Promoting mental health in the age of new digital tools: balancing challenges and opportunities of social media, chatbots, and wearables by Julien Coelho, Julien Coelho, Florian Pécune, Jean-Arthur Micoulaud-Franchi, Jean-Arthur Micoulaud-Franchi, Bernard Bioulac, Pierre Philip, Pierre Philip

“…Although stigma and discrimination hinder progress, these conditions are often preventable or manageable at minimal cost. The adoption of digital tools in mental health promotion, including telemedicine, online therapy, social media, and wearables, offers promising new avenues to address these issues. …”
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Scalable reinforcement learning for large-scale coordination of electric vehicles using graph neural networks by Stavros Orfanoudakis, Valentin Robu, E. Mauricio Salazar, Peter Palensky, Pedro P. Vergara

“…We further demonstrate that the proposed architecture’s flexibility allows it to be combined with most state-of-the-art deep RL algorithms to solve a wide range of problems, including those with continuous, multi-discrete, and discrete action spaces. …”
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Interpretable model based on MRI radiomics to predict the expression of Ki-67 in breast cancer by Li Zhang, Qinglin Du, Mengyi Shen, Xin He, Dingyi Zhang, Xiaohua Huang

“…The Shapley Additive Explanation (SHAP) algorithm was employed to explain the optimal model, and the AUC was used to assess the model’s performance. …”
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High-Dimensional Projected Clustering for Learner Competency Analysis in Medical Training Programs by Sandhya Harikumar, C. S. Jayamohan Pillai, V. Vani Chithra, Raghu Raman, Mr Kaimal, Kai-Yu Tang, Prema Nedungadi

“…The key contribution of this work is the process of forming a team of the most qualified medical professionals for a critical care case. …”
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Research and Analysis of Out-of-Tolerance Problem in Mold Processing Based on Intelligence by Juxian Wang

“…According to the numerical simulation results, the reasoning mechanism of an intelligent design system including instance and rule knowledge is designed, and an uncertain knowledge reasoning algorithm based on the reliability factor is proposed to improve the accuracy of mold size and reduce the overtolerance of the mold.…”
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Adaptive Feedforward Vibration Control of Helicopter Cabin Floor Driven by Piezoelectric Stack Actuators: Modeling, Simulation and Experiments by Laishou Song, Yingquan Wang, Xiaoyu Shen

“…Active control of structural response is the most practical and effective approach to mitigate helicopter vibration and enhance ride quality. …”
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Examination of Machining Parameters and Prediction of Cutting Velocity and Surface Roughness Using RSM and ANN Using WEDM of Altemp HX by I. V. Manoj, Hargovind Soni, S. Narendranath, P. M. Mashinini, Fuat Kara

“…The cutting velocity, surface roughness, recast layer, and microhardness variations were examined on the WEDMed surface. The genetic algorithm was used to optimize the cutting velocity and surface roughness, thereby improving the overall quality of the product. …”
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A comprehensive review of ball detection techniques in sports by Cristiano Moreira, Lino Ferreira, Paulo Jorge Coelho

“…This is a highly debated and researched topic, but most works focus on one sport. Effective generalization of a single method or algorithm to different sports is much harder to achieve. …”
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PATH PLANNING AND OBSTACLE AVOIDANCE METHODS FOR AUTONOMOUS MOBILE ROBOTS by Ihor Berizka

“…Research at the time of writing focuses on optimizing existing algorithms and hybridization to improve efficiency. …”
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Optimasi Algoritma Naive Bayes dengan Diskritisasi K-Means pada Diagnosis Penyakit Jantung by Nafa Fajriati, Budi Prasetiyo

“…This shows that K-means discretization plays a role in optimizing the performance of the Naïve Bayes algorithm to produce better accuracy. …”
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IoT Based Health Monitoring with Diet, Exercise and Calories recommendation Using Machine Learning by Muhammad Hassaan Naveed, Omar Bin Samin, Muhammad Bilal, Mustehsum Waseem

“…Additionally, machine learning algorithms recommend tailored exercise, diet type, Basal Metabolic Rate (BMR), and daily caloric intake based on individual member data. …”
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Predicting anemia management in dialysis patients using open-source machine learning libraries by Takahiro Inoue, Norio Hanafusa, Yuki Kawaguchi, Ken Tsuchiya

“…Performance metrics were compared across models, including XGBoost and LightGBM, to identify the most accurate algorithms. Results LightGBM and XGBoost outperformed logistic regression in predicting ESA and iron dosage changes, achieving high accuracy (e.g., area under the curve (AUC) = 0.86 for iron dosing). …”
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Distribution Ratio Prediction of Major Components in 30%TBP/kerosene-HNO3 System Based on Machine Learning by YU Ting1, ZHANG Yinyin2, ZHANG Ruizhi3, JIN Wenlei2, LUO Yingting2, ZHU Shengfeng3, HE Hui1, YE Guoan1, GONG Helin4

“…These models were trained based on different datasets, and their hyper-parameters were optimized using algorithms such as grid search, Bayesian optimization, and K-fold cross-validation. …”

Presenting a Multi-Start Hybrid Heuristic for Solving the Problem of Two-Echelon Location-Routing Problem with Simultaneous Pickup and Delivery (2E-LRPSPD) by Houming Fan, Jiaxin Wu, Xin Li, Xiaodan Jiang

“…Results indicate that MHH-PR can reduce the gap between the near optimal and global optimal solutions by 1%-2%. The proposed algorithm significantly improves computational efficiency by reducing the computational time of more than 10 min for existing cases involving 20 nodes to less than 10 s.…”
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Crop yield prediction using machine learning: An extensive and systematic literature review by Sarowar Morshed Shawon, Falguny Barua Ema, Asura Khanom Mahi, Fahima Lokman Niha, H.T. Zubair

“…Also, the most applied machine learning algorithms are Linear Regression (LR), Random Forest (RF), and Gradient Boosting Trees (GBT) whereas the most applied deep learning algorithms are Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM). …”
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Performance Statistics of Autoregressive Short and Ultrashort Signal Detectors by V. M. Kutuzov, V. P. Ipatov, S. S. Sokolov

“…Parametric spectral estimation methods provide an improved level of frequency resolution compared to matched signal processing conventionally used in radar technology. …”
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Solar panel fault diagnosis based on the intelligentrecursive method by Saadat Boulanouar, Fengal Boualem

“…It guarantees the optimal functioning of solar panels, maximizing energy production and improving return on investment. …”
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Bilinear Sequence Regression: A Model for Learning from Long Sequences of High-Dimensional Tokens by Vittorio Erba, Emanuele Troiani, Luca Biggio, Antoine Maillard, Lenka Zdeborová

“…We quantify the improvement that optimal learning brings with respect to vectorizing the sequence of tokens and learning via simple linear regression. …”
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IHML: Incremental Heuristic Meta-Learner by Onur Karadeli, Kıymet Kaya, Şule Gündüz Öğüdücü

“…The results show that the proposed model achieves more accuracy (in average % 10 and at most % 71 improvements) compared to the baseline machine learning models in the literature.…”
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A Novel Local Binary Patterns-Based Approach and Proposed CNN Model to Diagnose Breast Cancer by Analyzing Histopathology Images by Mehmet Gul

“…The histopathology images improved with the QS-LBP method were then analyzed with the most commonly used Random Forest and Optimized Forest algorithms among machine learning algorithms. …”
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