The development of precise prediction models for early intervention is of the utmost importance due to the significant public health issue of the increasing prevalence of adult obesity. The study introduces an innovat...
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Nowadays security is main issue during transmission of data. Among many cryptographic methods, ECC is the public key asymmetric cryptosystem which provides faster computation over smaller size in comparison to other a...
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The increasing need for accurate and reliable detection of prohibited items such as guns, knives, and pliers in X-ray security images underscores the importance of advanced frameworks to enhance security screening pro...
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With the evolution of communication technologies, various substantial applications help the human community. Various real-time applications rely on the 5G era to fulfil the baseline service requirements. These 5G netw...
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Smartphones are one of the most important devices in our daily life. It produces transfers and stores a significant amount of data about individuals such as banking transactions, authentication credentials, location d...
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Cryptography is used by all organizations to protect the data files and ensures confidentiality mainly at the time of sharing and storing in the cloud data storage. The cloud service providers use a wide range of tool...
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ECG signals are commonly used to diagnose various cardiac conditions, and the classification of these signals is an important task in Stress identification. With the advancement of ML techniques, several ML models hav...
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ISBN:
(数字)9781837242535
ISBN:
(纸本)9781837242672
ECG signals are commonly used to diagnose various cardiac conditions, and the classification of these signals is an important task in Stress identification. With the advancement of ML techniques, several ML models have been developed to classify ECG signals with high accuracy. This paper aims to review recent studies that use ML models for ECG classification, equivalence to the working nature of different prototype, and discuss the challenges and limitations of these models. The study found that deep learning models, such as CNNs and RNNs, outperformed traditional ML models in terms of closeness and validity. The results also showed that the combination of multiple models could improve the classification performance. However, the study also highlighted the need for large annotated ECG datasets and the requirement of domain expertise for feature engineering. Overall, this review provides a complete analysis of the current state of ECG classification using ML models and identifies areas for future research and improvement. Even though the accuracy of ML models in ECG classification is not always perfect, they can still provide valuable information and support to healthcare professionals. In some cases, even a lower accuracy can still be useful, especially in the context of large-scale screening and monitoring programs, where the goal is to identify individuals who require further evaluation. In these cases, the high computational speed and scalability of ML models make them an attractive option, as they can quickly process large amounts of data and provide preliminary results that can be followed up with more comprehensive and accurate tests. Additionally, the results from ML models can also be used as a starting point for further analysis by healthcare professionals, who can use their domain knowledge to interpret the results and make informed decisions. Therefore, despite their limitations, ML models can still play an vital role in the field of Electrocardiogram cla
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Many real world activities in computerscience scenarios are linked with concurrency and security related issues and have to handle large number of processes to be executed in parallel with false safe security solutio...
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Video compression mainly focuses on reducing the file size of videos with little loss of quality, but most video compression algorithms on the market do not focus on clarity. Data compression is increasingly being con...
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