT) devices has created an urgent demand for compact, low-power, long-life, and maintenance-free energy solutions. In many real-world …
Published in International Journal of Machine Systems and Manufacturing Technology · Vol. 4, Issue 1, 2026 Read article
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Designing an AI-Based Platform for Stock Market Prediction
Abstract: The AI-Based Platform for Stock Market Prediction is an advanced tool designed to forecast stock prices and market trends using artificial intelligence. This platform combines machine learning algorithms, real-time financial data, and sentiment analysis to provide investors with actionable insights. The platform uses advanced predictive techniques like Long Short-Term Memory (LSTM) networks and Gradient Boosting Machines to generate precise and reliable forecasts. Additionally, it incorporates interactive visualizations and portfolio optimization …
Published in E-Commerce for Future & Trends · Vol. 12, Issue 3, 2025 · pp. 14–19 Read article
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Machine Learning Based House Price Forecasting
Abstract: This research endeavours to craft a predictive model leveraging machine learning to estimate the market value of houses in Delhi. By integrating Python and its powerful libraries, pandas for data processing, Plot for interactive visualizations, scikit-learn for implementing machine learning algorithms, XGBoost for boosting the model's prediction accuracy, and to evaluate the model's performance cross-validation techniques are used. An interactive user interface is created using a Flask web application to …
Published in Current Trends in Information Technology · Vol. 14, Issue 1, 2024 · pp. 5–11 Read article
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The Role of IoT in Sustainable Agriculture: Leveraging Big Data for Precision Farming
Abstract: Precision farming combined with the Internet of Things (IoT) is transforming the agricultural industry by boosting productivity and encouraging sustainable practices.. This paper explores the transformative impact of IoT technologies on modern agriculture, focusing on how big data analytics can be leveraged to optimize farming practices, reduce waste, and conserve resources. The Internet of Things (IoT) offers real-time data on a range of agricultural characteristics, including soil moisture, temperature, humidity, …
Published in Journal of Instrumentation Technology & Innovations · Vol. 14, Issue 2, 2024 Read article
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Advances in Analytical Techniques for Water Quality Assessment
Abstract: Assessing the quality of water is essential for preserving ecological balance and public health. This study evaluates new developments in analytical methods that are meant to improve the accuracy, effectiveness, and reach of water quality monitoring. While fundamental, conventional procedures like spectrophotometry and chromatography have drawbacks in terms of sensitivity and real-time capability. By allowing quick and precise pollutant and contaminant identification, emerging technologies such as sensor networks, remote sensing, …
Published in International Journal of Environmental Noise and Pollution Control · Vol. 2, Issue 1, 2024 · pp. 1–7 Read article
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Hybrid Numerical–Artificial Neural Network Study of Chemically Reactive MHD Nanofluid Flow Incorporating Thompson–Troian Slip and Stefan Blowing
Abstract: Boundary layer behaviour in chemically reactive nanoliquid is significantly affected by surface conditions, magnetic fields, heat and mas transfer mechanisms. However, the combined impact of Stefan blowing and nonlinear Thompson–Troian slip under inclined magnetic fields remains mostly unexplored, particularly in mixed convection flows. In this research work, the flow of a chemically reactive nanoliquid over a permeable surface is investigated by considering the Troian slip, inclined magnetic fields, Stefan blowing, …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 409–425 Read article
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Automated Machine Learning System for Model Selection and Hyperparameter Optimization
Abstract: The proliferation of machine learning applications in various scientific and industrial domains has given rise to an urgent need for developing principled, automated techniques for optimal architecture selection and hyperparameter tuning for machine learning models without human expert intervention. In this paper, we introduce the Automated Machine Learning System for Model selection and hyperparameter Optimization (AMLSMO)—a state-of-the-art, all-encompassing AutoML system that combines the power of meta-learning-based warm-starting, Bayesian Optimization with …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 15–23 Read article
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Deep Learning Enhanced Compressive Sensing for Wireless IoT Data Optimization and Weather Monitoring.
Abstract: This research explores the application of deep learning and compressive sensing in order to optimize data traffic in non-orthogonal multiple access (NOMA)-based wireless internet of things (IoT) networks and weather monitoring. Such a framework would be very effective and overcome pilot attacks and reconstruction losses for secure data transmission. In this regard, a strong communication model has been adopted based on power-domain NOMA for simultaneous wireless transmission by multiple IoT …
Published in International Journal of Satellite Remote Sensing · Vol. 2, Issue 2, 2024 · pp. 20–36 Read article
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A SHAP - Enhanced Voice-Based Conversational Agent for Agriculture Using BERT
Abstract: The integration of advanced artificial intelligence technologies into modern agriculture has become increasingly important for narrowing the persistent knowledge gap faced by farmers, especially in regions with limited access to expert advisory services. While state-of-the-art language models such as BERT (Bidirectional Encoder Representations from Transformers) demonstrate exceptional performance in understanding and generating natural language, their opaque “black-box” nature often limits user confidence, trust, and widespread adoption. Farmers may hesitate to …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 Read article