Sustainability Awareness in Manufacturing: A Review of IoT Audio Sensor Applications in the Industry 5.0 Era by Stefania Ferrisi

“…The synergy between these technologies enhances operational efficiency, reduces downtime, and minimizes waste, aligning with energy conservation and resource optimization goals. The use of audio sensors provides a cost-effective, non-intrusive solution for machining operations. …”
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An online clustering algorithm predicting model for prostate cancer based on PHI-related variables and PI-RADS in different PSA populations by Jiyuan Hu, Qi Miao, Jiayi Ren, Hongbo Su, Xianlu Zhang, Jianbin Bi, Gejun Zhang

“…The clustering model of the optimal cohort for PSA > 4 and PSA 4–20 sub-groups showed a superior Silhouette coefficients of 0.433 and 0.526 than that of the customized PHI cohort (0.432, 0.452). …”
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Co-seismic landslides susceptibility evaluation of Bayesian random forest considering InSAR deformation: a case study of the Luding Ms6.8 earthquake by Qiang Lin, Zhihua Zhang, Zhenghua Yang, Xinxiu Zhang, Xing Rong, Shuwen Yang, Yuan Hao, Xinyu Zhu, Wei Wang

“…Subsequently, the deformation data was combined with 11 evaluation factors, including the distance to the fault and the Peak Ground Acceleration (PGA), to model the susceptibility of landslides. We constructed four models to evaluate the susceptibility of landslides in the study area: the Bayesian optimization random forest (BO-RF) model, the random forest model (RF), the logistic regression model (LR), and the support vector machine model (SVM). …”
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Development of a Novel One-Dimensional Nested U-Net Cloud-Classification Model (1D-CloudNet) by Minjie Deng, Yong Han, Yan Liu, Li Dong, Qicheng Zhou, Yurong Zhang, Ximing Deng, Tianwei Lu

“…This model is explicitly tailored for the analysis of one-dimensional, multi-channel images. …”
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Enhanced schizophrenia detection using multichannel EEG and CAOA-RST-based feature selection by Mohammad Abrar, Abdu Salam, Ahmed Albugmi, Fahad Al-otaibi, Farhan Amin, Isabel de la Torre, Thania Candelaria Chio Montero, Perla Araceli Arroyo Gala

“…In future work, we suggest incorporating large-size datasets that include more diverse patient groups and refining the model with advanced machine-learning models and techniques.…”
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A framework of crop water productivity estimation from UAV observations: A case study of summer maize by Minghan Cheng, Ni Song, Josep Penuelas, Matthew F. McCabe, Xiyun Jiao, Yuping Lv, Chengming Sun, Xiuliang Jin

“…Key scientific findings demonstrate: (1) SEBAL outperformed FAO-56 in daily ET estimation (R² = 0.76 vs. 0.71, RMSE = 1.15 vs. 1.31 mm/d). (2) The machine learning yield model exhibited robust predictive capability (R² = 0.77, RMSE = 0.98 t/ha), successfully capturing yield variability across treatments. (3) Error propagation analysis validated framework reliability (CWP RMSE = 0.67 kg/m³), effectively differentiating CWP performance among management practices. …”
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A comprehensive diagnostic framework for hepatitis C using structured data and predictive analytics by Behnaz Motamedi, Balázs Villányi

“…This study posits that a structured preprocessing and feature selection methodology might substantially improve the classification accuracy and generalizability of machine learning (ML) models in predicting stages of hepatitis C virus (HCV) using clinical and demographic data. …”
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Retail Demand Forecasting: A Comparative Analysis of Deep Neural Networks and the Proposal of LSTMixer, a Linear Model Extension by Georgios Theodoridis, Athanasios Tsadiras

“…The results indicate that the proposed LSTMixer model is the better predictor, whilst all the other aforementioned models outperform the common statistical and machine learning methods. …”
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Tumor detection in breast cancer pathology patches using a Multi-scale Multi-head Self-attention Ensemble Network on Whole Slide Images by Ruigang Ge, Guoyue Chen, Kazuki Saruta, Yuki Terata

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Credit card default prediction using ML and DL techniques by Fazal Wahab, Imran Khan, Sneha Sabada

“…Although there has been much research investigating the efficacy of conventional Machine Learning (ML) models, there has been relatively less emphasis on Deep Learning (DL) techniques. …”
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Artificial intelligence in vaccine research and development: an umbrella review by Rabie Adel El Arab, May Alkhunaizi, May Alkhunaizi, Yousef N. Alhashem, Alissar Al Khatib, Munirah Bubsheet, Salwa Hassanein, Salwa Hassanein

“…Quality assessments were performed using the ROBIS and AMSTAR 2 tools to evaluate risk of bias and methodological rigor.ResultsAmong the 27 reviews, traditional machine learning approaches—random forests, support vector machines, gradient boosting, and logistic regression—dominated tasks from antigen discovery and epitope prediction to supply‑chain optimization. …”
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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

“…The training set was used for model construction and optimization, and the testing set for evaluating generalization ability. …”
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An Improved Decline Curve Analysis Method via Ensemble Learning for Shale Gas Reservoirs by Yu Zhou, Zaixun Gu, Changyu He, Junwen Yang, Jian Xiong

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Revealing the Relationship Between Urban Park Landscape Features and Visual Aesthetics by Deep Learning-Driven and Spatial Analysis by Jiaxuan Shi, Lyu Mei, Yumeng Meng, Weijun Gao

“…Moreover, the XGBoost model and SHAP value from machine learning were used to reveal the nonlinear relationships and significant threshold effects between urban park visual quality and five objective landscape features: openness, greenness, enclosure, vegetation diversity, and Shannon–Wiener diversity index. …”
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Tunable Energy-Efficient Approximate Circuits for Self-Powered AI and Autonomous Edge Computing Systems by Shubham Garg, Kanika Monga, Nitin Chaturvedi, S. Gurunarayanan

“…Moreover, this problem becomes more complex while deploying computationally intensive heavy machine learning (ML) models on energy-constrained edge devices. …”
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A review article on the assessment of additive manufacturing by Teshager Awoke Yeshiwas, Atalay Bayable Tiruneh, Milashu Asnake Sisay

“…Recent advancements leveraging machine learning (ML) or (AI) integration are discussed, particularly in process monitoring, defect prediction, and print quality optimization. …”
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Neuropsychological and clinical variables associated with cognitive trajectories in patients with Alzheimer's disease by Marianna Riello, Monica Moroni, Stefano Bovo, Flavio Ragni, Manuela Buganza, Raffaella Di Giacopo, Marco Chierici, Lorenzo Gios, Matteo Pardini, Matteo Pardini, Federico Massa, Federico Massa, Monica Dallabona, Elisa Vanzetta, Cristina Campi, Cristina Campi, Michele Piana, Michele Piana, Sara Garbarino, Manuela Marenco, Venet Osmani, Giuseppe Jurman, Antonio Uccelli, Antonio Uccelli, Bruno Giometto, Bruno Giometto, NeuroArtP3 Network, Filippo Gerli, Guido Pasquini, Claudia Niccolai, Matteo Betti, Emilio Portaccio, Maria Pia Amato, Rossi Andrea, Parodi Costanza, Ramaglia Antonia, Tortora Domenico, Severino Mariasavina, Andrea Falini, Antonella Castellano, Nicolò Pecco, Paola Scifo, Sonia Calloni

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Advances in Federated Learning: Applications and Challenges in Smart Building Environments and Beyond by Mohamed Rafik Aymene Berkani, Ammar Chouchane, Yassine Himeur, Abdelmalik Ouamane, Sami Miniaoui, Shadi Atalla, Wathiq Mansoor, Hussain Al-Ahmad

“…Federated Learning (FL) is a transformative decentralized approach in machine learning and deep learning, offering enhanced privacy, scalability, and data security. …”
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Dual-Resource Scheduling with Improved Forensic-Based Investigation Algorithm in Smart Manufacturing by Yuhang Zeng, Ping Lou, Jianmin Hu, Chuannian Fan, Quan Liu, Jiwei Hu

“…Hence, a new dual-resource flexible job shop scheduling problem (DRFJSP) is put forward in this paper, considering workers with flexible working time arrangements and machines with versatile functions in scheduling production, as well as a multi-objective mathematical model for formalizing the DRFJSP and tackling the complexity of scheduling in human-centric manufacturing environments. …”
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Sensitive Multispectral Variable Screening Method and Yield Prediction Models for Sugarcane Based on Gray Relational Analysis and Correlation Analysis by Shimin Zhang, Huojuan Qin, Xiuhua Li, Muqing Zhang, Wei Yao, Xuegang Lyu, Hongtao Jiang

“…Subsequently, three supervised learning algorithms—Gradient Boosting Decision Tree (GBDT), Random Forest (RF), and Support Vector Machine (SVM)—were employed to develop both single-stage and multi-stage yield prediction models. …”
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