An optimization based framework for water quality assessment and pollution source apportionment employing GIS and machine learning techniques for smart surface water governance by Abhijeet Das

“…The duration of assessment of the current research is associated for six years (2017–2023). Surface water quality assessment sought to determine whether surface water was suitable for drinking using integrated approaches such as: Decision-Making Trial and Evaluation Laboratory (DEMATEL/De)-based water quality index (WQI). …”
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A comparative analysis of five land surface temperature downscaling methods in plateau mountainous areas by Ju Wang, Ju Wang, Ju Wang, Bo-Hui Tang, Bo-Hui Tang, Bo-Hui Tang, Bo-Hui Tang, Xinming Zhu, Xinming Zhu, Xinming Zhu, Dong Fan, Dong Fan, Dong Fan, Menghua Li, Menghua Li, Menghua Li, Junyi Chen, Junyi Chen, Junyi Chen

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Application of a Hybrid Model Based on CEEMDAN and IMSA in Water Quality Prediction by GUO Li-jin, WU Hao-tian

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Seismic Foresight: A Novel Multi-Input 1D Convolutional Mixer Model for Earthquake Prediction Using Ionospheric Signals by Hakan Uyanik, Mehmet Kokum, Erman Senturk, Mohamed Freeshah, Salih T. A. Ozcelik, Muhammed Halil Akpinar, Serenay Celik, Abdulkadir Sengur

“…The model architecture features depth-wise and point-wise convolutions with patch embedding, utilizing uses four tunable hyperparameters: network depth, hidden dimension size, kernel size, and patch size. …”
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Prediction of Rut Depth in Soil Caused by Wheels Using Artificial Neural Networks by N. Farhadi, A. Mardani, A. Hosainpour, B. Golanbari

“…These findings highlight the potential of machine learning in modeling complex interactions between soil and vehicles, which can enhance vehicle performance, mitigate soil erosion, and guide the design of off-road vehicles. Future research directions could include investigating additional soil parameters, various vehicle configurations, and the real-world implementation of autonomous off-road vehicles to promote more environmentally sustainable operations.…”
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Low-Code Application and Practical Implications of Common Machine Learning Models for Predicting Punching Shear Strength of Concrete Reinforced Slabs by Khuong Le Nguyen, Thanh Tu Do, Giang Huu Nguyen, Afaq Ahmad

“…RandomSearch and Bayesian optimisation were employed for tuning hyperparameters. The performance of these models was compared with six empirical models, which included three current design codes, three equations from other researchers, and 227 finite-element simulations conducted by the authors. …”
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Genetic algorithm–optimized support vector machine for real-time activity recognition in health smart home by Yan Hu, Bingce Wang, Yuyan Sun, Jing An, Zhiliang Wang

“…However, most of the existing researches focus on offline activity recognition, but not good at online real-time activity recognition. …”
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Long Short-Term Memory Networks for the Automated Identification of the Stationary Phase in Tribological Experiments by Yuxiao Zhao, Leyu Lin, Alois K. Schlarb

“…The process of fine-tuning key hyperparameters was systematically optimized through Bayesian optimization, coupled with K-fold cross-validation to minimize the inherent randomness associated with training neural networks. …”
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DeepGenMon: A Novel Framework for Monkeypox Classification Integrating Lightweight Attention-Based Deep Learning and a Genetic Algorithm by Abdulqader M. Almars

“…To facilitate the early detection of monkeypox, researchers have proposed several AI-based techniques for accurately classifying and identifying the condition. …”
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Unveiling diabetes onset: Optimized XGBoost with Bayesian optimization for enhanced prediction. by Muhammad Rizwan Khurshid, Sadaf Manzoor, Touseef Sadiq, Lal Hussain, Mohammed Shahbaz Khan, Ashit Kumar Dutta

“…Leveraging Bayesian optimization to fine-tune XGBoost, researchers can harness the power of complex data analysis to improve predictive accuracy. …”
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Design and Optimization of Hybrid CNN-DT Model-Based Network Intrusion Detection Algorithm Using Deep Reinforcement Learning by Lu Qiu, Zhiping Xu, Lixiong Lin, Jiachun Zheng, Jiahui Su

“…With the rapid development of network technology, modern systems are facing increasingly complex security threats, which motivates researchers to continuously explore more advanced intrusion detection systems (IDSs). …”
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The Impact of the Exchange Fees on Impermanent Loss of Liquidity Providers for Conservative Automated Market Makers by Roman Vlasov, Vladimir Gorgadze, Artem Barger

“…The article may be useful for both practitioners and researchers in the field of decentralised finance seeking a deeper understanding of the dynamics of automated market-making in an ever-changing DeFi environment. …”
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Comparative Analysis of Machine Learning Models for Predicting Innovation Outcomes: An Applied AI Approach by Marko Martinović, Kristian Dokic, Dalibor Pudić

“…Predicting innovation outcomes at the firm level continues to be an important but challenging goal for researchers and practitioners alike. In this study, multiple machine learning models, encompassing both ensemble-based and single-model approaches, were applied to data from the Community Innovation Survey. …”
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CNN Assisted Dual Reservoir Hybrid Network for Power Consumption Forecasting by Hammad I. Sherazi, Omar Alrumayh, Shabana Habib, Abdulrahman Alsafrani, Muhammad Islam, Hedi A. Guesmi

“…Developing a precise forecasting model for power usage is an essential requirement for modern power management systems. As a result, researchers are focusing their efforts on proposing effective and accurate energy forecasting techniques which are crucial for future energy management and planning in smart grids. …”
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An optimized LSTM-based deep learning model for anomaly network intrusion detection by Nitu Dash, Sujata Chakravarty, Amiya Kumar Rath, Nimay Chandra Giri, Kareem M. AboRas, N. Gowtham

“…Over time, intrusion detection systems have been instrumental in identifying network breaches and deviations. Several researchers have recommended the implementation of machine learning approaches in IDSs to counteract the menace posed by network intruders. …”
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A two phase ensembled deep learning approach of prominent gene extraction and disease risk prediction by Prajna Paramita DEBATA, Alakananda TRIPATHY, Pournamasi PARHI, Smruti Rekha DAS

“… Unlocking novel insights from gene expression of individual patient profiles, clinicians and researchers can discern patterns, biomarkers, and therapies. …”
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Output Power Prediction of a Photovoltaic Module Through Artificial Neural Network by Muhammad Aseer Khan, Muhammad Arqum Khan, Husan Ali, Bilal Ashraf, Shahbaz Khan, Dur-E-Zehra Baig, Abdul Wadood, Tahir Khurshaid

“…A detailed analysis and preprocessing of the data is carried out through Pearson’s correlation method prior to training. The hyperparameters of Neural Network tuning are selected through heuristic method. …”
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Classifying detrital zircon U-Pb age distributions using automated machine learning by Jack W. Fekete, Glenn R. Sharman, Xiao Huang

“…The prodigious use of detrital zircon U-Pb geochronology for provenance studies in recent decades has led many researchers to amass extensive datasets (>100,000 dates). …”
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Intrusion Detection System for Network Security Using Novel Adaptive Recurrent Neural Network-Based Fox Optimizer Concept by R. Manivannan, S. Senthilkumar

“…Abstract The majority of daily networks and communications rely heavily on network security. Researchers in cybersecurity emphasize the necessity of developing effective intrusion detection systems (IDS) to safeguard networks. …”
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DRCO: Dense-Label Refinement and Cross Optimization for Semi-Supervised Object Detection by Yunlong Qin, Yanjun Li, Feifan Ji, Yan Liu, Yu Wang, Ji Xiang

“…However, there are still relatively few researches focused on the dense pseudo-labeling paradigm. …”
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