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80 articles for “(Bi) LSTM”
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Research ArticleOpen accessEvery word in the titleEnhancing Video Tampering Detection Using Dynamic Temporal LSTM With Adaptive CNNGurpreet Kour Khalsa, Rakesh Ahuja, Rattan Deep AnejaIn the domain of information technology, video tampering detection has become hyper critical principally with the increase in deep fake as everyone is having affordable access to the internet. The long established methods lack in detecting the manipulated content specifically for temporal disordered and variant frames. In order to overcome such issues, the suggested innovative method encompasses Dynamic Temporal Warping … …Journal of Computer Science, Vol. 22, Issue 3 · 7 Mar 2026 · 2,015 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleDeep Learning-Based Spectrum Sensing in Cognitive Radio Networks using Stacked LSTM: Performance Analysis of SNR and BERKavitha Veerappan, Seetharaman GopalakrishnanRapid evolution of wireless communication technologies and emergence of 5G networks have addressed lack of spectrum resources, highlighting the need for innovative topologies to improve the spectrum utilization. Due to this reason, Cognitive Radio (CR) has evolved as an optimized solution to overcome these difficulties by allowing dynamic spectrum access, thereby reducing the under-usage of available spectrum. Considering this, precise … …Journal of Computer Science, Vol. 21, Issue 11 · 19 Dec 2025 · 2,219 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleA Deep Learning Approach for Telugu Domain Identification with Multichannel LSTM-CNNBuddha Hari Kumar, Chitra Perumal, Inakoti Ramesh Raja et al.The vast growth of textual data has ushered into the limelight, a plethora of applications in information retrieval and natural language processing (NLP). Proper extraction of information from text is heavily dependent on recognizing the thematic content, which becomes crucial in the tasks of document summarization, information extraction, question answering, machine translation, and sentiment analysis. The great complexity of this … …Journal of Computer Science, Vol. 21, Issue 9 · 26 Nov 2025 · 2,395 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleAssessing Bi-LSTM Model’s Performance in Identifying AI Generated Text in Digital MediaTinashe Crispen Garidzira, William Tichaona Vambe, Courage MatoboboThe rapid growth of AI-generated content from ChatGPT, Gemini, and many others poses significant challenges in digital media. There are increasing instances of text generated by machine learning models being indistinguishable from human-written text. This calls for an effective way of detecting and distinguishing text generated by AI from human text. Using a Cross-Industry Standard Process for Data Mining (CRISP-DM) … …Journal of Computer Science, Vol. 21, Issue 8 · 8 Oct 2025 · 2,416 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleA GUI-Enabled, Automated LSTM-Based Inverse Kinematics Pipeline for 6-DOF Robotic Arms Using MATLAB and CoppeliaSimWaleed Abdulrahman Saleh Al-Akwa, Mohammed Abdulwahab Ahmed DabaExisting deep learning-based inverse kinematics (IK) solutions often target specific robotic arms and require significant modifications when applied to different configurations. To support early-phase design and testing of 6-degree-of-freedom (6-DOF) robotic arms, this study presents a fast and adaptable IK solution through a user-friendly interface. Unlike traditional numerical methods that are computationally intensive, sensitive to initial conditions, and may not … …Journal of Mechatronics and Robotics, Vol. 9, Issue 1 · 26 May 2025 · 8,252 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleStock Closing Price Forecasting Using LSTM, Sentiment Analysis, Kalman FilterAssel Abdildayeva, Galymzhan Nurtugan, Guldana Taganova, Ardak AkhmetovaThe article explores the problem of predicting the closing prices of stocks using an innovative approach that combines methods of machine learning, sentiment analysis and noise filtering. The focus is on the development and testing of a comprehensive model that integrates recurrent neural networks (LSTM) for time series analysis, the GPT-3 language model for processing and analyzing textual data from … …Journal of Computer Science, Vol. 20, Issue 11 · 4 Sep 2024 · 6,262 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleSales Forecasting Models: Comparison between ARIMA, LSTM and ProphetDmitry BrykinSales forecasting is crucial for business planning and resource allocation. Data-driven approaches have become popular in this field. This study compares the performance of three forecasting models: Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM) and Prophet within the context of specific sales categories derived from acquiring data provided by a bank. This study uses a time series dataset … …Journal of Computer Science, Vol. 20, Issue 10 · 1 Aug 2024 · 7,981 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleAspect Based Sentiment Analysis Using Self-Attention Based LSTM Model with Word EmbeddingVimalkumar B. Vaghela, ZishanHaider Y. Noorani, Kalpesh Patel et al.Sentiment analysis is in advance more attention for research due to incremental convention in affairs of state, online marketing and social networking. Users afford their opinions for exacting object as reviews. Analysts categorize these reviews into prejudiced information. Consequently sentiment pulling out is the development of take out human awareness from amorphous text reviews. Extracted sentiment will assist to recognize … …Journal of Computer Science, Vol. 20, Issue 10 · 29 Jul 2024 · 6,512 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleForecasting of Multistep Multivariate Financial Data Through GSO Algorithm Infused Vanilla LSTM ModelNikhitha Pai, Ilango Velchamy, Nithya B RameshPredicting time series in the financial domain requires using models that can do sequence processing tasks. One such model is the LSTM model which belongs to the family of deep learning networks. The performance measure of LSTM in a regression task is to bring down the error in the predicted values. In this study, a novel fused LSTM model integrating … …Journal of Computer Science, Vol. 19, Issue 7 · 10 Jul 2023 · 6,074 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleBoosting Arabic Named Entity Recognition with K-Fold Cross Validation on LSTM and Bi-LSTM ModelsHamid Sadeq Mahdi Alsultani, Ahmed H. AliwyNamed-Entity-Recognition(NER) is one of the most important Information-Extraction (IE) use cases, whichis used to improve the performance of Natural Languages Processing (NLP) tasks,such as Relation-Extraction (RE), Question-Answering (QA). Recently, Arabic NER is tackled in differentways by researchers. In this study, we assess the performance of two widelyused models, namely, LSTM and Bi-LSTM on the NER task in the Arabic languageand … …Journal of Computer Science, Vol. 18, Issue 9 · 6 Sep 2022 · 6,317 accessesView PDFCite