Cosmological Prediction of the Void and Galaxy Clustering Measurements in the CSST Spectroscopic Survey by Yingxiao Song, Qi Xiong, Yan Gong, Furen Deng, Kwan Chuen Chan, Xuelei Chen, Qi Guo, Guoliang Li, Ming Li, Yun Liu, Yu Luo, Wenxiang Pei, Chengliang Wei

“…The joint constraint accuracy of the cosmological parameters can be improved by ∼20% compared to that of the galaxy power spectrum only. …”
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Deep learning based adaptive and automatic measurement of palpebral margin in eyelid morphology by Ali Ahemaiti, Si Chen, Siwen Lei, Li Liu, Muxin Zhao

“…However, such works encounter challenges in practical implementation, and the model accuracy needs to be improved. Here, we have introduced a deep learning-based adaptive and automatic measurement (DeepAAM) by employing the U-Net architecture enhanced through attention mechanisms and multiple algorithms. …”
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Combining methylated RNF180 and SFRP2 plasma biomarkers for noninvasive diagnosis of gastric cancer by Zhihao Dai, Jin Jiang, Qianping Chen, Minghua Bai, Quanquan Sun, Yanru Feng, Dong Liu, Dong Wang, Tong Zhang, Liang Han, Litheng Ng, Jun Zheng, Hao Zou, Wei Mao, Ji Zhu

“…Materials & Methods: A total of 165 healthy individuals, 34 patients with precancerous gastric lesions, and 104 patients with confirmed GC were divided into training and validation sets; methylated RNF180 and SFRP2 were detected in circulating DNA from blood samples. Six models, including those based on logistic regression, Naive Bayes, K-nearest neighbor algorithm, glmnet, neural network, and random forest (RF) were built and validated. …”
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Predicting postoperative trauma-induced coagulopathy in patients with severe injuries by machine learning by Xiaohui Du, Wei Wang, Bo Xu, Jiang Zheng, Victor W. Xia, Yi Guo, Shuai Feng, Qingxiang Mao, Hong Fu

“…Utilizing machine learning may enhance clinical decision-making and improve management strategies for postoperative coagulation issues.…”
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YOLO-SAIL: Attention-Enhanced YOLOv5 With Optimized Bi-FPN for Ship Target Detection in SAR Images by Prabu Selvam, P. Shanmuga Sundari, M. Tamilselvi, T. Suresh, M. Murugappan, Muhammad E. H. Chowdhury

“…As a solution to these challenges, this research article introduces a new ship identification model known as YOLO-SAIL. The improved Bi-directional Feature Pyramid Network (Bi-FPN) has been replaced by a conventional Path Aggregation Network (PAN) to extract more powerful semantic features and sharpen the distinction between multi-scale targets. …”
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Reinforcement learning for an efficient and effective malware investigation during cyber incident response by Dipo Dunsin, Mohamed Chahine Ghanem, Karim Ouazzane, Vassil Vassilev

“…Future work will incorporate more advanced RL algorithms and large language models (LLMs) to further enhance the effectiveness of malware forensic analysis.…”
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A novel ensemble of hybrid starling murmuration optimized stacked gated Ghostnets for robust classification of DDoS attacks by P. Mamatha, S. Balaji, S. Sai Anuraghav

“…Multiple heterogeneous datasets related to the DOS attacks are utilised to analyse the proposed model utilising the metrics including precision, accuracy, specificity, F1-score and recall. …”
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Advances in the Automated Identification of Individual Tree Species: A Systematic Review of Drone- and AI-Based Methods in Forest Environments by Ricardo Abreu-Dias, Juan M. Santos-Gago, Fernando Martín-Rodríguez, Luis M. Álvarez-Sabucedo

“…Additionally, we discuss the challenges of model generalization across different forest ecosystems and propose future research directions, including the development of standardized datasets and improved model architectures for robust tree species classification. …”
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Advancing Arabic Word Embeddings: A Multi-Corpora Approach with Optimized Hyperparameters and Custom Evaluation by Azzah Allahim, Asma Cherif

“…For instance, in the analogy test, a larger vocabulary size significantly improved outcomes, with the FastText skip-gram models excelling in accurately solving analogy questions. …”
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Research on Concrete Crack and Depression Detection Method Based on Multi-Level Defect Fusion Segmentation Network by Zhaochen Yao, Yanjuan Li, Hao Fu, Jun Tian, Yang Zhou, Chee-Loong Chin, Chau-Khun Ma

“…On our self-constructed concrete defect dataset, MDFNet significantly outperforms the baseline model. In terms of accuracy, the MDFNet Dice coefficient is 92.4%, an improvement of 4.1 percentage points compared to YOLOv11-Seg. …”
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Machine learning-based prediction of antibiotic resistance in Mycobacterium tuberculosis clinical isolates from Uganda by Sandra Ruth Babirye, Mike Nsubuga, Gerald Mboowa, Charles Batte, Ronald Galiwango, David Patrick Kateete

“…This work demonstrates that integration of diverse data types such as genomic and clinical data could improve resistance predictions while using machine learning algorithms, support robust surveillance systems and also inform targeted interventions to curb the rising threat of antimicrobial resistance.…”
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Can Machine Learning Enhance Intrusion Detection to Safeguard Smart City Networks from Multi-Step Cyberattacks? by Jowaria Khan, Rana Elfakharany, Hiba Saleem, Mahira Pathan, Emaan Shahzad, Salam Dhou, Fadi Aloul

“…System administrators are responsible for recreating whole attack scenarios and developing improved intrusion detection systems since the systems at present are still designed to generate alerts for only single attacks with little to no correlation. …”
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Drought Detection in Satellite Imagery: A Layered Ensemble Machine Learning Approach by Muhammad Owais Raza, Naeem Ahmed Mahoto, Mana Saleh Al Reshan, Ali Alqazzaz, Adel Rajab, Asadullah Shaikh

“…The validation of the model is computed using the stratified split method. …”
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A deep contrastive learning-based image retrieval system for automatic detection of infectious cattle diseases by Veerayuth Kittichai, Morakot Kaewthamasorn, Apinya Arnuphaprasert, Rangsan Jomtarak, Kaung Myat Naing, Teerawat Tongloy, Santhad Chuwongin, Siridech Boonsang

“…The model’s performance was also improved by a fine-tuned procedure between k-nearest neighbor and its normalized distance of each data point, including precision of 0.833 ± 0.134, specificity of 0.930 ± 0.054, recall of 0.838 ± 0.118, and accuracy of 0.915 ± 0.025, respectively. …”
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opstool: A Python library for OpenSeesPy analysis automation, streamlined pre- and post-processing, and enhanced data visualization by Yexiang Yan, Yazhou Xie

“…In addition, it integrates adaptive iteration algorithms to improve convergence issues in nonlinear static and dynamic response analyses. …”
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Optimizing Wheat Yields: AI and IoT Techniques for Continuous Agricultural Monitoring by Nandy Manish, Afzal Md

“…This data is analyzed using AI algorithms developed predictive models and actionable insights. …”
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TKEO-Enhanced Machine Learning for Classification of Bearing Faults in Predictive Maintenance by Xuanbai Yu, Olivier Caspary

“…Advanced classifiers, including support vector machines and random forests, demonstrated that TKEO effectively improved model accuracy in the capture of fault-related signal dynamics. …”
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Leader-Follower Game Mechanism and Strategy of Industrial Park Demand Response with User Aggregator by Zhangyi LI, Xin MA, Wei PEI, Liang ZHANG, Mingshuang LIU, Qiuhong LIANG

“…Besides, two stage-optimization algorithms are used to solve this model. By the comparison and analysis of optimization results, it is verified that the proposed mechanism could guarantee the economic interests of the three parties as well as improve the weak positions of aggregators and enterprise users during gaming mechanism. …”
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Landslide and Collapse Susceptibility Analysis in Wenchuan Earthquake-damaged Area Based on Ensemble Learning Methods by DING Jiawei, WANG Xiekang

“…Then, two advanced ensemble learning algorithms (XGBoost and LightGBM) were applied alongside two traditional algorithms (logistic regression and random forest) to construct landslide and collapse susceptibility assessment models for Wenchuan County. …”
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Comparative Analysis of Bio-Inspired Enhancement Techniques for Localization in 2D Wireless Sensor Networks by Rabhi Seddik

“…Simulation results indicate that while PSO and FOA perform well under specific conditions, DMOA outperforms them in terms of accuracy and precision, making it a promising solution for improving node localization in WSNs. This study underscores the effectiveness of bio-inspired algorithms in enhancing the reliability and efficiency of WSN deployments. …”
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