AI-Driven Drought Monitoring: Advanced Machine Learning Techniques for Early Prediction by Vij Priya, Tiwari Ankita

“…Moreover, the research conducts a comparative evaluation of various machine learning models to identify the most effective algorithms for different climatic zones and crop types. …”
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An Exploration of the Use of Educational Robotics in Preschool Education by Veronica Mihaela Rus, Beatrice Almășan

“…The results show that the use of educational robots in different stages of teaching, learning and evaluation helps in the development process of children by understanding the cause-effect relationship, acquiring spatial and temporal orientation skills, developing communication skills and supporting socio-emotional interactions with children of the same age. …”
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Research on the Forward Simulation and Intelligent Detection of Defects in Highways Using Ground-Penetrating Radar by Pengxiang Li, Mingzhou Bai, Xin Li, Chenyang Liu

“…An intelligent recognition platform was established, and a radar image dataset was created and trained to evaluate the recognition performance of the You Only Look Once (YOLO) v3 and Single-Shot Multi-Box Detector (SSD) algorithms. …”
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Quantum DeepONet: Neural operators accelerated by quantum computing by Pengpeng Xiao, Muqing Zheng, Anran Jiao, Xiu Yang, Lu Lu

“…However, classical DeepONet entails quadratic complexity concerning input dimensions during evaluation. Given the progress in quantum algorithms and hardware, here we propose to utilize quantum computing to accelerate DeepONet evaluations, yielding complexity that is linear in input dimensions. …”
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Free field Active Noise Control system development using a 3D Finite Element based approach by Budnik Maximilian, Mees Valentin

“…It further allows for the optimization of control algorithms, evaluation of the attenuation performance, and methodical iteration of the overall concept design. …”
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Techno-economic optimization of hybrid renewable energy system for islands application by Mohammad Toudefallah, Panagiotis Stathopoulos

“…The analysis further indicates that the differences between minute-by-minute and hourly optimization diminish as the volatility of the input data to the algorithm decreases.…”
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Noninvasive prediction of meningioma brain invasion via multiparametric MRI⁃based brain⁃tumor interface radiomics by CHENG Xing, WANG Zhi⁃chao, LI Hua⁃ning, WANG Xie⁃feng, YOU Yong⁃ping

“…Decision curve analysis (DCA) was performed to evaluate the clinical net benefit of the models across different threshold probabilities. …”
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Integrative radiomics of intra- and peri-tumoral features for enhanced risk prediction in thymic tumors: a multimodal analysis of tumor microenvironment contributions by Liang zhu, Jiamin Li, Xuefeng Wang, Yan He, Siyuan Li, Shuyan He, Biao Deng

“…These selected features were then used to train machine learning models, which were optimized on the training dataset and assessed for predictive performance. To further evaluate the effectiveness of these models, various statistical methods were applied, including DeLong’s test, NRI, and IDI, to compare predictive differences among models. …”
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A novel early stage drip irrigation system cost estimation model based on management and environmental variables by Masoud Pourgholam-Amiji, Khaled Ahmadaali, Abdolmajid Liaghat

“…The two LCA and FOA algorithms produced the best estimation, according to the evaluation criteria results. …”
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Green Infrastructure and Integrated Optimisation Approach Towards Urban Sustainability: Case Study in Altstetten-Albisrieden, Zurich by Yingying Jiang, Sacha Menz

“…A 10-m rectangular grid was employed to visualise and integrate the evaluation results from different perspectives. Furthermore, clustering algorithms were utilised to generate spatial patterns indicative of unique land characteristics. …”
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A comprehensive Malabar Spinach dataset for diseases classificationMendeley Data by Mushfiqur Rahman, Md Al Mamun

“…This study seeks to develop reliable disease detection models through the examination of different image processing techniques and deep learning algorithms such as ResNet50. …”
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Massive Acquisition of Ultraviolet Color Excess Information from GALEX and UVOT Bands by Dongliang Yang, Mingxu Sun, Biwei Jiang, Wenyuan Cui, Ruoyi Zhang, Luyao Shi, Jiachen Wei

“…By considering the impact of stellar parameters ( T _eff , [Fe/H], and ${\rm{log}}\,g$ ) on intrinsic color indices and utilizing the blue-edge method combined with a random forest algorithm, an empirical relationship between stellar parameters and intrinsic ultraviolet color indices is established. …”
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Maximizing Energy Efficiency in IRS-Assisted Phase Cooperative PS-SWIPT-Based Self-Sustainable IoT Network by Haleema Sadia, Ahmad Kamal Hassan, Ziaul Haq Abbas, Ghulam Abbas, Thar Baker, Nasir Saeed

“…The proposed solutions’ analysis shows low computational complexity and fast convergence, achieving near-optimal EE performance for different network settings. We conducted extensive simulations to evaluate our proposed framework’s performance and unveiled significant EE performance achieved by the IoT network consistent with the numerical findings. …”
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Vis/NIR Spectroscopy and Vis/NIR Hyperspectral Imaging for Non-Destructive Monitoring of Apricot Fruit Internal Quality with Machine Learning by Tiziana Amoriello, Roberto Ciorba, Gaia Ruggiero, Francesca Masciola, Daniela Scutaru, Roberto Ciccoritti

“…The fruit supply chain requires simple, non-destructive, and fast tools for quality evaluation both in the field and during the post-harvest phase. …”
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Inverse Dynamic Parameter Identification for Remote Sensing of Soil Moisture From SMAP Satellite Observations by Runze Zhang, Adam Watts, Mohamad Alipour

“…However, there is no consistent trend in the magnitudes of dynamic <italic>h</italic> and &#x03C9; between different algorithms. Identifying the most effective dynamic <italic>h</italic> and &#x03C9; parameters within the SMAP algorithmic framework necessitates not only selecting an appropriate parameter range but also accurately tracking the temporal evolutions of surface roughness and vegetation scattering. …”
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Reliability Analysis of Three-dimensional Soil Slopes Considering Spatial Variability of Soil Parameters by Wan Yukuai, Zhou Yuqi, Shao Linlan, Wang Yuke, Zhang Fei

“…To complement the random field modeling, an enhanced PSO algorithm is proposed for 3D slope stability analysis. …”
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Interpretable machine learning analysis of environmental characteristics on bacillary dysentery in Sichuan Province by Yao Zhang, Qiao-Lin Wang, Wei Peng, Meng-Yuan Zhang, Yao Qin, Lun Zhang, Rong-Jie Wei, Dian-Ju Kang

“…The eXtreme Gradient Boosting (XGBoost) algorithm was employed to assess the influence of key environmental features, including precipitation, temperature, PM10, potential evaporation, vegetation cover, and NDVI, on BD incidence. …”
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To accurately predict lymph node metastasis in patients with mass-forming intrahepatic cholangiocarcinoma by using CT radiomics features of tumor habitat subregions by Pengyu Chen, Zhenwei Yang, Peigang Ning, Hao Yuan, Zuochao Qi, Qingshan Li, Bo Meng, Xianzhou Zhang, Haibo Yu

“…Using information from the arterial and venous phases of multisequence CT images, tumor habitat subregions were delineated through the K-means clustering algorithm. Radiomic features were extracted and screened, and prediction models based on different subregions were constructed and compared with traditional intratumoral models. …”
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Ultrasound-based radiomic nomogram for predicting the invasive status of breast cancer: a multicenter study by Dan Yan, Jingwen Xie, Wanling Cheng, Wen Xue, Yaohong Den, JianXing Zhang

“…A total of 1125 radiomic features were extracted from the training set of CUS images, and Radiomics Scores (Rad-scores) were constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Different nomogram models were constructed using logistic regression, including a clinical–radiomics model (Clinic + Rad), a CUS–clinical model (CUS + Clinic), and a combined CUS–clinical–radiomics model (CUS + Clinic + Rad). …”
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Triboinformatic analysis and prediction of B4C and granite powder filled Al 6082 composites using machine learning regression models by Amit Aherwar, Anamika Ahirwar, Vimal Kumar Pathak

“…The developed model’s results were evaluated utilizing a number of statistical metrics to identify the most reliable algorithm for wear and COF prediction. …”
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