Accurate prediction of electrical load is crucial for optimising energy management, especially in areas where temperature fluctuations directly influence energy demand. This study proposes an innovative approach by ap...
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Stochastic neighbor embedding (SNE) performs nonlinear transformation from high-dimensional observation space to low-dimensional latent space which preserves neighbor affinities. Data pairs in latent space tend to be ...
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Recent studies have demonstrated the utility of simulations and video games as viable approximations for real-world problems, particularly in autonomous driving. Initial testing and validation of autonomous systems ar...
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Artificial Intelligence (AI) chatbots play an important role in modernizing education, particularly e-learning platforms by acting as intelligent tools to address issues such as disengagement and dropout rates. AI cha...
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Tactile sensing plays a crucial role in enabling robots to safely interact with objects in dynamic environments [1].Given that potential physical contact can occur at any location during robot interaction, there is a ...
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Tactile sensing plays a crucial role in enabling robots to safely interact with objects in dynamic environments [1].Given that potential physical contact can occur at any location during robot interaction, there is a need for a tactile sensor that can be deployed extensively across the robot's body.
Reducing the cost of radio frequency (RF) chains remains a critical challenge in multiple-input multiple-output (MIMO) systems. In this paper, we investigate a novel low-RF-chain technique, termed as phase-shifter-aid...
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Human pose estimation is a fundamental yet challenging task in computer vision. However, difficult scenarios such as invisible keypoints, occlusions and small-scale persons are still not well-handed. In this paper, we...
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Data clustering is an unsupervised learning method used to extract knowledge from data, it is an NP-Hard (nondeterministic polynomial time) problem;there is no known deterministic technique that can find the optimal s...
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In response to the accelerating integration of IoT devices for monitoring residential energy consumption, this study focuses on identifying robust predictive models aligned with global sustainability goals. Investigat...
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Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization *** approach aims to leverage the strengths of mult...
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Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization *** approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization *** this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)*** proposed hybrid algorithm will be referred to as *** this fusion,the BHJO algorithm aims to leverage the strengths of each *** this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and *** meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization *** addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid ***,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem ***,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation *** rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedd
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