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
Traffic conflict techniques rely heavily on the proper identification of conflict extremes,which directly affects the prediction performance of extreme value *** sampling techniques,namely,block maxima and peak over t...
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Traffic conflict techniques rely heavily on the proper identification of conflict extremes,which directly affects the prediction performance of extreme value *** sampling techniques,namely,block maxima and peak over threshold,form the core of these *** studies have demonstrated the inefficacy of extreme value models based on these sampling approaches,as their crash estimates are too imprecise,hindering their widespread practical ***,anomaly detection techniques for sampling conflict extremes have been used,but their application has been limited to estimating crash frequency without considering the crash severity *** address this research gap,this study proposes a hybrid model of machine learning and extreme value theory within a bivariate framework of traffic conflict measures to estimate crash frequency by severity *** particular,modified time-to-collision(MTTC)and expected post-collision change in velocity(Delta-V orΔV)have been proposed in the hybrid modeling framework to estimate rear-end crash frequency by severity ***-end conflicts were identified through artificial intelligence-based video analytics for three four-legged signalized intersections in Brisbane,Australia,using four days of ***-stationary bivariate hybrid generalized extreme value models with different anomaly detection/sampling techniques(isolation forest and minimum covariance determinant)were *** non-stationarity of traffic conflict extremes was handled by parameterizing model parameters,including location,scale,and both location and scale parameters *** results indicate that the bivariate hybrid models can estimate severe and non-severe crashes when compared with historical crash records,thereby demonstrating the viability of the proposed approach.A comparative analysis of two anomaly techniques reveals that the isolation forest model marginally outperforms the minimum covariance determinant ***,the modeling f
Predicting crop disease on the image obtained from the affected crop has been a potential research topic. In this research, the Localise Search Optimisation Algorithm (LSOA) enabled deep Convolutional Neural Network (...
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This study unveils a groundbreaking system leveraging the capabilities of machine learning to forecast and identify seizures, thereby making a substantial positive impact on the lives of individuals grappling with sei...
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Autonomous underwater vehicles(AUVs) have attracted considerable attention due to their vast potential applications, such as offshore oil exploration, underwater rescue, military reconnaissance, and marine scientific ...
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Autonomous underwater vehicles(AUVs) have attracted considerable attention due to their vast potential applications, such as offshore oil exploration, underwater rescue, military reconnaissance, and marine scientific research.
The tile-based multiplayer game Mahjong is widely played in Asia and has also become increasingly popular worldwide. Face-to-face or online, each player begins with a hand of 13 tiles and players draw and discard tile...
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The tile-based multiplayer game Mahjong is widely played in Asia and has also become increasingly popular worldwide. Face-to-face or online, each player begins with a hand of 13 tiles and players draw and discard tiles in turn until they complete a winning hand. An important notion in Mahjong is the deficiency number(*** number in Japanese Mahjong) of a hand, which estimates how many tile changes are necessary to complete the hand into a winning hand. The deficiency number plays an essential role in major decision-making tasks such as selecting a tile to discard. This paper proposes a fast algorithm for computing the deficiency number of a Mahjong hand. Compared with the baseline algorithm, the new algorithm is usually 100 times faster and, more importantly,respects the agent's knowledge about available tiles. The algorithm can be used as a basic procedure in all Mahjong variants by both rule-based and machine learning-based Mahjong AI.
Finding appropriate information on the web is a tedious task and thus demands an intelligent mechanism to assist users for this purpose. Students are the victims of information overloading on the internet the most, as...
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The study demonstrates how technology has had a dramatic influence on healthcare, allowing for the analysis of large clinical datasets using machine learning for early illness identification. Chronic disorders such as...
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Plasmodium parasites cause malaria, a deadly disease that continues to pose a significant global health burden, particularly in resource-limited regions. Detecting and classifying the parasite accurately and promptly ...
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The surrounding environmental and climatic conditions have a significant impact on the utilisation of ecosystem services for recreational purposes. Climate change poses a threat to future natural leisure opportunities...
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