TinyML and IoT-enabled system for automated chicken egg quality analysis and monitoring by Omoy Kombe Hélène, Martin Kuradusenge, Louis Sibomana, Ipyana Issah Mwaisekwa

“…The poultry industry grapples with challenges in maintaining optimal egg quality during incubation, with environmental factors such as temperature, humidity, and lighting playing crucial roles. …”
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From Code to Life: The AI‐Driven Revolution in Genome Editing by Zhidong Li, Wasi Ullah Khan, Genxiang Bai, Chao Dong, Jungang Wang, Youpeng Zhang, Chong Wang, Hongbin Zhang, Wenyi Wang, Ming Luo, Fei Chen

“…Recent advancements in artificial intelligence (AI) have significantly enhanced genome editing by improving target selection, minimizing off‐target effects, and optimizing CRISPR‐associated systems. …”
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Machine Learning for Earthquake Emergency Evacuation: Site Selection and Neighborhood Navigation by Amirmasoud Amiran, Behrouz Behnam, Sanaz Seyedin

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Investigating of Machine Learning Based Algorithms for Liver Cirrhosis Prediction by Young Choi, Eunsung Oh

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EPICS: error-preserved and interpolation-corrected surrogate-assisted particle swarm optimization for complex optimization by Tongbang Jiang, Jeng-Shyang Pan, Shu-Chuan Chu, Chaoli Sun, Václav Snášel

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Deep reinforcement learning applications and prospects in industrial scenarios by JING TAN, Ligang YANG, Xiaorui LI, Zhaolin YUAN, Yunduan CUI, Chao YAO, Zongjie WANG, Xiaojuan BAN

“…Central to these systems are control algorithms, which enable the automation of operations, optimization of process parameters, and reduction of operational costs. …”
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A hybrid slime mold enhanced convergent particle swarm optimizer for parameter estimation of proton exchange membrane fuel cell by Mohammad Aljaidi, Sunilkumar P. Agrawal, Anil Parmar, Pradeep Jangir, Arpita, Bhargavi Indrajit Trivedi, G. Gulothungan, Ali Fayez Alkoradees, Reena Jangid, Mohammad Khishe

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The financial impact of human resources configuration: A quantitative analysis based on modified single candidate optimizer by Zhuozhuo Zhang, Jun Lu, Qi Wang

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Enhanced Short-Term PV Power Forecasting via a Hybrid Modified CEEMDAN-Jellyfish Search Optimized BiLSTM Model by Yanhui Liu, Jiulong Wang, Lingyun Song, Yicheng Liu, Liqun Shen

“…This study proposes a novel hybrid forecasting model that integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the jellyfish search (JS) optimization algorithm, and a bidirectional long short-term memory (BiLSTM) neural network. …”
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Reinforcement-Learning-Based Trajectory Design and Phase-Shift Control in UAV-Mounted-RIS Communications by Tianjiao Sun, Sixing Yin, Li Deng, F. Richard Yu

“…Experimental results demonstrate that the proposed RL solutions are highly efficient in model training along with acceptable performance close to the benchmark, which relies on conventional optimization algorithms with the UT’s locations and channel parameters explicitly known beforehand.…”
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Angle Modulated Artificial Bee Colony Algorithms for Feature Selection by Gürcan Yavuz, Doğan Aydin

“…Optimal feature subset selection is an important and a difficult task for pattern classification, data mining, and machine intelligence applications. …”
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Satellite Image Classification Using a Hybrid Manta Ray Foraging Optimization Neural Network by Amit Kumar Rai, Nirupama Mandal, Krishna Kant Singh, Ivan Izonin

“…The manta ray foraging optimization algorithm is inspired by the intelligent behaviour of manta rays. …”
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A Nonparametric Translational Motion Compensation Algorithm for ISAR Imaging by Using the Alternating Iteration and LBFGS Algorithm by Yuexin Gao, Min Xue, Hanwen Yu, Jianlai Chen

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Research progress on test scenario of ship autonomous navigation by Lijia CHEN, Kai WANG, Liwen HUANG, Shengwei LI, Xinwei ZHOU, Yanzhi LIU

“…Based on the characteristics in test object and scenario requirements, the scenario construction methods are divided into real data extraction, mechanism modeling and reconstruction, data-driven derivation, and optimization search methods. The performance and characteristics of different scenario construction methods are compared and described, and the problems of scenario construction methods are analyzed in combination with different testing requirements such as algorithm verification optimization, performance evaluation, and safety verification. …”
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Reinforcement Learning-Driven Secrecy Energy Efficiency Maximization in RIS-Enabled Communication Systems by Akram Y. Sarhan, Osamah A. Abdullah, Hayder Al-Hraishawi, Faisal S. Alsubaei

“…We introduce an innovative framework that utilizes the deep deterministic policy gradient (DDPG) algorithm to address the challenges of the MDP. Our extensive simulations show that our method works much better than current techniques using deterministic policy gradients, like the proximal policy optimization (PPO) framework, in improving SEE. …”
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Detection of Foreign Bodies in Transmission Line Channels Based on Fusion of Swin Transformer and YOLOv5 by XUE Ang, JIANG Enyu, ZHANG Wentao, LIN Shunfu, MI Yang

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Multi-objective autonomous eco-driving strategy: A pathway to future green mobility by He Tong, Liang Chu, Zheng Chen, Yonggang Liu, Yuanjian Zhang, Jincheng Hu

“…With the wide popularity of electric vehicles in the market and advancements in autonomous driving technology, intelligent electric vehicles (iEVs) equipped with comprehensive eco-driving capabilities are expected to play a pivotal role in energy conservation and emission reduction of future mobility. …”
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Deep Q-Learning-Based Resource Management in IRS-Assisted VLC Systems by Ahmed Al Hammadi, Lina Bariah, Sami Muhaidat, Mahmoud Al-Qutayri, Paschalis C. Sofotasios, Merouane Debbah

“…Our results also highlight the effectiveness of the proposed algorithm in leveraging the increasing number of IRS elements for optimized performance.…”
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Sample-Adjusted Threshold Calculation (SATC) for Optimizing Flood Mapping From a Trustworthy and Explainable Perspective by Zhijun Jiao, Biyan Chen, Zhimei Zhang, Lixin Wu

“…Furthermore, incorporating SATC into the knowledge-driven flood intelligent monitoring (KDFIM) framework yields optimal results, achieving a flood mapping accuracy >95% and a Kappa value >0.95. …”
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Recurrent Neural Network Optimized by Grasshopper for Accurate Audio Data-Based Diagnosis of Parkinson's Disease by Saif Wali Ali Alsudani, Ghassan Khudair Saud

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