Artificial rabbits optimization(ARO)is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in ***,for solving optimization problems,the ARO al...
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Artificial rabbits optimization(ARO)is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in ***,for solving optimization problems,the ARO algorithm shows slow convergence speed and can fall into local *** overcome these drawbacks,this paper proposes chaotic opposition-based learning ARO(COARO),an improved version of the ARO algorithm that incorporates opposition-based learning(OBL)and chaotic local search(CLS)*** adding OBL to ARO,the convergence speed of the algorithm increases and it explores the search space *** maps in CLS provide rapid convergence by scanning the search space efficiently,since their ergodicity and non-repetitive *** proposed COARO algorithm has been tested using thirty-three distinct benchmark *** outcomes have been compared with the most recent optimization ***,the COARO algorithm’s problem-solving capabilities have been evaluated using six different engineering design problems and compared with various other *** study also introduces a binary variant of the continuous COARO algorithm,named *** performance of BCOARO was evaluated on the breast cancer *** effectiveness of BCOARO has been compared with different feature selection *** proposed BCOARO outperforms alternative algorithms,according to the findings obtained for real applications in terms of accuracy performance,and fitness *** experiments show that the COARO and BCOARO algorithms achieve promising results compared to other metaheuristic algorithms.
Feature selection (FS) is one of the basic preprocessing steps in data mining and is a challenging binary optimization problem. FS is the process of determining the subset that can best represent the dataset by removi...
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Recently,to build a smart factory,research has been conducted to perform fault diagnosis and defect detection based on vibration and noise signals generated when a mechanical system is driven using deep-learning techn...
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Recently,to build a smart factory,research has been conducted to perform fault diagnosis and defect detection based on vibration and noise signals generated when a mechanical system is driven using deep-learning technology,a field of artificial *** of the related studies apply various audio-feature extraction techniques to one-dimensional raw data to extract sound-specific features and then classify the sound by using the derived spectral image as a training ***,compared to numerical raw data,learning based on image data has the disadvantage that creating a training dataset is very ***,we devised a two-step data preprocessing method that efficiently detects machine anomalies in numerical raw *** the first preprocessing process,sound signal information is analyzed to extract features,and in the second preprocessing process,data filtering is performed by applying the proposed *** efficient dataset was built formodel learning through a total of two steps of data *** addition,both showed excellent performance in the training accuracy of the model that entered each dataset,but it can be seen that the time required to build the dataset was 203 s compared to 39 s,which is about 5.2 times than when building the image dataset.
Networks based on backscatter communication provide wireless data transmission in the absence of a power source.A backscatter device receives a radio frequency(RF)source and creates a backscattered signal that deliver...
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Networks based on backscatter communication provide wireless data transmission in the absence of a power source.A backscatter device receives a radio frequency(RF)source and creates a backscattered signal that delivers data;this enables new services in battery-less domains with massive Internet-of-Things(IoT)*** is highly energy-efficient in the context of massive IoT ***,long-range(LoRa)backscattering facilitates large IoT services.A backscatter network guarantees timeslot-and contention-based ***-based transmission ensures data transmission,but is not scalable to different numbers of transmission *** contention-based transmission is used,collisions are *** reduce collisions and increase transmission efficiency,the number of devices transmitting data must be *** control device activation,the RF source range can be modulated by adjusting the RF source power during LoRa *** reduces the number of transmitting devices,and thus collisions and retransmission,thereby improving transmission *** performed extensive simulations to evaluate the performance of our method.
Dear Editor,This letter presents a new transfer learning framework for the deep multi-agent reinforcement learning(DMARL) to reduce the convergence difficulty and training time when applying DMARL to a new scenario [1...
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Dear Editor,This letter presents a new transfer learning framework for the deep multi-agent reinforcement learning(DMARL) to reduce the convergence difficulty and training time when applying DMARL to a new scenario [1], [2].
GPT is widely recognized as one of the most versatile and powerful large language models, excelling across diverse domains. However, its significant computational demands often render it economically unfeasible for in...
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EEG-based interfaces are an active research area with great potential. We, therefore, focused on classifying motor imaging (MI) tasks from various problem areas. Because of that, we applied MI patterns to voting ensem...
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Nowadays, bio-signal-based emotion recognition have become a popular research topic. However, there are some problems that must be solved before emotion-based systems can be realized. We therefore aimed to propose a f...
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We present Q-Cogni, an algorithmically integrated causal reinforcement learning framework that redesigns Q-Learning to improve the learning process with causal inference. Q-Cogni achieves improved policy quality and l...
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Cerebral stroke is a major health problem, and if not recognized and treated immediately, it can result in considerable morbidity and fatality. Predicting the possibility of a stroke can help with intervention, result...
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