Balancing Predictive Performance and Interpretability in Machine Learning: A Scoring System and an Empirical Study in Traffic Prediction by Fabian Obster, Monica I. Ciolacu, Andreas Humpe

“…As Machine Learning algorithms become increasingly embedded in decision-making processes, particularly for traffic management and other high-level commitment applications, concerns regarding the transparency and trustworthiness of complex ‘black-box’ models have grown. …”
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Preliminary analysis of wave retrieval from Chinese Gaofen-3 SAR imagery in the Arctic Ocean by Wei-Zeng Shao, Chi Zhao, Xing-Wei Jiang, Wei-Li Wang, Wei Shen, Jun-Cheng Zuo

“…Although the analysis concludes that GF-3 SAR has the capability for wave monitoring in Arctic Ocean due to the high spatial resolution of SAR-derived wave spectra, an optimal wave retrieval algorithm needs to be developed for improving the retrieval accuracy.…”
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Analysis of injured-skin SS-OCT images based on combined attention UNet. by Xiyu Zheng, Jingyuan Wu, Qiong Ma, Diantao Luo, Qingyu Cai, Haiyang Sun, Hongxing Kang

“…To enhance image clarity, we applied noise reduction using the BM3D algorithm. We employed an improved UNet network model that incorporates SimAM and PSA modules, forming three attention mechanisms: TandemAT-UNet, ParallelAT-UNet, and NestedAT-UNet. …”
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Enhanced Occupational Safety in Agricultural Machinery Factories: Artificial Intelligence-Driven Helmet Detection Using Transfer Learning and Majority Voting by Simge Özüağ, Ömer Ertuğrul

“…This AI-driven helmet detection model demonstrates significant potential in improving occupational safety by assisting safety officers, especially in confined environments, reducing human error, and enhancing efficiency.…”
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Day-Ahead Scheduling of IES Containing Solar Thermal Power Generation Based on CNN-MI-BILSTM Considering Source-Load Uncertainty by Kun Ding, Yalu Sun, Boyang Chen, Jing Chen, Lixia Sun, Yingjun Wu, Yusheng Xia

“…The validity of the proposed model is verified by algorithm prediction simulation and day-ahead scheduling experiments under different configurations.…”
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Convergence of evolving artificial intelligence and machine learning techniques in precision oncology by Elena Fountzilas, Tillman Pearce, Mehmet A. Baysal, Abhijit Chakraborty, Apostolia M. Tsimberidou

“…Currently, many operational and technical challenges exist related to data technology, engineering, and storage; algorithm development and structures; quality and quantity of the data and the analytical pipeline; data sharing and generalizability; and the incorporation of these technologies into the current clinical workflow and reimbursement models.…”
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Establishing an AI-based diagnostic framework for pulmonary nodules in computed tomography by Ruiting Jia, Baozhi Liu, Mohsin Ali

“…The algorithm effectively handled the CT images at the preprocessing stage, and the deep learning model performed well in detecting and classifying nodules. …”
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Real time counting method for coal mine drill pipes based on deep learning by Fukai ZHANG, Yiran SUN, Xufeng WU, Aijun LI, Peiyang LI, Dengke WANG, Guan YUAN, Shan ZHAO, Haiyan ZHANG

“…It consists of two parts: the drill recognition model Drill-YOLOv8 optimized based on AM-NT and the drill pipe counting inference algorithm Pipe Count based on two-level judgment regions. …”
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REU-Net: A Remote Sensing Image Building Segmentation Network Based on Residual Structure and the Edge Enhancement Attention Module by Tianen Yuan, Bo Hu

“…Furthermore, a hybrid loss function combining edge consistency loss and binary cross-entropy loss is used to train the network, aiming to improve segmentation accuracy. Experimental results show that REU-Net(2EEAM) achieves optimal performance across multiple evaluation metrics (such as P, MPA, MIoU, and FWIoU), particularly excelling in the accurate recognition of building edges, significantly outperforming other network models. …”
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Multi-label classification for image tamper detection based on Swin-T segmentation network in the spatial domain by Li Li, Kejia Zhang, Jianfeng Lu, Shanqing Zhang

“…Our method introduces three key innovations: (1) A spatial perception module that combines the spatial rich model (SRM) with constrained convolution, enabling focused detection of tampering traces while suppressing interference from image content; (2) A hierarchical feature learning architecture that integrates Swin Transformer with UperNet for effective multi-scale tampering pattern recognition; and (3) A comprehensive optimization strategy including auxiliary supervision, self-supervised learning, and hard example mining, which significantly improves model convergence and detection accuracy. …”
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Comparative Assessment of Several Effective Machine Learning Classification Methods for Maternal Health Risk by Md Nurul Raihen, Sultana Akter

“…Maternal risk analysis can improve prenatal care, improve mother and baby health, and optimize healthcare resources by identifying misclassified observations using machine learning algorithms such as LDA, QDA, KNN, Decision Tree, Random Forest, Bagging, and Support Vector Machine, all of which have a significant impact on maternity health risk assessment. …”
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Fast Multimodal Trajectory Prediction for Vehicles Based on Multimodal Information Fusion by Likun Ge, Shuting Wang, Guangqi Wang

“…Finally, we propose a multi-stage decoder that generates more accurate and reasonable predicted trajectories by predicting trajectory reference points and performing spatial and posture optimization on the predicted trajectories. Comparative experiments with existing advanced algorithms demonstrate that our method improves the minimum Average Displacement Error (minADE), minimum Final Displacement Error (minFDE), and Miss Rate (MR) by 10.3%, 10.3%, and 14.5%, respectively, compared to the average performance. …”
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MUFFNet: lightweight dynamic underwater image enhancement network based on multi-scale frequency by Dechuan Kong, Dechuan Kong, Yandi Zhang, Xiaohu Zhao, Yanqiang Wang, Lei Cai

“…A Multi-Scale Joint Loss framework facilitates dynamic network optimization.ResultsExperimental results demonstrate that MUFFNet outperforms existing state-of-the-art models while consuming fewer computational resources and aligning enhanced images more closely with human visual perception.DiscussionThe enhanced images generated by MUFFNet exhibit better alignment with human visual perception, making it a promising solution for improving underwater robotic vision systems.…”
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Numerical Methodology for Enhancing Heat Transfer in a Channel with Arc-Vane Baffles by Piphatpong Thapmanee, Arnut Phila, Khwanchit Wongcharee, Naoki Maruyama, Masafumi Hirota, Varesa Chuwattanakul, Smith Eiamsa-ard

“…The calculations utilize the finite volume method, and the SIMPLE algorithm is executed with the QUICK scheme. For the analysis of turbulent flow, the finite volume method with the Renormalization Group (RNG) <i>k-ε</i> turbulence model was used. …”
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Cerebral gray matter volume identifies healthy older drivers with a critical decline in driving safety performance using actual vehicles on a closed-circuit course by Handityo Aulia Putra, Kaechang Park, Kaechang Park, Fumio Yamashita

“…Feature selection and classification were performed using the Random Forest machine learning algorithm, optimized to identify the most predictive GM regions.ResultsOut of 114 GM regions, eleven were selected as optimal predictors: left angular gyrus, frontal operculum, occipital fusiform gyrus, parietal operculum, postcentral gyrus, planum polare, superior temporal gyrus, and right hippocampus, orbital part of the inferior frontal gyrus, posterior cingulate gyrus, and posterior orbital gyrus. …”
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Estimation of Wind Turbine Blade Icing Volume Based on Binocular Vision by Fangzheng Wei, Zhiyong Guo, Qiaoli Han, Wenkai Qi

“…Therefore, this paper proposes a non-contact ice volume estimation method based on binocular vision and improved image processing algorithms. The method employs a stereo matching algorithm that combines dynamic windows, multi-feature fusion, and reordering, integrating gradient, color, and other information to generate matching costs. …”
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Designing and implementing a Web-based real time routing service for crisis management (a case study for district 11 of Tehran) by javad sadidi, seyed hassan hosseini sajedi

“…In this service ANP model was used to evaluate increasing interaction between the factors, and since the purpose of this study was to find the best possible routs between two nodes by non-negative weight according to the main distance factor, Dijkstra's algorithm has been chosen as a proper routing algorithm. …”
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A Reputation Value-Based Task-Sharing Strategy in Opportunistic Complex Social Networks by Jia Wu, Fangfang Gou, Wangping Xiong, Xian Zhou

“…The two submodels apply an improved particle swarm algorithm and a Lagrange multiplier, respectively. …”
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Socio-Economical Analysis of a Green Reverse Logistics Network under Uncertainty: A Case Study of Hospital Constructions by Alireza Alibakhshi, Amirreza Saffarian, Erfan Hassannayebi

“…It suggests potential future directions, such as the application of metaheuristic algorithms and improved stochastic planning methods.…”
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Blind super-resolution network based on local fuzzy discriminative loss for fabric data augmentation by Ning Dai, Xiaohan Hu, Kaixin Xu, Xudong Hu, Yanhong Yuan, Bo Cao, Luhong Shi

“…To address these challenges, this paper proposes a blind super-resolution algorithm for fabric defect data augmentation. The model is based on Real-ESRGAN and has been optimized specifically for the resolution degradation module to better adapt to the resolution degradation process in fabric images. …”
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