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	71	Comments on “Land subsidence prediction in coal mining using machine learning models and optimization techniques” Enthalten in Environmental science and pollution research 23.2.2025: 1
	72	Harnessing Advanced Machine Learning Techniques for Microscopic Vessel Segmentation in Pulmonary Fibrosis Using Novel Hierarchical Phase-Contrast Tomography (HiP-CT) Images Enthalten in Methods of information in medicine 18.02.2025
	73	Refinement of surface sterilization protocol for in vitro olive (Olea europaea L.) shoot proliferation and optimizing by machine learning techniques Enthalten in Horticulture, environment, and biotechnology 18.2.2025: 1-16
	74	Unlocking drought in pistachio orchards: monitoring and forecasting using landsat time series and machine learning techniques Enthalten in Horticulture, environment, and biotechnology 17.2.2025: 1-20
	75	Optimizing bed shear stress prediction in open flow channels: an investigation of heuristic machine learning techniques Enthalten in Natural hazards 15.2.2025: 1-37
	76	Application of Non-destructive Methods for Predicting the Concrete’s Compressive Strength, Using Hyper-Tuned Machine Learning Techniques Enthalten in Iranian journal of science and technology / Transactions of civil engineering 13.2.2025: 1-17
	77	Computational Optimization of Ceramic Waste-Based Concrete Mixtures: A Comprehensive Analysis of Machine Learning Techniques Enthalten in Archives of computational methods in engineering 12.2.2025: 1-20
	78	Application of machine learning techniques to assess surface water quality in the Sebou Basin, Morocco Enthalten in Euro-Mediterranean journal for environmental integration 7.2.2025: 1-14
	79	Optimization of delignification and organic acid synthesis from Arachis hypogaea L. shell: a comparison of machine learning approaches and experimental techniques Enthalten in Biomass Conversion and Biorefinery 7.2.2025: 1-12
	80	Predicting land use changes and ecosystem service impacts with CA-Markov and machine learning techniques Enthalten in Environment, development and sustainability 5.2.2025: 1-23

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