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42 results for “Word Accuracy (WA)”
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Research ArticleOpen accessIn the keywordsA Hybrid Method for Automatic Speech Recognition Performance Improvement in Real World Noisy EnvironmentUrmila Shrawankar, Vilas ThakareIt is a well known fact that, speech recognition systems perform well when the system is used in conditions similar to the one used to train the acoustic models. However, mismatches degrade the performance. In adverse environment, it is very difficult to predict the category of noise in advance in case of real world environmental noise and difficult to achieve … …Journal of Computer Science, Vol. 9, Issue 1 · 27 Feb 2013 · 7,754 accessesView PDFCite
Research ArticleOpen accessIn the keywordsAn Effective System for Acute Spotting Aberration in the Speech of Abnormal Children Via Artificial Neural Network and Genetic AlgorithmC. R. Bharathi, V. ShanthiProblem statement: In real-world environment, speech signal processing plays a vital role among the research communities. A wide range of researches are carried out in this field for denoising, enhancement and more. Besides the other, stress management is important to identify the spot in which the stress has to be made. Approach: In this study, in order to provide proper … …American Journal of Applied Sciences, Vol. 9, Issue 10 · 13 Aug 2012 · 7,205 accessesView PDFCite
Research ArticleOpen accessIn the abstractContext Tamil Llama-QA: A Question and Answering Framework for Semantic Information Retrieval Using Tamil Large Language ModelJeevit Davidson S., M. MuraliThe Question Answering System (QA) is essential for effectively retrieving and providing relevant information, and its performance is heavily reliant on the efficiency of information retrieval mechanisms. As the volume of information within the corpus continues to grow, the complexity of generating accurate and pertinent answers also increases substantially. This challenge is particularly significant when designing QA systems for regional … …Journal of Computer Science, Vol. 22, Issue 10 · 7 Oct 2026 · 442 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnhanced RoBERTa Model for OCR-Based EHR Parsing and Information ExtractionBalaji Ganesh Rajagopal, Amarnath C, Chidambaram Sawri Rajan, Deebalakshmi RamalingamHealthy source data for medical research and health analytics in general can be obtained from Electronic Health Records (EHRs). Nevertheless, due to the complexities of the design and especially the unstructured nature of them, it is not easy to extract important information from digital documents. This paper proposes a fundamentally new approach to the problem of interpreting EHRs obtained by … …Journal of Computer Science, Vol. 22, Issue 4 · 5 May 2026 · 1,581 accessesView PDFCite
Research ArticleOpen accessIn the abstractAraSenti-MARBERT-DynGCN: An Advanced Framework for Sarcasm Detection in Arabic TextBassma M Mousa, Mohammed H. Haggag, Mervat GheithSarcasm detection in Arabic texts is challenging because of the complexity in Arabic morphology, the multitudes of dialects used by native speakers, and its strong reliance on context. The lack of standardized orthography in most written forms of Arabic, combined with the frequent use of colloquialisms and metaphorical expressions, makes the task all the more difficult when trying to detect … …Journal of Computer Science, Vol. 21, Issue 11 · 12 Feb 2026 · 1,562 accessesView PDFCite
Research ArticleOpen accessIn the abstractEmpowering Small Language Models With Chain of Thought and Parameter Efficient Fine Tuning for Efficient Deep ReasoningAryan Raina, Shiwani Gupta, Jagruti Jadhav et al.Small Language Models (SLMs) run faster and fit on modest hardware, yet solving multi-step logic problems has traditionally been difficult for them. This work investigates a systematic, multi-model framework that combines Chain-of-Thought (CoT) prompting with Low-Rank Adaptation (LoRA) parameter-efficient fine-tuning, and measures the contribution of each component independently. We evaluate three SLMs (TinyLlama-1.1B, Phi-3-mini-4k-instruct, and Qwen2.5-1.5B) using 1,164 training samples … …Journal of Computer Science, Vol. 22, Issue 8 · 8 Sep 2026 · 862 accessesView PDFCite
Research ArticleOpen accessIn the abstractFake News Detection Using Weighted Fine-Tuned BERT and Sparse Recurrent Neural NetworkAsha Kathigi, Mukta Pujar, A M S Akshatha et al.Fake news refers to misinformation or false reports shared in the form of images, articles, or videos, disguised as real news to manipulate people’s opinions. Recently, fake news and rumors have spread extensively and rapidly around the world. This has led to the production and propagation of inaccurate news articles. Therefore, it is necessary to restrict the spread of fake … …Journal of Computer Science, Vol. 21, Issue 12 · 23 Jan 2026 · 2,087 accessesView PDFCite
Research ArticleOpen accessIn the abstractFew-shot Fine-tuning of BERT Multilingual for Hindi Word Sense DisambiguationShailendra Kumar Patel, Rakesh Kumar, Anuj Kumar SirohiWord Sense Disambiguation (WSD) is a fundamental task in Natural Language Processing (NLP), addressing the challenge of identifying correct word meanings in context. This task is particularly complex for morphologically rich and resource-limited languages like Hindi, which exhibit significant lexical ambiguity compounded by limited availability of annotated corpora. To address these challenges, we propose a supervised approach combining the multilingual … …Journal of Computer Science, Vol. 21, Issue 11 · 26 Dec 2025 · 2,250 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Comprehensive Survey on Fake News Detection Using Machine LearningShraddha Shah, Sachin PatelIn the age of information data surplus and social media impact, the propagation of fake news has developed a significant societal concern. This challenge requires robust tools and methodologies for addressing, with machine learning emerging as a promising approach. The paper reviews different machine learning techniques in fake news detection, including supervised, unsupervised and semi-supervised methods. Supervised methods utilize labelled … …Journal of Computer Science, Vol. 21, Issue 4 · 20 Mar 2025 · 9,212 accessesView PDFCite
Research ArticleOpen accessIn the abstractNovel Depression Classification Framework Using Optimal Feature Integration with Hybrid Convolution (1D/3D) Based Adaptive Residual DenseNetB. Manjulatha, Suresh PabbojuThis study introduces a novel depression classification method by incorporating multimodal data to reduce this limitation and improve the accuracy of depression diagnosis. In the beginning, the multimodal data such as speech signal, Electroencephalographic (EEG), and text data is obtained from the benchmark datasets. These acquired text data are subjected to the text pre-processing phase, where the stemming, character removal, … …Journal of Computer Science, Vol. 21, Issue 3 · 25 Feb 2025 · 5,860 accessesView PDFCite