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7 articles for “Gated Recurrent Unit”
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LightGBM-Based Grid Demand Estimation and Prediction for Intelligent Electricity Load Forecasting
Abstract: Correctly predicting electricity demand in emerging energy markets like India is the most effective way to maintain grid stability. This is particularly important. This article offers a comprehensive data- based structure that captures both the broad temporal dynamics and regional diversity. This technique utilizes a large-scale multi-dimensional feature space that includes temporal indicators, autoregressive lag terms, sliding-window rolling statistics, and state-level administrative markers. Our systematic benchmarking approach involves the use …
Published in Journal of Power Electronics and Power Systems · Vol. 16, Issue 3, 2026 Read article
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Detecting Phishing Websites Using Hybrid Methodologies
Abstract: In the digital era, personal information theft has become a widespread and increasingly severe crime. Cybercriminals, often known as hackers, use deceptive strategies, with phishing websites being a major method for stealing confidential data. These fake websites imitate legitimate ones, tricking users into revealing sensitive personal and financial information, which has led to a rise in fraud cases. To address this escalating threat, a comprehensive research paper is proposed. This …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 3, 2024 · pp. 59–65 Read article
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Efficient Machine Defect Detection with Sugeno Fuzzy Membership and GRU Networks for Robust Industrial Automation
Abstract: Machine fault detection is of immense significance in industrial automation to achieve efficient operations, reduced downtime, and reduced economic losses. Sugeno fuzzy logic and Gated Recurrent Unit (GRU) networks are used in this research to provide a new hybrid solution that addresses problems such as noisy data, evolving defect patterns, and real-time detection. To improve readability and reliability, the Sugeno fuzzy logic unit preprocesses fuzzy and uncertain input data into …
Published in Journal of Mechatronics and Automation · Vol. 12, Issue 2, 2025 · pp. 17–26 Read article
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Learning Data Structures: Key to Good Programming
Abstract: Data structures are the most crucial feature of good programming and are needed to solve hard computational problems. This model makes use of two different recurrent neural network architectures, specifically long short-term memory (LSTM), and gated recurrent unit (GRU) networks. It explains how selecting and using the correct data structures may speed up computations, optimize memory, and scale code. How data structures and algorithms relate and how to think about …
Published in International Journal of Data Structure Studies · Vol. 4, Issue 1, 2026 · pp. 29–39 Read article
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Optimized Sentiment Analysis Through TextBlob and Hybrid RNN Models
Abstract: In today’s world, analyzing people’s feelings from what they write online has become very important. This is because there is a large amount of content created by users. To make this analysis accurate and fast, we present a method. This method uses a mix of two approaches: one that looks up words in a dictionary and another that uses computer learning. TextBlob is an affordable tool for getting an initial …
Published in International Journal of Computer Science Languages · Vol. 4, Issue 1, 2026 · pp. 29–28 Read article
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Recognition of Thyroid Nodules Using Hierarchical Temporal Awareness Networking Scanning
Abstract: Contrast-enhanced ultrasonography (CEUS), a preferred imaging technique for thyroid nodule diagnosis, is able to reveal the vascular distribution within a thyroid nodule right away. With the aim of mining pathologically-related enhancing dynamics and creating predictions in one step without taking into account a native diagnostic dependency, a number of learning-based algorithms have recently been created. In clinics, separating benign from malignant nodules is always done before identifying pathological types. In …
Published in Trends in Opto-electro & Optical Communication · Vol. 12, Issue 2, 2022 · pp. 31–35 Read article
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Hoax News Detection Using Convolutional Neural Network
Abstract: In this paper our objective is to build a classifier that can predict whether a piece of news is Hoax or not based only its content, thereby approaching the problem from a purely deep learning perspective by technique models like LSTM(GRU), Naive Bayes and CNN. We will show the difference and analysis of results by applying them to the dataset Hoax_news which is available on kaggle.com. We found that the …
Published in Journal of Operating Systems Development & Trends · Vol. 7, Issue 2, 2020 · pp. 19–23 Read article