Energy-efficient cooperative sensing and transmission in relay-assisted cognitive radio network by Li FENG, Yu-jun KUANG, Bin-wei WU, Xin-chuan FU

“…An innovative EE-oriented cooperative sensing and transmission scheme in relay-assisted cognitive radio networks,called energy-efficient best-relay cooperative transmission (BCT) was proposed.Based on the BCT scheme,mean energy efficiency (MEE) maximization problem with sensing duration and transmitting power as optimization variables was modeled for fading channels under constraint of minimal secondary outage probability.By virtue of Jensen’s inequality,the original optimization problem was decomposed into two relatively independent subproblems which solved sensing duration and power allocation respectively.And for the two subproblems,an efficient cross iteration based algorithm was proposed to obtain the suboptimal solutions.Both analytical and simulation results demonstrate that the proposals can achieve significantly higher EE while enhancing reliability of secondary transmission remarkably compared to non-cooperation single cognitive transmission schemesin high QoS requirement.…”
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Energy-efficient cooperative sensing and transmission in relay-assisted cognitive radio network by Li FENG, Yu-jun KUANG, Bin-wei WU, Xin-chuan FU

“…An innovative EE-oriented cooperative sensing and transmission scheme in relay-assisted cognitive radio networks,called energy-efficient best-relay cooperative transmission (BCT) was proposed.Based on the BCT scheme,mean energy efficiency (MEE) maximization problem with sensing duration and transmitting power as optimization variables was modeled for fading channels under constraint of minimal secondary outage probability.By virtue of Jensen’s inequality,the original optimization problem was decomposed into two relatively independent subproblems which solved sensing duration and power allocation respectively.And for the two subproblems,an efficient cross iteration based algorithm was proposed to obtain the suboptimal solutions.Both analytical and simulation results demonstrate that the proposals can achieve significantly higher EE while enhancing reliability of secondary transmission remarkably compared to non-cooperation single cognitive transmission schemesin high QoS requirement.…”
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Multi-Objective-Based Multi-Heterogeneous- Agent Deep Reinforcement Learning for Minimization of Voltage Deviation and Operation Cost in Active Distribution System by Anurak Deanseekeaw, Watcharakorn Pinthurat, Boonruang Marungsri

“…The proposed framework employs a multi-objective optimization approach, integrating three advanced algorithms: multi-agent proximal policy optimization (MAPPO), multi-agent asynchronous actor-critic (MAA2C), and multi-agent twin delayed deep deterministic policy gradient (MATD3). …”
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Artificial intelligence-empowered functional design of semi-transparent optoelectronic and photonic devices via deep Q-learning by Caglar Cetinkaya, Erman Cokduygulular, Muhammed Yusuf Aykut, Okan Erkal, Fatma Aydogmus, Baris Kinaci

“…Abstract Photonic-based design of semi-transparent organic solar cells (ST-OSCs) demands a careful balance between optical transparency and photovoltaic efficiency, often requiring trade-offs that complicate optimization. This study, for the first time, employs deep Q-learning, a reinforcement learning algorithm, to address this challenge, integrating transfer matrix method for precise optical calculations. …”
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Cascade-Based Input-Doubling Classifier for Predicting Survival in Allogeneic Bone Marrow Transplants: Small Data Case by Ivan Izonin, Roman Tkachenko, Nazarii Hovdysh, Oleh Berezsky, Kyrylo Yemets, Ivan Tsmots

“…The proposed method was tested on a small dataset within transplantology, focusing on binary classification. Optimal parameters for the method were identified using the Dual Annealing algorithm. …”
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Impedance-Driven Decoupling Water–Nitrogen Stress in Wheat: A Parallel Machine Learning Framework Leveraging Leaf Electrophysiology by Shuang Zhang, Xintong Du, Bo Zhang, Yanyou Wu, Xinyi Yang, Xinkang Hu, Chundu Wu

“…A parallel modelling strategy was implemented employing Gradient Boosting, Random Forest, and Ridge Regression, selecting the optimal algorithm per feature based on predictive performance. …”
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Remote Target High-Precision Global Geolocalization of UAV Based on Multimodal Visual Servo by Xuyang Zhou, Ruofei He, Wei Jia, Hongjuan Liu, Yuanchao Ma, Wei Sun

“…We design a step-convergent target geolocation optimization algorithm. By adjusting the step size and the scale factor of the cost function, we achieve fast accuracy convergence for different UAV reconnaissance modes, while maintaining the geolocation accuracy without divergence even when the laser ranging sensor is turned off for a short period. …”
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HouseGanDi: A Hybrid Approach to Strike a Balance of Sampling Time and Diversity in Floorplan Generation by Azmeraw Bekele Yenew, Beakal Gizachew Assefa, Elefelious Getachew Belay

“…To address these issues, various techniques such as regualrization techniques, architectural modifications, and optimization algorithms, have been employed. However, existing techniques still struggle to balance both sampling time and diversity simultaneously. …”
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Leveraging machine learning techniques to analyze nutritional content in processed foods by K. A. Muthukumar, Soumya Gupta, Doli Saikia

“…The SVR model was optimized to identify the best-fitting hyperplane in high-dimensional space, while the RF model utilized GridSearchCV for hyperparameter tuning and performed a “Feature Importance Analysis” to identify key factors influencing the outcomes. …”
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Impact of Public Space in Primary and Secondary Schools Based on Natural Visibility Ratio by Feng Liu, Hao Zhou, Jiangtao Xie, Yue Tang, Shuyu Liu

“…In recent years, the safety of primary and secondary schools has garnered considerable attention from policymakers and architects, necessitating rational design methods to develop effective strategies that optimize the campus environment. This study utilizes algorithmic simulations, spatial analysis, and statistical methods to examine the relationships between the layouts of buildings, public space morphology, and campus safety. …”
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Trade-off between gradient measurement efficiency and expressivity in deep quantum neural networks by Koki Chinzei, Shinichiro Yamano, Quoc Hoan Tran, Yasuhiro Endo, Hirotaka Oshima

“…Abstract Quantum neural networks (QNNs) require an efficient training algorithm to achieve practical quantum advantages. …”
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XSQ-Learning: Adaptive Similarity Thresholds for Accelerated and Stable Q-Learning by Ansel Y. Rodríguez González, Roberto E. López Díaz, Shender M. Ávila Sansores, María G. Sánchez Cervantes

“…Reinforcement Learning (RL) enables agents to learn optimal policies through environment interaction, with Q-learning being a fundamental algorithm for Markov Decision Processes (MDPs). …”
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Lightweight UAV Detection Method Based on IASL-YOLO by Huaiyu Yang, Bo Liang, Song Feng, Ji Jiang, Ao Fang, Chunyun Li

“…To address this, we propose the IASL-YOLO algorithm, which optimizes the YOLOv8s model to enhance detection accuracy and lightweight efficiency. …”
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Ultra-High Spatial Resolution Clinical Positron Emission Tomography (PET) Systems by Myungheon Chin, Muhammad Nasir Ullah, Derek Innes, Craig S. Levin

“…A key strategy involves organ-specific, or loco-regional, scanner configurations that optimize photon detection efficiency (PDE) while balancing the trade-off between spatial resolution and image signal-to-noise ratio (SNR). …”
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Efficient structure learning of gene regulatory networks with Bayesian active learning by Dániel Sándor, Péter Antal

“…We evaluate scalable structure learning algorithms within an active learning framework to optimize intervention selection. …”
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Pig Aggression Tracking and Analysis Application Based on A RPMeMOTR Method by Shuqin Tu, Haoxuan Ou, Aqing Yang, Yun Liang, Jiaying Du, Yuefei Cao, Ruilin He, Fang Yuan

“…The management of pig aggression in group-housed environments is crucial for ensuring animal welfare and optimizing production efficiency in the global agricultural industry. …”
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Data-Driven Pavement Performance: Machine Learning-Based Predictive Models by Mohammad Fahad, Nurullah Bektas

“…A k-fold cross-validation technique was employed to optimize hyperparameters. Results indicate that LightGBM and CatBoost outperform other models, achieving the lowest mean squared error and highest R² values. …”
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Convergence of nanotechnology and artificial intelligence in the fight against liver cancer: a comprehensive review by Manjusha Bhange, Darshan Telange

“…We highlight how AI-powered algorithms can optimize nanocarrier design, facilitate real-time monitoring of treatment efficacy, and enhance clinical decision-making. …”
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Heterogeneity-aware device selection for efficient federated edge learning by Yiran Shi, Jieyan Nie, Xingwei Li, Hui Li

“…This rigorous selection process ensures that the participating device set is optimally positioned to balance the dual objectives of training efficiency and model accuracy. …”
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Pseudolite Self-Interference Cancellation Without Additional GNSS Signal Distortion by Lichao Gao, Jin He, Yuanxin Wu, Wenxian Yu

“…The simulation test results show that the algorithm can filter self-interference without additional distortion of the satellite navigation signals. …”
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