The agricultural sector is one of India's most important and major endeavors, and it is also critical to the country's economic development. Agriculture is one of the most important things that contributes to ...
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This study presents a revolutionary deep-learning architecture that focuses on feature extraction, feature selection, and sales forecasting. The technique begins with a pre-processing step using median imputation and ...
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Hypervolume is most likely the most often used quality indicator in EMO due to its monotonicity with respect to the dominance relation. Since, however, exact calculation of hypervolume is computationally demanding, ma...
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Hypervolume is most likely the most often used quality indicator in EMO due to its monotonicity with respect to the dominance relation. Since, however, exact calculation of hypervolume is computationally demanding, many researchers have proposed methods for hypervolume estimation. Many of such methods use numerical integration of the distance from the reference point to the upper boundary of the dominated region along uniformly sampled directions. To find this distance for a given direction, the maximum value of the inverse weighted Chebycheff function needs to be found. For small solution sets this could be done by the exhaustive search which, however, may be very inefficient for large solution sets, e.g. for unbounded external archives of EMO algorithms. In this paper, we adapt the ND-Tree-based algorithm for finding the minimum of the standard weighted Chebycheff function to finding the maximum of the inverse function. Through a computational experiment we show that this ND-Tree-based algorithm may be used either for reduction of the running time of hypervolume estimation by up to two orders of magnitude or for improving the estimation accuracy with the same time budget up to an order of magnitude for data sets with up to 12 objectives and 50000 points. IEEE
As a result of its aggressive nature and late identification at advanced stages, lung cancer is one of the leading causes of cancer-related deaths. Lung cancer early diagnosis is a serious and difficult challenge that...
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Residential burglary is a severe crime that affects millions of residents each year. It is critical to analyze patterns of human behavior in surveillance video data and discover suspicious actions to avoid and deter t...
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The past decades have witnessed a wide application of federated learning in crowd sensing,to handle the numerous data collected by the sensors and provide the users with precise and customized ***,how to protect the p...
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The past decades have witnessed a wide application of federated learning in crowd sensing,to handle the numerous data collected by the sensors and provide the users with precise and customized ***,how to protect the private information of users in federated learning has become an important research *** with the differential privacy(DP)technique and secure multiparty computation(SMC)strategy,the covert communication mechanism in federated learning is more efficient and energy-saving in training the ma-chine learning *** this paper,we study the covert communication problem for federated learning in crowd sensing Internet-of-Things *** from the previous works about covert communication in federated learning,most of which are considered in a centralized framework and experimental-based,we firstly proposes a centralized covert communication mechanism for federated learning among n learning agents,the time complexity of which is O(log n),approximating to the optimal ***,for the federated learning without parameter server,which is a harder case,we show that solving such a problem is NP-hard and prove the existence of a distributed covert communication mechanism with O(log logΔlog n)times,approximating to the optimal solution.Δis the maximum distance between any pair of learning *** analysis and nu-merical simulations are presented to show the performance of our covert communication *** hope that our covert communication work can shed some light on how to protect the privacy of federated learning in crowd sensing from the view of communications.
Online social networks are becoming more and more popular, according to recent trends. The user's primary concern is the secure preservation of their data and privacy. A well-known method for preventing individual...
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The emergence of multimodal disease risk prediction signifies a pivotal shift towards healthcare by integrating information from various sources and enhancing the reliability of predicting susceptibility to specific d...
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The disease that contains the highest mortality and morbidity across the world is cardiac disease. Annually millions of people are affected and deaths take place due to cardiac diseases worldwide. There are various di...
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Lung cancer is a prevalent and deadly disease worldwide, necessitating accurate and timely detection methods for effective treatment. Deep learning-based approaches have emerged as promising solutions for automated me...
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