A Crowd Counting and Localization Network Based on Adaptive Feature Fusion and Multi-Scale Global Attention Up Sampling by Min Wang, Li Huang, Jingke Yan, Jin Huang, Tao Yang

“…The F1-m scores obtained on the SHHA and SHHB datasets reach 72.9% and 79.4% respectively, significantly improving the model’s performance.Finally, this paper extends the application of crowd counting and localization algorithms to different domains such as citrus orchards, vehicles, and campus crowds. …”
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Use of Aposteriori Information in the Implementation of Radar Recognition Systems Using Neural Network Technologies by Dmitrii F. Beskostyi, Sergei G. Borovikov, Yurii V. Yastrebov, Ilya A. Sozontov

“…The current need to obtain relevant, complete and reliable information about airborne objects has led to the continuous improvement of modern radar recognition systems (MRRS) as part of control systems. …”
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Monitoring Pine Wilt Disease Using High-Resolution Satellite Remote Sensing at the Single-Tree Scale with Integrated Self-Attention by Wenhao Lv, Junhao Zhao, Jixia Huang

“…The results demonstrate that introducing appropriate self-attention algorithms significantly improves detection accuracy for pine wilt disease. …”
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Comparing the Effectiveness of Artificial Intelligence Models in Predicting Ovarian Cancer Survival: A Systematic Review by Farkhondeh Asadi, Milad Rahimi, Nahid Ramezanghorbani, Sohrab Almasi

“…ABSTRACT Background This systematic review investigates the use of machine learning (ML) algorithms in predicting survival outcomes for ovarian cancer (OC) patients. …”
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Frame Theory and Fractional Programming for Sparse Recovery-Based mmWave Channel Estimation by Razvan-Andrei Stoica, Hiroki Iimori, Giuseppe Thadeu Freitas de Abreu, Koji Ishibashi

“…Simulation results confirm the channel estimation accuracy improvements obtained by both contributions.…”
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A Lightweight Model for Shine Muscat Grape Detection in Complex Environments Based on the YOLOv8 Architecture by Changlei Tian, Zhanchong Liu, Haosen Chen, Fanglong Dong, Xiaoxiang Liu, Cong Lin

“…Compared with the latest YOLOv11n, our model achieved a 3.3% improvement in mAP, with reductions of 26.4% in parameters, 14.3% in FLOPs, and 14.6% in inference time, demonstrating comprehensive enhancements. …”
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Design of a microservices-based architecture for residential energy efficiency monitoring by Ivonne Nuñez, Carlos Rovetto, Edmanuel Cruz, Andrzej Smolarz, Dimas Concepcion, Elia Esther Cano

“…The conclusions highlight the importance of energy efficiency in reducing the environmental impact and costs associated with electric power, suggesting future improvements in data access and the implementation of advanced machine learning algorithms.…”
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Forecast Padding Enhances Accuracy and Robustness of EEG-Phase-Synchronized TMS by Yu-Cheng Chang, Pin-Hsuan Chao, Yan-Ming Kuan, Chiu-Jung Huang, Li-Fen Chen, Wei-Chung Mao, Tung-Ping Su, Sin-Horng Chen, Chun-Shu Wei

“…Through this framework, we demonstrate that forecast padding significantly improves both stimulation accuracy and deadlock rate. …”
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Continuum Robots: Current Trends in Design, Control, and Applications by Marina Kalekeyeva, Zarina Konakbay, Meiramgul Imanbekova, Aliya Shakbutova, Olga Garmash, Gulzhan Muratbekova, Doszhan Mambetalin

“…Despite promising achievements, the processes of design, modeling, control system development, and improvement of existing CR mechanisms remain quite complex. …”
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OCT5k: A dataset of multi-disease and multi-graded annotations for retinal layers by Mustafa Arikan, James Willoughby, Sevim Ongun, Ferenc Sallo, Andrea Montesel, Hend Ahmed, Ahmed Hagag, Marius Book, Henrik Faatz, Maria Vittoria Cicinelli, Amani A. Fawzi, Dominika Podkowinski, Marketa Cilkova, Diana Morais De Almeida, Moussa Zouache, Ganesham Ramsamy, Watjana Lilaonitkul, Adam M. Dubis

“…Abstract Publicly available open-access OCT datasets for retinal layer segmentation have been limited in scope, often being small in size, specific to a single disease, or containing only one grading. This dataset improves upon this with multi-grader and multi-disease labels for training machine learning-based algorithms. …”
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Artificial Intelligence in the Organization of Nursing Care: A Scoping Review by João Ventura-Silva, Maria Manuela Martins, Letícia de Lima Trindade, Ana da Conceição Alves Faria, Soraia Pereira, Samuel Spiegelberg Zuge, Olga Maria Pimenta Lopes Ribeiro

“…Conclusions: AI tools such as automated alert systems, predictive algorithms, and decision support transform nursing by increasing efficiency, accuracy, and patient-centered care, improving communication, reducing errors, and enabling earlier interventions with safer and more efficient quality care.…”
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Single Vector Hydrophone DOA Estimation: Leveraging Deep Learning with CNN-CBAM by Fanyu ZENG, Yaning HAN, Hongyuan YANG, Dapeng YANG, Fan ZHENG

“…However, traditional direction of arrival (DOA) estimation algorithms often struggle to maintain high accuracy in nonstationary noise conditions. …”
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Capsule Endoscopy Image Enhancement for Small Intestinal Villi Clarity by Shaojie Zhang, Yinghui Wang, Peixuan Liu, Yukai Wang, Liangyi Huang, Mingfeng Wang, Ibragim Atadjanov

“…Experimental results show that our proposed method achieved a 45.47% increase in PSNR compared to classical enhancement algorithms, a 12.63% improvement in IRMLE relative to the original images, and a 31.84% reduction in NIQE with respect to the original images.…”
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Lightweight Deepfake Detection Based on Multi-Feature Fusion by Siddiqui Muhammad Yasir, Hyun Kim

“…Moreover, the features extracted with a histogram of oriented gradients (HOG), local binary pattern (LBP), and KAZE bands were integrated to evaluate using random forest, extreme gradient boosting, extra trees, and support vector classifier algorithms. Our findings show a feature-level fusion of HOG, LBP, and KAZE features improves accuracy to 92% and 96% on FaceForensics++ and Celeb-DF(v2), respectively.…”
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A lightweight deep-learning model for parasite egg detection in microscopy images by Wenbin Xu, Qiang Zhai, Jizhong Liu, Xingyu Xu, Jing Hua

“…The YOLOv5n model is used as the baseline model, and then two improvements are made to the baseline model based on the specificity of the egg data. …”
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NOVEL METHODS FOR AUTOANTIBODY DETECTION IN LABORATORY DIAGNOSTICS OF AUTOIMMUNE RHEUMATIC DISEASES by D. Roggenbuck, P. Schierac, U. Sack, S. V. Lapin, A. V. Mazing, Areg A. Totolian

“…Novel technical developments in the area of digital analysis of immunofluorescent images paved the way for the improvement of intra- and interlaboratory standardizationof test results. …”
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The methodology for reactive power control to ensure voltage quality using fuzzy logic by Kholiddinov Ilkhombek, Sharobiddinov Mirzokhid, Kholiddinova Mashkhurakhon, Komolddinov Sokhib, Qodirov Afzal, Tukhtasinov Saidislomkhon

“…The benefits of utilizing fuzzy logic for controlling reactive power and improving regulation are highlighted, along with assessments of future developments in this field. …”
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Research Progress on Application of Intelligent Operation and Maintenance Models in Medical Equipment Management by Jin LI, Xiu XU, Jing TONG, Wei JIN, Chenge WANG, Ruiyao JIANG

“…It also proposes issues that need improvement in intelligent O&M models, aiming to provide valuable references for the future development of medical equipment management.…”
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Computer vision based automatic evaluation method of Y2O3 steel coating performance with SEM image by Jianhong Zhao, Huamin Yang, Yi Sui

“…The results demonstrate the method’s potential in automating and improving the reliability of microstructural analysis in materials science.…”
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Preface to Special Issue on Scientific Computing and Learning Analytics for Smart Healthcare Systems (Part II) by Chinmay Chakraborty, Sayonara Barbosa, Lalit Garg

“…This special issue offers valuable insights to researchers and engineers on designing intelligent bio-inspired Health 4.0 technologies and improving remote patient information delivery and care. …”
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