We investigate tolerant testing and distance estimation problems in distribution testing when the amount of memory is limited. In particular, our aim is to provide a good estimate of the distance measure (in total var...
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This research presents a novel method of automatic video summarising and note-generating using Natural Language Processing (NLP) and audio recognition techniques. The exponential rise in internet video footage has inc...
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Analyzing incomplete data is one of the prime concerns in data analysis. Discarding the missing records or values might result in inaccurate analysis outcomes or loss of helpful information, especially when the size o...
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Analyzing incomplete data is one of the prime concerns in data analysis. Discarding the missing records or values might result in inaccurate analysis outcomes or loss of helpful information, especially when the size of the data is small. A preferable alternative is to substitute the missing values using imputation such that the substituted values are very close to the actual missing values and this is a challenging task. In spite of the existence of many imputation algorithms, there is no universal imputation algorithm that can yield the best values for imputing all types of datasets. This is mainly because of the dependence of the imputation algorithm on the inherent properties of the data. These properties include type of data distribution, data size, dimensionality, presence of outliers, data dependency among the attributes, and so on. In the literature, there exists no straightforward method for determining a suitable imputation algorithm based on the data characteristics. The existing practice is to conduct exhaustive experimentation using the available imputation techniques with every dataset and this requires a lot of time and effort. Moreover, the current approaches for checking the suitability of imputations cannot be done when the ground truth data is not available. In this paper, we propose a new method for the systematic selection of a suitable imputation algorithm based on the inherent properties of the dataset which eliminates the need for exhaustive experimentation. Our method determines the imputation technique which consistently gives lower errors while imputing datasets with specific properties. Also, our method is particularly useful when the real-world data do not have the ground truth for missing data to check the imputation performance and suitability. Once the suitability of a DI technique is established based on the data properties, this selection will remain valid for another dataset with similar properties. Thus, our method can save time an
Advanced biometric identification techniques are becoming increasingly necessary in response to rising security requirements. The limits of conventional facial recognition systems have been further brought to light by...
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While machine learning (ML) models can be trained on large amounts of data, their applicability in critical domains such as cyber-attacks and clinical applications is still limited. Recent research suggests that ML mo...
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The shift from basic cell phones to smartphones has ushered in intelligent devices, alongside a surge in mobile apps that reshape daily life. Simultaneously, technologies like data analytics, AI and IoT have enabled s...
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Heart disease is one of the top causes of mortality globally. Early detection can save a person's life. In previous five years, it is found that young individuals are also experiencing heart disease. In order to a...
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The saturated licensed spectrum is insufficient to meet the increasing demand for data traffic, which paves the path for exploring new bands. Since the unlicensed bands are free of cost, efforts are made to offload th...
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This paper introduces a comprehensive framework for deploying and evaluating the performance of applications in serverless cloud environments for microservices. It addresses challenges stemming from the distributed na...
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The desire to delegate computation to the cloud arises from the proliferation of small, computational restrained devices. However, to ensure the security and privacy of the stored data in the cloud, data owners always...
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