Natural Language Processing (NLP) is the branch of Artificial Intelligence that deals with the interpretation of human speech. NLP is a vast area of study that is continually being developed each day, with active rese...
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Comfy Shell is an open-source protocol used for many years to comfy community logins and communication over the net. It is primarily based on public key cryptography and is well known for being one of the maximum cozy...
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Cardiac arrhythmia, an abnormal heart condition, requires timely and accurate identification to prevent life-threatening complications. Conventional methods for arrhythmia detection involve manual electrocardiogram (E...
Cardiac arrhythmia, an abnormal heart condition, requires timely and accurate identification to prevent life-threatening complications. Conventional methods for arrhythmia detection involve manual electrocardiogram (ECG) signal analysis, which is time-consuming and prone to human error. This study proposes a lightweight, explainable deep learning model based on EfficientNetB3 for automated cardiac arrhythmia prediction using ECG signals to address these limitations. The model is evaluated on two publicly available MIT-BIH Arrhythmia datasets, demonstrating superior performance over existing state-of-the-art approaches. The proposed method integrates depth-wise separable convolutions for improved efficiency and employs SHAP (Shapley Additive Explanations) to enhance model interpretability. Experimental results show that our model achieves an accuracy of 98.78% and 99.22% on the two datasets, surpassing conventional deep learning models in classification performance. The findings suggest that the proposed lightweight EfficientNetB3 model is reliable, generalizable, and can support clinical decision-making by offering interpretable predictions, potentially improving patient care and early diagnosis of cardiac arrhythmias.
In today's world, our social groups are best defined by online social networks. It includes all aspects of our life including our feelings towards one another. The study of our feelings or relationships in a socia...
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Secure and efficient enforcement of dynamic access control policies on shared data is a central problem in dynamic and scalable application scenarios, including the Internet of Things and smart cities. Key-aggregate c...
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Roads are essential for daily transportation worldwide, but their aging and usage patterns can cause deterioration of the road surface, leading to a decline in quality. This deterioration often results in the formatio...
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In a dual-chamber photocatalytic fuel cell device,polyvinyl alcohol degradation and H2 evolution were concurrently *** setup involved commercial P25 as the photoanode and Ag@Fe_(2)O_(3) nanoparticles as the ***,the fe...
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In a dual-chamber photocatalytic fuel cell device,polyvinyl alcohol degradation and H2 evolution were concurrently *** setup involved commercial P25 as the photoanode and Ag@Fe_(2)O_(3) nanoparticles as the ***,the feasibility of a Fentonlike reaction in the cathode,utilizing Fe^(2+)ions and pumped O_(2),was *** cathode materials,polyvinyl alcohol types,and pH values’effects were assessed on device *** tests highlighted photoinduced holes(h+)and OH·radicals as pivotal contributions to polyvinyl alcohol ***-term stability of the device was established through cycling experiments.
The security of IoT that is based on layered approaches has shortcomings such as the redundancy, inflexibility, and inefficiently of security solutions. There are many harmful attacks in IoT networks such as DoS and D...
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Keyphrase extraction aims to extract important phrases that reflect the main topics of a document. Recently, deep learning methods are used to model semantic information and rank candidates based on the similarities b...
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Keyphrase extraction aims to extract important phrases that reflect the main topics of a document. Recently, deep learning methods are used to model semantic information and rank candidates based on the similarities between the n-grams and the document. However, existing keyphrase extraction methods mainly caused the keyphrase extraction task to be independent of the embedding. Based on the fact that phrases that are semantically closer to the document are more likely to become keyphrases, we propose a novel contrastive learning strategy for supervised keyphrase extraction by integrating local and global information of the document. A pre-trained RoBERTa model is used to model contextual information of sub-words in the document. Then, the embedding vectors of n-grams and the document are calculated by the convolution neural layers. Finally, we propose a novel loss function for efficiently ranking candidate phrases by combining n-gram features and document embeddings during the training of the model.
The Internet of Things (IoT) platform is becoming ubiquitous since its usage is mandatory in a smart environment. IoT solutions have greatly improvised the quality of life of mankind. Internet of Things is connected w...
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