Integrating Multi-Variable Driving Factors to Improve Land Use & Land Cover Classification Accuracy using Machine Learning Approaches: A Case Study from Lombok Island by Miftahul Irsyadi Purnama, Hüseyin Oğuz Çoban

“…In field validation assessments, comparing the predictions of these machine learning models with ground truth data, Random Forest was the most reliable, achieving an overall accuracy of 88%. …”
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Detecting Obfuscated Malware Infections on Windows Using Ensemble Learning Techniques by Yadigar Imamverdiyev, Elshan Baghirov, John Chukwu Ikechukwu

“…The findings contribute to cybersecurity efforts by comprehensively assessing machine learning algorithms for obfuscated malware detection through memory analysis. …”
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History of Location Objectives (Hilal Tracker): Development of Astronomical Observations from 2015-Present by Marataon Ritonga, Alya Fathi Muhammad Hasibuan, Rizqan Arya Perdana Nazara

“…This article explains the history of the development of hilal Tracker technology and methods, including integrating astronomical data, using satellite imagery, algorithms for predicting hilal visibility, and cooperation with various local and international institutions. …”
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Knowledge graph convolutional networks with user preferences for course recommendation by Zhong Hua, Jianbai Yang, Weidong Ji

“…Research on KG-enhanced recommendation algorithms has found that the utilization of side information in KGs is crucial for improving accuracy. …”
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AI-Driven Gamification and Biotechnical Systems in Cardiovascular Monitoring of Cyclic Sport Athletes by Shukurova Sayyora, Pulatova Mavlyuda, Adilbekov Taxir

“…The paper attempts to move forward research in biotechnical systems and gamification from traditional sports medicine, athlete conditioning, and exercise physiology contexts to emerging AI-enhanced frameworks that address current cardiovascular health challenges.In proposing such a novel approach, the authors reason why AHP-regression-based studies may be particularly suited for the iterative assessment, validation, and optimization of findings in the form of decision-making models such as personalized AI-driven training algorithms. Additionally, the analytical hierarchy process (AHP) framework is used to organize a systematic evaluation of predictive modeling techniques to identify some best practices related to specific biométrie parameters and athlete performance metrics.This methodological application then furthers the examination of the physiological and computational implications related to the use of AI-driven cardiovascular monitoring in terms of accuracy, efficiency, and adaptability. …”
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From Classic to Cutting-Edge: A Near-Perfect Global Thresholding Approach with Machine Learning by Nicolae Tarbă, Costin-Anton Boiangiu, Mihai-Lucian Voncilă

“…We also compared our results with state-of-the-art binarization algorithms and outperformed them on certain datasets. …”
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A Modular, Model, Library Framework (DebrisLib) for Non-Newtonian Geophysical Flows by Ian E. Floyd, Alejandro Sánchez, Stanford Gibson, Gaurav Savant

“…Numerical modelers have developed a variety of non-Newtonian algorithms to simulate this range of physical processes. …”
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Machine learning provides reconnaissance-type estimates of carbon dioxide storage resources in oil and gas reservoirs by Emil Attanasi, Philip Freeman, Timothy Coburn

“…We demonstrate the application of four different ML algorithms using data from onshore and offshore oil and gas reservoirs in Europe, and show they perform well when predictions are compared to engineering estimates. …”
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A Machine Learning-Based Real-Time Remaining Useful Life Estimation and Fair Pricing Strategy for Electric Vehicle Battery Swapping Stations by Seyit Alperen Celtek, Seda Kul, A. Ozgur Polat, Hamed Zeinoddini-Meymand, Farhad Shahnia

“…The paper evaluates various machine learning algorithms for real-time RUL estimation regarding accuracy, computation time, and memory usage. …”
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Investigating the Use of Machine Learning Models to Understand the Drugs Permeability Across Placenta by Vaisali Chandrasekar, Mohammed Yusuf Ansari, Ajay Vikram Singh, Shahab Uddin, Kirthi S. Prabhu, Sagnika Dash, Souhaila Al Khodor, Annalisa Terranegra, Matteo Avella, Sarada Prasad Dakua

“…Owing to limited drug testing possibilities in pregnant population, the development of computational algorithms is crucial to predict the fate of drugs in the placental barrier; it could serve as an alternative to animal testing. …”
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Downscaling Satellite Night-Time Light Imagery While Addressing the Blooming Effect by Nikolaos Tziokas, Ce Zhang, Alexandros Tziokas, Qunming Wang, Peter M. Atkinson

“…We compared several image fusion algorithms for downscaling while reducing the blooming effect. …”
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A Comparative Analysis of Machine Learning and Pedotransfer Functions Under Varying Data Availability in Two Greek Regions by Panagiotis Tziachris, Panagiota Louka, Eirini Metaxa, Miltiadis Iatrou, Konstantinos Tsiouplakis

“…The current study evaluates the performance of pedotransfer functions (PTFs) and machine learning (ML) algorithms in predicting the soil bulk density (BD) across two distinct regions in Greece—Kozani and Veroia—using both limited and extended sets of soil parameters. …”
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Automated interpretation of deep learning-based water quality assessment system for enhanced environmental management decisions by Javed Mallick, Saeed Alqadhi, Majed Alsubih, Mohamed Fatahalla Mohamed Ahmed, Hazem Ghassan Abdo

“…Methodologically, the study used CNN algorithms optimised by Bayesian techniques for the prediction of eight water quality indices, coupled with SHAPley Additive exPlanations (SHAP) analysis under XAI to interpret the complex decision-making processes of these models. …”
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Benchmark dataset on feeding intensity of the pearl gentian grouper(Epinephelus fuscoguttatus♀×E. lanceolatus♂) by Haijing Qin, Yunchen Tian, Jianing Quan, Xueqi Cong, Qingfei Li, Jinzhu Sui

“…In addition, training on this benchmark dataset proposes an improved feeding intensity evaluation network, which achieves a good balance in prediction accuracy and parameter memory and offers the possibility of subsequent deployment of the model on mobile. …”
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Seismic Identification and Characterization of Deep Strike-Slip Faults in the Tarim Craton Basin by Fei Tian, Wenhao Zheng, Aosai Zhao, Jingyue Liu, Yunchen Liu, Hui Zhou, Wenjing Cao

“…This model incorporates select seismic attributes and leverages fusion algorithms like K-means, ellipsoid growth, and wavelet transformations. …”
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Goal-oriented autonomous decision-making for social robots via collaborative interactive inverse reinforcement learning approach by Mingyue Luo, Hui Li, Wanbo Luo, Hewei Li, Jianan Li

“…Moreover, trajectory prediction at longer distances also poses significant challenges for such trajectory planning methods. …”
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Geostatistics and artificial intelligence coupling: advanced machine learning neural network regressor for experimental variogram modelling using Bayesian optimization by Saâd Soulaimani, Saâd Soulaimani, Ayoub Soulaimani, Kamal Abdelrahman, Abdelhalim Miftah, Mohammed S. Fnais, Biraj Kanti Mondal

“…One part of this AI approach is the use of population search algorithms to fine-tune hyperparameters for better prediction performing. …”
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A Survey of Matrix Completion Methods for Recommendation Systems by Andy Ramlatchan, Mengyun Yang, Quan Liu, Min Li, Jianxin Wang, Yaohang Li

“…The collaborative filtering problem can be viewed as predicting the favorability of a user with respect to new items of commodities. …”
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Data-driven discovery of ultrahigh specific hardness alloys by Taeyeop Kim, Wook Ha Ryu, Geun Hee Yoo, Donghyun Park, Ji Young Kim, Eun Soo Park, Dongwoo Lee

“…This study employed an iterative process of ML prediction paired with combinatorial experimental verification to discover new ternary alloys with ultrahigh-specific hardness. …”
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Resilience evaluation of memristor based PUF against machine learning attacks by Hebatallah M. Ibrahim, Heorhii Skovorodnikov, Hoda Alkhzaimi

“…Our results yield low accuracy and ROC results of within $$0.49-0.52$$ 0.49 - 0.52 and $$0.49-0.52$$ 0.49 - 0.52 respectively, indicating failure in predicting random data demonstrates efficient randomness prediction resiliency of the MR-PUF. …”
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