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Giorgio Lazzarinetti, Riccardo Dondi, Sara Manzoni and Italo Zoppis
Solving combinatorial problems on complex networks represents a primary issue which, on a large scale, requires the use of heuristics and approximate algorithms. Recently, neural methods have been proposed in this context to find feasible solutions for r...
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Weilai Jiang, Chenghong Zheng, Delong Hou, Kangsheng Wu and Yaonan Wang
The autonomous shape decision-making problem of a morphing aircraft (MA) with a variable wingspan and sweep angle is studied in this paper. Considering the continuity of state space and action space, a more practical autonomous decision-making algorithm ...
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Haohan Shi, Xiyu Shi and Safak Dogan
Audio inpainting plays an important role in addressing incomplete, damaged, or missing audio signals, contributing to improved quality of service and overall user experience in multimedia communications over the Internet and mobile networks. This paper p...
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Omar Serghini, Hayat Semlali, Asmaa Maali, Abdelilah Ghammaz and Salvatore Serrano
Spectrum sensing is an essential function of cognitive radio technology that can enable the reuse of available radio resources by so-called secondary users without creating harmful interference with licensed users. The application of machine learning tec...
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Cheolhyeon Kwon and Donghyun Kang
Recently, the technologies of on-device AI have been accelerated with the development of new hardware and software platforms. Therefore, many researchers and engineers focus on how to enable ML technologies on mobile devices with limited hardware resourc...
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Anastasios Fanariotis, Theofanis Orphanoudakis and Vassilis Fotopoulos
Having as a main objective the exploration of power efficiency of microcontrollers running machine learning models, this manuscript contrasts the performance of two types of state-of-the-art microcontrollers, namely ESP32 with an LX6 core and ESP32-S3 wi...
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Tianao Qin, Ruixin Chen, Rufu Qin and Yang Yu
Time series prediction is an effective tool for marine scientific research. The Hierarchical Temporal Memory (HTM) model has advantages over traditional recurrent neural network (RNN)-based models due to its online learning and prediction capabilities. G...
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Konstantinos Dolaptsis, Xanthoula Eirini Pantazi, Charalampos Paraskevas, Selçuk Arslan, Yücel Tekin, Bere Benjamin Bantchina, Yahya Ulusoy, Kemal Sulhi Gündogdu, Muhammad Qaswar, Danyal Bustan and Abdul Mounem Mouazen
Irrigation plays a crucial role in maize cultivation, as watering is essential for optimizing crop yield and quality, particularly given maize?s sensitivity to soil moisture variations. In the current study, a hybrid Long Short-Term Memory (LSTM) approac...
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Efrain Noa-Yarasca, Javier M. Osorio Leyton and Jay P. Angerer
Timely forecasting of aboveground vegetation biomass is crucial for effective management and ensuring food security. However, research on predicting aboveground biomass remains scarce. Artificial intelligence (AI) methods could bridge this research gap a...
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Jinhui Guo, Xiaoli Zhang, Kun Liang and Guoqiang Zhang
In recent years, the emergence of large-scale language models, such as ChatGPT, has presented significant challenges to research on knowledge graphs and knowledge-based reasoning. As a result, the direction of research on knowledge reasoning has shifted....
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