A Comprehensive Review on Sensor-Based Electronic Nose for Food Quality and Safety by Teodora Sanislav, George D. Mois, Sherali Zeadally, Silviu Folea, Tudor C. Radoni, Ebtesam A. Al-Suhaimi

“…To address these gaps, our review recommends solutions such as the adoption of adaptive machine learning models to reduce calibration needs and enhance drift resilience and the implementation of standardized protocols for data acquisition and model validation. …”
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DBSANet: A Dual-Branch Semantic Aggregation Network Integrating CNNs and Transformers for Landslide Detection in Remote Sensing Images by Yankui Li, Wu Zhu, Jing Wu, Ruixuan Zhang, Xueyong Xu, Ye Zhou

“…Considering the significant semantic gap between the encoder and decoder, a Spatial Gate Attention Module (SGAM) is used to suppress the noise from the decoder feature maps during decoding and guides the encoder feature maps based on its output, thereby reducing the semantic gap during the fusion of low-level and high-level semantic information. The DBSANet model demonstrated superior performance compared to existing models such as UNet, Deeplabv3+, ResUNet, SwinUNet, TransUNet, TransFuse, and UNetFormer on the Bijie and Luding datasets, achieving IoU values of 77.12% and 75.23%, respectively, with average improvements of 4.91% and 2.96%. …”
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Machine Learning-Driven Prediction of Brain Age for Alzheimer’s Risk: APOE4 Genotype and Gender Effects by Carter Woods, Xin Xing, Subash Khanal, Ai-Ling Lin

“…Comparing the APOE4 carriers with noncarriers, the models showed enhanced ID values and consistent brain age predictions, improving the overall performance. …”
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Comprehensive Performance Comparison of Signal Processing Features in Machine Learning Classification of Alcohol Intoxication on Small Gait Datasets by Muxi Qi, Samuel Chibuoyim Uche, Emmanuel Agu

“…Classifying all domain 22 significant features using a random forest model improved classification accuracy to 84.9%. …”
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Identification of novel gut microbiota-related biomarkers in cerebral hemorrhagic stroke by Fengli Ye, Huili Li, Hongying Li, Xiue Mu

“…Hub genes were screened using LASSO, RandomForest, and SVM-RFE algorithms. Validation was conducted in plasma samples from ICH patients (n=20) and controls (n < 20) by qRT-PCR, and in a collagenase-induced ICH mouse model. …”
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ES-Net Empowers Forest Disturbance Monitoring: Edge–Semantic Collaborative Network for Canopy Gap Mapping by Yutong Wang, Zhang Zhang, Jisheng Xia, Fei Zhao, Pinliang Dong

“…Canopy gaps are vital microhabitats for forest carbon cycling and species regeneration, whose accurate extraction is crucial for ecological modeling and smart forestry. However, traditional monitoring methods have notable limitations: ground-based measurements are inefficient; remote-sensing interpretation is susceptible to terrain and spectral interference; and traditional algorithms exhibit an insufficient feature representation capability. …”
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Randomization-Driven Hybrid Deep Learning for Diabetic Retinopathy Detection by A. M. Mutawa, G. R. Hemalakshmi, N. B. Prakash, M. Murugappan

“…We enhance the model’s diagnostic capability through complex image preprocessing techniques, such as improved noise reduction and morphological approaches. …”
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Data augmentation of time-series data in human movement biomechanics: A scoping review. by Christina Halmich, Lucas Höschler, Christoph Schranz, Christian Borgelt

“…<h4>Conclusion</h4>This review highlights the importance of data augmentation in addressing limited data availability and improving model generalization in biomechanics. Tailoring augmentation to data characteristics can enhance the performance and relevance of predictive models. …”
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Deep learning and georeferenced RGB-D imaging for hydroponic strawberry yield mapping by Camilo Pardo-Beainy, Carlos Parra, Leonardo Solaque, Won Suk Lee

“…This study evaluates four instance segmentation algorithms: YOLOv8n, YOLOv8s, YOLOv8m, and YOLOv8l, along with a low-cost GNSS RTK system to detect and count strawberries in a hydroponic environment. …”
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Optimizing solar maximum power point tracking with adaptive PSO: A comparative analysis of inertia weight and acceleration coefficient strategies by Denesh Sooriamoorthy, Aaruththiran Manoharan, Siva Kumar Sivanesan, Soon Kian Lun, Alexander Chee Hon Cheong, Sathish Kumar Selva Perumal

“…However, conventional MPPT techniques, such as Perturb and Observe (P&O), often suffer from power losses, slow convergence, and poor performance under partial shading conditions (PSC). Metaheuristic algorithms such as Particle Swarm Optimization (PSO) are explored extensively for MPPT applications. …”
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Subtypes detection of papillary thyroid cancer from methylation assay via Deep Neural Network by Andrea Colacino, Andrea Soricelli, Michele Ceccarelli, Ornella Affinito, Monica Franzese

“…Background and Objective: In recent years, DNA methylation-tumor classification based on artificial intelligence algorithms has led to a notable improvement in diagnostic accuracy compared to traditional machine learning methods. …”
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Federated Learning for privacy-Friendly Health Apps: A Case Study on Ovulation Tracking by Nikolaos Pavlidis, Andreas Sendros, Theodoros Tsiolakis, Periklis Kostamis, Christos Karasoulas, Eleni Briola, Christos Chrysanthos Nikolaidis, Vasilis Perifanis, George Drosatos, Eleftheria Katsiri, Despoina Elisavet Filippidou, Anastasios Manos, Pavlos S. Efraimidis

“…Unlike conventional centralized systems, FLORA ensures that sensitive information remains on users’ devices, with predictive algorithms powered by local computations. Blockchain technology provides immutable consent tracking and model update transparency, further improving user trust. …”
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Urban Tree Canopy Mapping and Analysis Using Iterative Annotation Method and Deep Learning: A Case Study in Beijing by Yulong Ding, Ximin Cui, Zhengchao Chen, Zeqing Wang, Debao Yuan, Xiang Meng, Xuan Yang, Yue Xu, Xiangyu Tian

“…We anticipate that the UTC map of Beijing will facilitate research on the city's ecosystem services, advance sustainable urban development, and improve the quality of life for residents.…”
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Advances in Deep Learning Applications for Plant Disease and Pest Detection: A Review by Shaohua Wang, Dachuan Xu, Haojian Liang, Yongqing Bai, Xiao Li, Junyuan Zhou, Cheng Su, Wenyu Wei

“…Additionally, this study highlights the role of large-scale pre-trained models and transfer learning in improving detection accuracy and scalability across diverse crop types and environmental conditions. …”
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Boosting grapevine phenological stages prediction based on climatic data by pseudo-labeling approach by Mehdi Fasihi, Mirko Sodini, Alex Falcon, Francesco Degano, Paolo Sivilotti, Giuseppe Serra

“…Performance metrics, including the coefficient of determination (R2) and root-mean-square error (RMSE), are employed to assess the effectiveness of the models. The study demonstrates that integrating the proposed Pseudo-labeling strategy with supervised learning approaches significantly improves predictive accuracy. …”
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MISHTI study evaluating hepatic fibrosis in type 2 diabetes patients in India by Debasis Datta, Krishna G. Seshadri, Samit Ghosal

“…Logistic regression highlighted BMI, AST, and HbA1c as key predictors of fibrosis, emphasising the need to combine non-invasive tools with clinical variables for more accurate risk stratification and improved management of MASLD. Future research should focus on refining diagnostic algorithms to better address the burden of significant fibrosis in at-risk populations.…”
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Hybrid Machine Learning-Based Fault-Tolerant Sensor Data Fusion and Anomaly Detection for Fire Risk Mitigation in IIoT Environment by Jayameena Desikan, Sushil Kumar Singh, A. Jayanthiladevi, Shashi Bhushan, Vinay Rishiwal, Manish Kumar

“…The proposed approach also deploys machine learning algorithms to dynamically adjust probabilistic models based on real-time sensor reliability, thereby improving prediction accuracy even in the presence of unreliable sensor data. …”
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Entity and Event Recognition Method for Power Grid Fault Handling Plan Based on UIE Framework by Junbo PI, Shixiong QI, Wenduo SUN, Xiansi LOU, Jiandong WO, Yue ZHANG, Tao JIANG, Lianfei SHAN

“…Through verification by the plans of different regional power grid dispatch and control centers, the proposed method has higher entity and event recognition accuracy for fault handling plans than other algorithms. It can accurately identify the fault handling strategies and recovery strategies in the plan, and provide support for the improvement of the regional power grid elasticity in the case of fault.…”
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Artificial intelligence in prostate cancer by Wei Li, Ruoyu Hu, Quan Zhang, Zhangsheng Yu, Longxin Deng, Xinhao Zhu, Yujia Xia, Zijian Song, Alessia Cimadamore, Fei Chen, Antonio Lopez-Beltran, Rodolfo Montironi, Liang Cheng, Rui Chen, Yuanyuan Ji

“…Early diagnosis, personalized treatment, and prognosis prediction of PCa play a crucial role in improving patients’ survival rates. The advancement of artificial intelligence (AI), particularly the utilization of deep learning (DL) algorithms, has brought about substantial progress in assisting the diagnosis, treatment, and prognosis prediction of PCa. …”
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Robust ground control point localization from multi-aspect stereo SAR observations using Chinese commercial satellite SVN-2 by Minzheng Mu, Zhiwei Li, Heng Zhang, Xin He, Yan Zhu, Yifan Zhang, Chenglong Cao, Xun Du, Kun Han

“…This work lays the algorithmic foundation for the automatic extraction of GCPs from stereo SAR imagery, and offers experimental support for the future application and development of Chinese commercial SAR constellations.…”
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