This research presents a web-based real-time Sri Lankan Sign Language (SLSL) translation system aimed at bridging communication gaps for individuals with speech and hearing disabilities. Leveraging advanced machine le...
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Today, image processing plays a significant role in early disease detection, allowing doctors to treat conditions like breast, lung, and brain malignancies. A brain tumor is a serious condition that can be fatal. Brai...
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software piracy is a major problem faced in the software industry. Despite the several techniques proposed in previous studies, software piracy and intellectual property theft have continued to pose a threat in the so...
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This research introduces an innovative ensemble approach,combining Deep Residual Networks(ResNets)and Bidirectional Gated Recurrent Units(BiGRU),augmented with an Attention Mechanism,for the classification of heart **...
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This research introduces an innovative ensemble approach,combining Deep Residual Networks(ResNets)and Bidirectional Gated Recurrent Units(BiGRU),augmented with an Attention Mechanism,for the classification of heart *** escalating prevalence of cardiovascular diseases necessitates advanced diagnostic tools to enhance accuracy and *** model leverages the deep hierarchical feature extraction capabilities of ResNets,which are adept at identifying intricate patterns within electrocardiogram(ECG)data,while BiGRU layers capture the temporal dynamics essential for understanding the sequential nature of ECG *** integration of an Attention Mechanism refines the model’s focus on critical segments of ECG data,ensuring a nuanced analysis that highlights the most informative features for arrhythmia *** on a comprehensive dataset of 12-lead ECG recordings,our ensemble model demonstrates superior performance in distinguishing between various types of arrhythmias,with an accuracy of 98.4%,a precision of 98.1%,a recall of 98%,and an F-score of 98%.This novel combination of convolutional and recurrent neural networks,supplemented by attention-driven mechanisms,advances automated ECG analysis,contributing significantly to healthcare’s machine learning applications and presenting a step forward in developing non-invasive,efficient,and reliable tools for early diagnosis and management of heart diseases.
Sleep paralysis is when you're awake but powerless to move. Although the majority of occurrences are linked to extreme terror and some potentially clinically significant sufferings are connected with the case;litt...
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Brain stroke is the world's leading cause of death, impacting numerous lives annually. The chances of having a stroke have increased by 50% over one's lifetime, impacting one in four people worldwide. Machine ...
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This preliminary work introduces MelanoTech, a mHealth application designed and implemented to offer a user-friendly and intuitive interface for the early diagnosis of melanoma, a kind of skin cancer with significant ...
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Disinformation,often known as fake news,is a major issue that has received a lot of attention *** researchers have proposed effective means of detecting and addressing *** machine and deep learning based methodologies...
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Disinformation,often known as fake news,is a major issue that has received a lot of attention *** researchers have proposed effective means of detecting and addressing *** machine and deep learning based methodologies for classification/detection of fake news are content-based,network(propagation)based,or multimodal methods that combine both textual and visual *** introduce here a framework,called FNACSPM,based on sequential pattern mining(SPM),for fake news analysis and *** this framework,six publicly available datasets,containing a diverse range of fake and real news,and their combination,are first transformed into a proper ***,algorithms for SPM are applied to the transformed datasets to extract frequent patterns(and rules)of words,phrases,or linguistic *** obtained patterns capture distinctive characteristics associated with fake or real news content,providing valuable insights into the underlying structures and commonalities of ***,the discovered frequent patterns are used as features for fake news *** framework is evaluated with eight classifiers,and their performance is assessed with various *** experiments were performed and obtained results show that FNACSPM outperformed other state-of-the-art approaches for fake news classification,and that it expedites the classification task with high accuracy.
To address the increased complexity of surface cover, increased heterogeneity within homogeneous regions, and increased similarity between different regions in high-resolution remote sensing images, which lead to incr...
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