DRFW-TQC: Reinforcement Learning for Robotic Strawberry Picking with Dynamic Regularization and Feature Weighting by Anping Zheng, Zirui Fang, Zixuan Li, Hao Dong, Ke Li

“…Strawberry harvesting represents a labor-intensive agricultural operation where existing end-effector pose control algorithms frequently exhibit insufficient precision in fruit grasping, often resulting in unintended damage to target fruits. …”
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UAV Mission Computer Operation Mode Optimization Focusing on Computational Energy Efficiency and System Responsiveness by Oleksandr Liubimov, Ihor Turkin, Valeriy Cheranovskiy, Lina Volobuieva

“…The results are the basis for developing algorithms and energy-efficient design strategies for the mission computer to solve the optimization problem. …”
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HarmonizR: blocking and singular feature data adjustment improve runtime efficiency and data preservation by Simon Schlumbohm, Julia E. Neumann, Philipp Neumann

“…An additionally welcome update regarding improved feature rescue furthermore enhances the algorithms ability to quickly and robustly perform batch effect reduction.…”
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Exploring the Role of Artificial Intelligence in Detecting Advanced Persistent Threats by Pedro Ramos Brandao

“…By leveraging machine learning algorithms and data analytics, AI systems can identify patterns and anomalies that are indicative of sophisticated cyber-attacks. …”
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Intelligent Wireless Power Scheduling for Lunar Multienergy Systems: Deep Reinforcement Learning for Real-Time Adaptive Beam Steering and Vehicle-to-Grid Energy Optimization by Thomas Tongxin Li, Shuangqi Li, Cynthia Xin Ding, Zhaoyao Bao, Mohannad Alhazmi

“…Future work will explore the integration of hybrid energy storage models, quantum-inspired optimization for real-time decision-making, and predictive beamforming algorithms to further enhance the reliability and efficiency of lunar energy networks.…”
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ARTIFICIAL INTELLIGENCE IN UNIVERSITY MARKETING: SOME REALITIES AND POSSIBLE APPLICATIONS by Eudochia JOMIR, Grigore BELOSTECINIC

“…In the present research, it is proposed to use the EyeQuant platform, based on predictive artificial intelligence and algorithms able to simulate the reactions of real users to various visual elements of university websites. …”
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Strengthening health information in India: are artificial intelligence based solutions enough to manage health infodemic? by Rohit Jangra, Divya Sharma, Arun Kumar Aggarwal, Ashish Behera, Pankaj Arora, Mohit Kumar, Pulak Syal, Pritam Halder, Kapil Goel, Vineeth Rajagopal, Tanvi Kiran

“…This system should include advanced algorithms and AI to monitor and analyze health-related information, authoritative sources like the Ministry of Health and Family Welfare, public education initiatives, a robust legal and regulatory framework, and continuous research. …”
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CURRENT PRINCIPLES AND TRENDS OF ATRIAL FIBRILLATION PHARMACOTHERAPY by Yu. A. Bunin

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Enhanced machine learning model for classification of the impact of technostress in the COVID and post-COVID era by Gabriel James, Anietie Ekong, Aloysius Akpanobong, Enefiok Etuk, Saviour Inyang, Samuel Oyong, Ifeoma Ohaeri, Chikodili Orazulume, Peace Okafor

“…This study models a system that employs a Random Forest algorithm for prediction and classification, using age, gender, hours spent, and technological experience as parameters to categorize stress into high, moderate, and low levels. …”
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A Decision Support Tool to Assess the Energy Renovation Performance Through a Timber-Based Solution for Concrete-Framed Buildings by Gianpiero Evola, Michele Torrisi, Vincenzo Costanzo, Marilena Lazzaro, Diego Arnone, Giuseppe Margani

“…The discrepancies identified through this comparison will inform the refinement of the e-DSS algorithms to increase their accuracy and reliability. …”
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Application of a Texture Appearance Model for Segmentation of Lung Nodules on Computed Tomography of the Chest by F. Shariaty, V. A. Pavlov, S. V. Zavjalov, M. Orooji, T. M. Pervunina

“…The results of applying the described TAM method to normal and noisy CT images are presented and compared to those obtained using the Region Growing and Active Contour algorithms, as well as the combination of Active Contour and Watershed algorithms. …”
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Research and analysis of differential gene expression in CD34 hematopoietic stem cells in myelodysplastic syndromes. by Min-Xiao Wang, Chang-Sheng Liao, Xue-Qin Wei, Yu-Qin Xie, Peng-Fei Han, Yan-Hui Yu

“…After comprehensive evaluation, we ultimately selected three algorithms-Lasso regression, random forest, and support vector machine (SVM)-as our core predictive models. …”
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AI in Medical Questionnaires: Innovations, Diagnosis, and Implications by Xuexing Luo, Yiyuan Li, Jing Xu, Zhong Zheng, Fangtian Ying, Guanghui Huang

“…In terms of disease prediction, one study had an area under the curve of 0.790 for cataract surgery risk prediction. …”
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Relationships Between Oat Phenotypes and UAV Multispectral Imagery Under Different Water Deficit Conditions by Structural Equation Modelling by Yayang Feng, Guoshuai Wang, Jun Wang, Hexiang Zheng, Xiangyang Miao, Xiulu Sun, Peng Li, Yan Li, Yanhui Jia

“…However, the prediction accuracy for moisture conditions using spectral data alone is low. …”
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Exploring the Current Status of Risk Stratification in Hypertrophic Cardiomyopathy: From Risk Models to Promising Techniques by Alexandros Kasiakogias, Christos Kaskoutis, Christos-Konstantinos Antoniou, Stavros Georgopoulos, Dimitrios Tsiachris, Petros Arsenos, Alexandrina Kouroutzoglou, Dimitrios Klettas, Charalambos Vlachopoulos, Konstantinos Tsioufis, Konstantinos Gatzoulis

“…Improving clinical prediction of sudden cardiac death is a crucial step in the management of patients with hypertrophic cardiomyopathy. …”
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The role of artificial intelligence in promoting health and developing preventive strategies for diabetes by Ameneh Marzban

“…Dear Editor Diabetes remains a significant public health challenge, and the integration of artificial intelligence (AI) presents remarkable opportunities to enhance early diagnosis, personalized treatment, and effective prevention strategies.1 AI algorithms, including supervised learning and convolutional neural networks, can efficiently analyze large datasets to identify patterns and risk factors associated with diabetes, surpassing the capabilities of traditional methods.2 This advanced analysis enables healthcare providers to predict the likelihood of diabetes in individuals and populations, facilitating timely interventions and customized prevention strategies. …”
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Maximum Power Exploitation of Photovoltaic System under Fast-Varying Solar Irradiation and Local Shading by Yi-Jui Chiu, Bi Li, Chin-Ling Chen, Shui-Yang Lien, Ding Chen, Ji-Ming Yi, Yung-Hui Shih

“…Finally, an improved MPPT system with the PF-MPPT algorithm was proposed to solve the problem of the significant reduction of the output power of a PV system under fast-varying solar irradiation and local shading. …”
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Lightweight coal mine conveyor belt foreign object detection based on improved Yolov8n by Jierui Ling, Zhibo Fu, Xinpeng Yuan

“…Fourthly, the original channel attention mechanism in the Head part is replaced with C2f_MLCA, which not only speeds up the processing speed but also successfully avoids the problems of accuracy degradation caused by channel dimensionality reduction. Fifthly, the lightweight detection head Detect-LSDECD is added, which uses Detail Enhancement Convolution (DEConv) for improvement and optimization, enhancing the stability of the algorithm’s recognition under various environmental factors. …”
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Rapid and simultaneous determination of mixed pesticide residues in apple using SERS coupled with multivariate analysis by Ting-feng Shi, Ting-tiao Pan, Ping Lu

“…This study aims to apply multivariate analysis algorithms for modeling the same spectra, for simultaneous determination of pymetrozine and carbendazim residues in apple. …”
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Application of machine learning and neural network models based on experimental evaluation of dissimilar resistance spot-welded joints between grade 2 titanium alloy and AISI 304 s... by Marwan T. Mezher, Alejandro Pereira, Rusul Ahmed Shakir, Tomasz Trzepieciński

“…However, the random forest algorithm gave the second best prediction of the MSE while the CatBoost and gradient boosting algorithms were third and fourth, respectively. …”
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