Reinforcement Learning With Deep Features: A Dynamic Approach for Intrusion Detection in IoT Networks by Mohamad Khayat, Ezedin Barka, Mohamed Adel Serhani, Farag Sallabi, Khaled Shuaib, Heba M. Khater

“…First, various preprocessing techniques such as handling missing values, outliers, and min-max scaling normalization were applied. …”
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Spatial Prediction of Soil Continuous and Categorical Properties Using Deep Learning Approaches for Tamil Nadu, India by Thamizh Vendan Tarun Kshatriya, Ramalingam Kumaraperumal, Sellaperumal Pazhanivelan, Nivas Raj Moorthi, Dhanaraju Muthumanickam, Kaliaperumal Ragunath, Jagadeeswaran Ramasamy

“…Irrespective of the algorithms and datasets, the R² and RMSE values of the pH attribute ranged from 0.15 to 0.30 and 0.97 to 1.15, respectively. …”
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Improving National Forest Mapping in Romania Using Machine Learning and Sentinel-2 Multispectral Imagery by Mohamed Islam Keskes, Aya Hamed Mohamed, Stelian Alexandru Borz, Mihai Daniel Niţă

“…This study addresses these challenges by integrating machine learning algorithms with high-resolution remotely sensed data and rigorously collected ground truth measurements to produce accurate, national-scale maps of forest attributes in Romania. …”
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Geoinformatics and Machine Learning for Shoreline Change Monitoring: A 35-Year Analysis of Coastal Erosion in the Upper Gulf of Thailand by Chakrit Chawalit, Wuttichai Boonpook, Asamaporn Sitthi, Kritanai Torsri, Daroonwan Kamthonkiat, Yumin Tan, Apised Suwansaard, Attawut Nardkulpat

“…This study analyzes 35 years (1988–2023) of shoreline changes using geoinformatics, machine learning algorithms (Random Forest, Support Vector Machine, Maximum Likelihood, Minimum Distance), and the Digital Shoreline Analysis System (DSAS). …”
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Genomic Analysis of Reproductive Trait Divergence in Duroc and Yorkshire Pigs: A Comparison of Mixed Models and Selective Sweep Detection by Changyi Chen, Yu He, Juan Ke, Xiaoran Zhang, Junwen Fei, Boxing Sun, Hao Sun, Chunyan Bai

“…Notably, 587 SNPs and 171 genes were uniquely detected by the LMM + ADDO method and not among loci detected by the top 5% of <i>F_ST</i> and θ_π values. Key candidate genes associated with litter size included <i>HSPG2</i>, <i>KAT6B</i>, <i>SAMD8</i>, and <i>LRMDA</i>, while <i>DLGAP1</i>, <i>MYOM1</i>, and <i>VTI1A</i> were associated with teat number traits. (3) Conclusions: This study demonstrates the power of integrating additive and dominant effect modeling with population genetics approaches for the detection of genomic regions under selection. …”
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Exploring the host response in infected lung organoids using NanoString technology: A statistical analysis of gene expression data. by Mostafa Rezapour, Stephen J Walker, David A Ornelles, Muhammad Khalid Khan Niazi, Patrick M McNutt, Anthony Atala, Metin Nafi Gurcan

“…To enhance the comprehensiveness of our analysis, we introduced a novel algorithm, namely MAS (Magnitude-Altitude Score). This innovative approach uniquely combines biological significance, as indicated by fold changes in gene expression, with statistical rigor, as represented by adjusted p-values. …”
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Real-Time Detection and Localization of Force on a Capacitive Elastomeric Sensor Array Using Image Processing and Machine Learning by Peter Werner Egger, Gidugu Lakshmi Srinivas, Mathias Brandstötter

“…This study presents a real-time force point detection and tracking system using a custom-fabricated soft elastomeric capacitive sensor array in conjunction with image processing and machine learning techniques. The system integrates Otsu’s thresholding, Connected Component Labeling, and a tailored cluster-tracking algorithm for anomaly detection, enabling real-time localization within 1 ms. …”
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Dehydrotanshinone II A alleviates osteoarthritis via activating PPARγ to inhibit ferroptosis in chondrocytes by Wenli Guan, Fahu Yuan, Xin Wang

“…Furthermore, the scTenifoldKnk algorithm was applied to perform a virtual knockout of PPARγ based on the GSE216651 single-cell RNA-seq dataset to explore its downstream regulatory effects. …”
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Bearing Response Prediction in Hydrothermal Aged Carbon Fiber Reinforced Epoxy Composite Joints Using Machine Learning Techniques by Mohit Kumar, Govind Vashishtha, Babita Dhiman, Sumika Chauhan

“…The predictive models find the value of 0.0081 RSME and 0.8 R2 respectively through support vector regression confirming that the predicted values lie in between the upper and lower bond.…”
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Wide-Angle Image Distortion Correction and Embedded Stitching System Design Based on Swin Transformer by Shiwen Lai, Zuling Cheng, Wencui Zhang, Maowei Chen

“…Experiments on synthetic and real-world datasets show that the method outperforms mainstream algorithms, with PSNR gains of 3.28 dB and 2.18 dB on wide-angle and fisheye images, respectively, while maintaining real-time performance. …”
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Multi-Agent Reinforcement Learning in Games: Research and Applications by Haiyang Li, Ping Yang, Weidong Liu, Shaoqiang Yan, Xinyi Zhang, Donglin Zhu

“…Building upon stochastic game and extensive-form game-theoretic frameworks, we establish a methodological taxonomy across three dimensions: value function optimization, policy gradient learning, and online search planning, thereby clarifying the evolutionary logic and innovation trajectories of algorithmic advancements. …”
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Design of Cloud-Based Complex VR 3D Scene Interaction Based on Attribute Preferences by Ze Yu Yang, Yeom Jun Young

“…This research sets a precedent for future advancements in VR environments, emphasizing the value of combining advanced algorithms to address complex challenges in immersive technology.…”
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QSAR Models for Predicting the Antioxidant Potential of Chemical Substances by Sofia Ghironi, Edoardo Luca Viganò, Gianluca Selvestrel, Emilio Benfenati

“…Gradient Boosting and eXtreme Gradient Boosting also achieved promising results with R² values of 0.76 and 0.75, respectively. Given these results, we developed an integrated method that not only outperformed the individual models, achieving an R² of 0.78 on the external test set, but also provided valuable insights into the range of predicted values.…”
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State of Health Prediction for Lithium-Ion Batteries Based on Gated Temporal Network Assisted by Improved Grasshopper Optimization by Xiankun Wei, Silun Peng, Mingli Mo

“…Accurate SOH prediction provides a reliable reference for lithium-ion battery maintenance. However, novel algorithms are still needed because few studies have considered the correlations between monitored parameters in Euclidean space and non-Euclidean space at different time points. …”
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Assessment of Landslide Susceptibility Based on the Two-Layer Stacking Model—A Case Study of Jiacha County, China by Zhihan Wang, Tao Wen, Ningsheng Chen, Ruixuan Tang

“…These landslide conditioning factors were integrated into a total of 4660 Stacking ensemble learning models, randomly combined by 10 base-algorithms, including AdaBoost, Decision Tree (DT), Gradient Boosting Decision Tree (GBDT), k-Nearest Neighbors (kNNs), LightGBM, Multilayer Perceptron (MLP), Random Forest (RF), Ridge Regression, Support Vector Machine (SVM), and XGBoost. …”
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Enhancing SQL Learning: Gamified tutorials and flipped classroom synergy by Sandor Kiraly, Tamas Balla, Dora Kiraly, Geoffrey Vaughan

“…These results underscore the value of integrating interactive, student-centered approaches in early SQL education.…”
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HAF-YOLO: Dynamic Feature Aggregation Network for Object Detection in Remote-Sensing Images by Pengfei Zhang, Jian Liu, Jianqiang Zhang, Yiping Liu, Jiahao Shi

“…The growing use of remote-sensing technologies has placed greater demands on object-detection algorithms, which still face challenges. This study proposes a hierarchical adaptive feature aggregation network (HAF-YOLO) to improve detection precision in remote-sensing images. …”
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Improving Sharpness-Aware Minimization Using Label Smoothing and Adaptive Adversarial Cross-Entropy Loss by Tanapat Ratchatorn, Masayuki Tanaka

“…Recent advances in learning algorithms have identified loss surface sharpness as an effective metric for reducing the generalization gap. …”
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Archetype Identification and Energy Consumption Prediction for Old Residential Buildings Based on Multi-Source Datasets by Chengliang Fan, Rude Liu, Yundan Liao

“…Furthermore, XGBoost and Random Forest machine learning algorithms were used to predict city-scale old residential building energy consumption. …”
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HMSTNet: A Deep Learning Multimodal Approach for Personalized English Literature Recommendations by Xiaopeng Wang, Xue Liang

“…This research serves to unite conventional recommendation algorithms with English literature reading requirements. …”
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