SNN-Based Semantic Segmentation Method Using Adaptive Threshold and Multi-Feature Fusion by HUANG Yongbin, LI Chen, DONG Wenbo, LIU Shunlian

“…Experimental results showed that the proposed approach achieved a mean intersection of union (mIoU) on the VOC2012 dataset that was 6.8% higher than that of existing SNN semantic segmentation algorithms. This solution also yielded good segmentation effect on the OCS dataset in high-speed railway scenarios. …”
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A feature-based approach for atlas selection in automatic pelvic segmentation. by Guoping Shan, Xue Bai, Yun Ge, Binbing Wang

“…The proposed procedure also reduced computation time compared to cMAS, making it a promising tool for medical image analysis applications. …”
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The evolving role of programming and LLMs in the development of self-driving laboratories by John R. Kitchin

“…While LLMs present new opportunities for streamlining automation, they also introduce challenges related to reproducibility, security, and reliability. …”
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Detection and Analysis of Malicious Software Using Machine Learning Models by Selman Hızal, Ahmet Öztürk

“…In this study, we evaluate the performance of ML algorithms for obfuscated malware detection using the CIC-MalMem-2022 dataset. …”
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A Comprehensive Study of Recent Path-Planning Techniques in Dynamic Environments for Autonomous Robots by Nour AbuJabal, Mohammed Baziyad, Raouf Fareh, Brahim Brahmi, Tamer Rabie, Maamar Bettayeb

“…This review covers the entire process, starting from obstacle detection techniques, through path-planning strategies, and also extending to formation control and communication styles. …”
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Modeling, optimization, and thermal management strategies of hydrogen fuel cell systems by Abubakar Unguwanrimi Yakubu, Liu Qingsheng, Meng Kai, Chen Jinwei, Omer Abbaker Ahmed Mohammed, Jiahao Zhao, Qi Jiang, Xuanhong Ye, Junyi Liu, Qinglong Yu, Muhammad Aurangzeb, Shusheng Xiong

“…The review also explores hybrid physical-AI models, CFD-based surrogate models, and predictive machine-learning methods like LSTM and CNN. …”
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Recent Advances in Multi-Agent Reinforcement Learning for Intelligent Automation and Control of Water Environment Systems by Lei Jia, Yan Pei

“…This paper focuses on its applications and development in water environmental systems, providing a systematic review of the theoretical foundations of multi-agent systems and reinforcement learning and summarizing three representative categories of mainstream MARL algorithms. Typical control scenarios in water systems are also examined. …”
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In Silico Conotoxin Studies: Progress and Prospects by Ruihan Li, Md. Mahadhi Hasan, Dan Wang

“…With the augmentation of computational algorithms from the realms of bioinformatics and machine learning, in silico strategies have made substantial contributions to facilitate conotoxin studies although still with certain limitations. …”
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Artificial Intelligence in Uropathology by Katia Ramos Moreira Leite, Petronio Augusto de Souza Melo

“…AI, especially deep learning algorithms, has shown significant potential in improving the accuracy and efficiency of pathology workflows. …”
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Progress in the Application of Chemometrics in the Field of Food Flavor by ZHANG Qian, HAN Haoying, MENG Fanyu, WANG Yadong, WANG Bei, JIANG Tao

“…Therefore, it is crucial to use new chemometrics methods, such as artificial intelligence algorithms, correctly and reasonably to obtain effective information in this field. …”
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Hybrid intelligent watermark System by Fardos Abdalkader, Shahbaa Khaleel, Nada Saleem

“…The method indicates hiding the watermark in both spatial and frequency domains using Artificial Intelligent techniques, such as neural networks and genetic algorithms by dividing the watermark depending on the important information contents. …”
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Unsupervised design and geometry optimization of high-sensitivity ring-resonator-based sensors by Tanveerul Haq, Slawomir Koziel, Anna Pietrenko-Dabrowska

“…This enables not only the explicit enhancement of the circuit’s sensitivity but also ensures the attainment of the desired operating frequency. …”
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Vision-Based Branch Road Detection for Intersection Navigation in Unstructured Environment Using Multi-Task Network by Joonwoo Ahn, Yangwoo Lee, Minsoo Kim, Jaeheung Park

“…The proposed multi-task deep neural network distinguishes the branch road into a shape of rotated bounding boxes and also recognizes the drivable area to consider obstacles inside the box. …”
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Pricing in the Sharing Economy—A Hybrid Approach Leveraging Econometrics, Machine Learning, and Artificial Intelligence by Kornilios Vezyroglou, Fotios Siokis

“…This underscores the increasing importance of visual marketing in the sharing economy and the democratization of AI tools for optimizing pricing strategies. We also conduct machine learning analysis, employing algorithms like Random Forest, k-Nearest Neighbors, Support Vector Machine, Neural Network, Gradient Boosting, and AdaBoost. …”
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A survey on deep learning in UAV imagery for precision agriculture and wild flora monitoring: Datasets, models and challenges by Lorenzo Epifani, Antonio Caruso

“…In the last decade, it has been also widely used in Precision Agriculture (PA) and Wild Flora Monitoring (WFM) to address a set of problems with a big impact on economy, society and academia, heralding a paradigm shift across the industry and academia. …”
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A fact-checking tool based on Artificial Intelligence to fight disinformation on Telegram by Francisco-Javier Cantón-Correa, Lucia Ballesteros-Aguayo, Andrés Montoro-Montarroso

“… This article develops an automatic detection model based on natural learning by designing a useful tool for disinformation monitoring in Telegram that avoids the algorithmic bias of artificial intelligence. It is of relevance the monitoring of online platforms and messaging applications such as WhatsApp and Telegram because they have become tools for circumventing traditional verification controls, and, therefore, for conveying disinformation content. …”
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Reduction to master integrals and transverse integration identities by Vsevolod Chestnov, Gaia Fontana, Tiziano Peraro

“…We include some applications to cutting-edge integral families, showing significant improvements over traditional algorithms.…”
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Prediction of China’s Sulfur Dioxide Emissions by Discrete Grey Model with Fractional Order Generation Operators by Wei Meng, Daoli Yang, Hui Huang

“…Based on fractional order operators, a discrete grey model with fractional operators is developed, which also makes use of genetic algorithms to optimize the modeling parameter r. …”
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Arduino-based devices in healthcare and environmental monitoring by Nkgaphe T. Tsebesebe, Kelvin Mpofu, Sudesh Sivarasu, Patience Mthunzi-Kufa

“…Furthermore, the review highlights the use of machine learning algorithms to enhance the functionality of user-defined prototypes, aiming to realize PoC devices. …”
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Machine learning modeling for predicting adherence to physical activity guideline by Ju-Pil Choe, Seungbak Lee, Minsoo Kang

“…Variables were categorized into demographic, anthropometric, and lifestyle categories. 18 prediction models were created by 6 ML algorithms and evaluated via accuracy, F1 score, and area under the curve (AUC). …”
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