The ubiquitous use of social media impacts the social life of humans. Fake news negatively affects a person's behavior. Fake news detection in social media has been a challenging problem for the last decade. Resea...
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In recent years, although a number of spatial directional relation models have been proposed, the reasoning ability of spatial directional relations is limited due to the inherent defects of some models and the weak r...
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Aiming at the problems of poor transmission stability and long transmission time in the traditional industrial internet of things communication data transmission, a stable transmission method of industrial internet of...
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To tackle the energy crisis and climate change,wind farms are being heavily invested in across the *** China's coastal areas,there are abundant wind resources and numerous offshore wind farms are being *** secure ...
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To tackle the energy crisis and climate change,wind farms are being heavily invested in across the *** China's coastal areas,there are abundant wind resources and numerous offshore wind farms are being *** secure operation of these wind farms may suffer from typhoons,and researchers have studied power system operation and resilience enhancement in typhoon ***,the intricate movement of a typhoon makes it challenging to evaluate its spatial-temporal *** published papers only consider predefined typhoon trajectories neglecting *** address this challenge,this study proposes a stochastic unit commitment model that incorporates high-penetration offshore wind power generation in typhoon *** adopts a data-driven method to describe the uncertainties of typhoon trajectories and considers the realistic anti-typhoon mode in offshore wind farms.A two-stage stochastic unit commitment model is designed to enhance power system resilience in typhoon *** formulate the model into a mixed-integer linear programming problem and then solve it based on the computationally-efficient progressive hedging algorithm(PHA).Finally,numerical experiments validate the effectiveness of the proposed method.
Major traffic accidents are attributed to driver fatigue, according to study on the topic. Driver drowsiness is a state in which the driver of a car is on the verge of falling asleep or losing consciousness. It can be...
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Human face detection along with its localization is a difficult task when the face is presented in the cluttered scene in an unconstrained scenario that might be with arbitrary pose variations, occlusions, random back...
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Natural disasters like flood happen when water overtakes normally dry places. During rescue missions aimed at saving lives, rescue teams responding to floods encounter numerous obstacles. Locating and discovering surv...
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This research aims to develop a model that can predict layoffs within a company using a combination of financial performance, industry trends, and company demographics as input variables. A machine learning algorithm ...
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Preventing crime is important for justice and safety in cities. Using computers to predict crime trends can help make cities safer. Reliable real-time crime prediction is necessary for public safety, but there are sti...
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Glaucoma is one of the leading causes of visual impairment worldwide. If diagnosed too late, the disease can irreversibly cause severe damage to the optic nerve, resulting in permanent loss of central vision and blind...
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Glaucoma is one of the leading causes of visual impairment worldwide. If diagnosed too late, the disease can irreversibly cause severe damage to the optic nerve, resulting in permanent loss of central vision and blindness. Therefore, early diagnosis of the disease is critical. Recent advancements in machine learning techniques have greatly aided ophthalmologists in timely and efficient diagnosis through the use of automated systems. Training the machine learning models with the most informative features can significantly enhance their performance. However, selecting the most informative feature subset is a real challenge because there are 2n potential feature subsets for a dataset with n features, and the conventional feature selection techniques are also not very efficient. Thus, extracting relevant features from medical images and selecting the most informative is a challenging task. Additionally, a considerable field of study has evolved around the discovery and selection of highly influential features (characteristics) from a large number of features. Through the inclusion of the most informative features, this method has the potential to improve machine learning classifiers by enhancing their classification performance, reducing training and testing time, and lowering system diagnostic costs by incorporating the most informative features. This work aims in the same direction to propose a unique, novel, and highly efficient feature selection (FS) approach using the Whale Optimization Algorithm (WOA), the Grey Wolf Optimization Algorithm (GWO), and a hybridized version of these two metaheuristics. To the best of our knowledge, the use of these two algorithms and their amalgamated version for FS in human disease prediction, particularly glaucoma prediction, has been rare in the past. The objective is to create a highly influential subset of characteristics using this approach. The suggested FS strategy seeks to maximize classification accuracy while reducing the t
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