Machine learning classifiers to detect data pattern change of continuous emission monitoring system: A typical chemical industrial park as an example by Zhefeng Xu, Xiahong Shi, Wei Shu, Yilu Xin, Xuan Zan, Zhaonian Si, Jinping Cheng

“…Random Forest classifiers (RFC) consistently demonstrated high accuracy (up to 100% in specific datasets), outperforming other models, while gradient boost-based methods also excelled. Temporal emission pattern analysis revealed significant changes in 334 instances (90% confidence) across collection weeks, though only 24 aligned with regulatory offsite supervision records, highlighting discrepancies between algorithmic detection and traditional compliance checks. …”
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A Summary of Recent Advances in the Literature on Machine Learning Techniques for Remote Sensing of Groundwater Dependent Ecosystems (GDEs) from Space by Chantel Nthabiseng Chiloane, Timothy Dube, Mbulisi Sibanda, Tatenda Dalu, Cletah Shoko

“…The paper also discusses challenges in mapping GDEs and proposes mitigation strategies. …”
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Enhanced climate change resilience on wheat anther morphology using optimized deep learning techniques by Arifa Zahir, Zulfiqar Ali, Ahmad Sami Al-Shamayleh, Syed Raza Ab bas, Basharat Mahmood, Abdullah Hussein Al-Ghushami, Rubina Adnan, Adnan Akhunzada

“…Various Deep Learning algorithms, including Convolution Neural Network (CNN), LeNet, and Inception-V3 are implemented to classify the records and extract various patterns. …”
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SEARCH FOR CLINICAL PREDICTORS OF PULMONARY HYPERTENSION IN PATIENTS WITH SYSTEMIC SCLEROSIS by N. A. Klyaus, M. A. Simakova, A. L. Maslyansky, O. M. Moiseeva

“…The scope of investigations necessary to detect PH, the complexity of its diagnostic algorithms for routine use, as well as the impossibility to apply the existing algorithms for all PH variants increase the relevance of searching for novel PH predictors in patients with SSc.Objective: to reveal the relationship of capillary structural changes to the clinical and immunological subtype of SSc, disease activity, and risk for PH.Subjects and methods. …”
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ACTION: Augmentation and computation toolbox for brain network analysis with functional MRI by Yuqi Fang, Junhao Zhang, Linmin Wang, Qianqian Wang, Mingxia Liu

“…To facilitate multi-site fMRI studies, it is also equipped with several popular federated learning strategies. …”
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Privacy-Preserving Diabetes and Heart Disease Prediction via Federated Learning and WCO by Sachikanta Dash, Sasmita Padhy, Preetam Suman, Sandip Mal, Lokesh Malviya, Amrit Suman, Jaydeep Kishore

“…The method not only guarantees privacy conformity but also decreases communication expenses. FLWCO demonstrates superiority over existing federated learning algorithms in real-world heart illness prediction. …”
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Delivering data: A real-world dataset for last-mile delivery optimizationZenodo by Anna Vrani, Savvas D. Apostolidis, Athanasios Ch. Kapoutsis, Elias B. Kosmatopoulos

“…The collected matrices were processed and structured for direct use in VRP algorithms.The dataset offers substantial reuse potential by serving as a benchmark for evaluating VRP algorithms, enabling the comparison of optimization methods based on real-world logistics problems. …”
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An optimized ensemble model with advanced feature selection for network intrusion detection by Afaq Ahmed, Muhammad Asim, Irshad Ullah, Zainulabidin, Abdelhamied A. Ateya

“…In today’s digital era, advancements in technology have led to unparalleled levels of connectivity, but have also brought forth a new wave of cyber threats. Network Intrusion Detection Systems (NIDS) are crucial for ensuring the security and integrity of networked systems by identifying and mitigating unauthorized access and malicious activities. …”
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Utilizing Artificial Intelligence for Adaptive Scenario Development in Interprofessional Education by Hatice Şahin, Aysel Baser

“…However, AI-generated content may lack the emotional depth and ethical nuance essential to interprofessional collaboration. Overreliance on AI also risks reducing critical thinking and creativity, while concerns such as algorithmic bias and data privacy must be carefully addressed to ensure equitable educational practices.…”
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New Perspectives on Lung Cancer Screening and Artificial Intelligence by Leonardo Duranti, Luca Tavecchio, Luigi Rolli, Piergiorgio Solli

“…AI algorithms enhance diagnostic accuracy by automating image analysis and reducing inter-reader variability. …”
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Efficient Explainable Models for Alzheimer’s Disease Classification with Feature Selection and Data Balancing Approach Using Ensemble Learning by Yogita Dubey, Aditya Bhongade, Prachi Palsodkar, Punit Fulzele

“…Clinical data, including a range of cognitive, functional, and demographic variables, play a crucial role in Alzheimer’s disease classification. Also, challenges such as data imbalance and high-dimensional feature sets often hinder model performance. …”
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NeIL: Intelligent Replica Selection for Distributed Applications by Faraz Ahmed, Lianjie Cao, Ayush Goel, Puneet Sharma

“…We propose to use bandits with experts based Multi-Armed Bandit (MAB) algorithms and adapt these algorithms for replica selection at individual clients without centralized coordination. …”
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A comprehensive review of bibliometric and methodological approaches in flood mitigation studies: Current trends and future directions by Funmilayo Ebun Rotimi, Roohollah Kalatehjari, Taofeeq Durojaye Moshood, George Dokyi

“…Furthermore, it highlights the growing diversity of approaches, with increasing interest in machine learning algorithms and combined methods. Also, this study provides valuable recommendations for future research, emphasizing the importance of developing effective flood-mitigating strategies to enhance community resilience. …”
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Analysis of the Results of a Questionnaire Survey of Doctors and Clinical Psychologists on Somatovegetative Symptoms of Stress by Anna A. Kuzyukova, Natalia A. Levchenko, Larisa A. Marchenkova

“…They formed the basis of the questionnaire being developed, but turned out to be non-specific: they could not only be somatovegetative manifestations of stress conditions, but also be the cause of other diseases. CONCLUSION. …”
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Leveraging Machine Learning for Accurate and Fast Stellar Mass Estimation of Galaxies by Vahid Asadi, Akram Hasani Zonoozi, Hosein Haghi, Fatemeh Abedini, Atousa Kalantari, Marziye Jafariyazani, Nima Chartab

“…K-means and HDBSCAN emerge as top performers among our selected ML algorithms. Supervised learning algorithms like Random Forest and manifold learning techniques such as Pt-SNE and SOM also show promising results. …”
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Application of non-invasive monitoring technology in intensive sheep farming: A review by Jinxin Liang, Zhiyu Yuan, Xinhui Luo, Jianrui Qu, Yu Qi, Chunxin Wang

“…The results show that contact sensors have advantages such as relatively precise, continuous, and accurate monitoring of individual animals with minimal stress responses during use. However, they also have drawbacks, including wear and tear caused by stress and high equipment maintenance costs. …”
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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

“…Modern approaches to kinematic and dynamic modelling of CRs and CR control algorithms, including AI-based methods, were analyzed. …”
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Termite Detection Techniques in Embankment Maintenance: Methods and Trends by Xiaoke Li, Xiaofei Zhang, Shengwen Dong, Ansheng Li, Liqing Wang, Wuyi Ming

“…The review summarizes key principles, application scenarios, advantages, and limitations of each technique. It also highlights integrated multi-sensor frameworks and artificial intelligence algorithms as emerging solutions to enhance detection accuracy, adaptability, and automation. …”
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Correlated Photon Lidar Based on Time-Division Multiplexing by Yun Jiang, Bo Liu, Zixun Wang, Fengyun Huang, Taibei Liu, Lan Luo, Feifan He, Yongqi Yang, Bin Zhao

“…However, its susceptibility to background noise necessitates the implementation of advanced filtering techniques and complex algorithms, which can significantly increase system cost and complexity. …”
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Quantum Computing in Community Detection for Anti-Fraud Applications by Yanbo (Justin) Wang, Xuan Yang, Chao Ju, Yue Zhang, Jun Zhang, Qi Xu, Yiduo Wang, Xinkai Gao, Xiaofeng Cao, Yin Ma, Jie Wu

“…The structure also unambiguously identifies a high-risk community, which contains almost 70% of all the fraudulent accounts, demonstrating the practical utility of the method for banks’ anti-fraud business.…”
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