An Optical Sensor for Measuring In-Plane Linear and Rotational Displacement by Suhana Jamil Ahamed, Michael Aaron McGeehan, Keat Ghee Ong

“…The reliability of the predicted linear and rotational displacement results was evaluated using two different color gradient wheels: a wheel with changing color hue, and another wheel with changing color hue and saturation. …”
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Angle Estimation for Range-Spread Targets Based on Scatterer Energy Focusing by Zekai Huang, Peiwu Jiang, Maozhong Fu, Zhenmiao Deng

“…Finally, the angle of the energy-focused signal is estimated using the sum-and-difference amplitude comparison method. The proposed method can effectively focus the scatterers’ energy. …”
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Using N-Version Architectures for Railway Segmentation with Deep Neural Networks by Philipp Jaß, Carsten Thomas

“…In addition, we implement a new method for evaluating and combining prediction confidence values in the N-version architecture during runtime. …”
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Lightweight terminal cross-domain authentication protocol in edge computing environment by Hongying ZHU, Xinyou ZHANG, Huanlai XING, Li FENG

“…Edge computing has gained widespread usage in intelligent applications due to its benefits, including low latency, high bandwidth, and cost-effectiveness.However, it also faces many security challenges due to its distributed, real-time, multi-source and heterogeneous data characteristics.Identity authentication serves as the initial step for terminal to access to the network and acts as the first line of defense for edge computing.To address the security issues in the edge computing environment, a terminal cross-domain authentication protocol suitable for the edge computing environment was proposed based on the ＆quot;cloud-edge-end＆quot; three-level network authentication architecture.Access authentication was implemented between terminals and local edge nodes based on the SM9 algorithm, and session keys were negotiated.The secret key was combined with symmetric encryption technology and hash function to achieve cross-domain authentication for the terminal.The pseudonym mechanism was used in the authentication process to protect the privacy of end users.The terminal only needs to register once, and it can roam randomly between different security domains.BAN logic was used to prove the correctness of the protocol and analyze its security.The results show that this protocol is capable of resisting common attacks in IoT scenarios, and it features characteristics such as single sign-on and user anonymity.The performance of the cross-domain authentication protocol was evaluated based on computational and communication costs, and compared with existing schemes.The experimental results show that this protocol outperforms other schemes in terms of computational and communication costs, making it suitable for resource-constrained terminal devices.Overall, the proposed protocol offers lightweight and secure identity authentication within edge computing environments.…”
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Energy aware resource management in 6G IoT networks using STAR RIS by Ali Alqahtani, Neeti Taneja, Ashu Taneja, Nayef Alqahtani

“…The system spectral efficiency and energy efficiency is evaluated for different RIS elements N and transmit power levels $$P_{max}$$ . …”
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CAPSE-ViT: A Lightweight Framework for Underwater Acoustic Vessel Classification Using Coherent Spectral Estimation and Modified Vision Transformer by Najamuddin NAJAMUDDIN, Usman Ullah SHEIKH, Ahmad Zuri SHA’AMERI

“…The major challenge is inserting area-specific understanding into ML frameworks to extract features that effectively distinguish between different vessel types. In this study, we propose a model that uses the coherently averaged power spectral estimation (CAPSE) algorithm. …”
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Exploring the genetic profiles linked to senescence in thyroid tumors: insights on predicting disease progression and immune responses by Baoliang Zhang, Yanping Pang

“…We constructed a prognostic model using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and verified its efficacy. Kaplan-Meier survival curves were plotted, and Receiver Operating Characteristic (ROC) curves rigorously confirmed the accuracy of model predictions.ResultsTo evaluate the predictive power of prognostic models across different phenotypic traits, we performed survival analysis, Gene Set Enrichment Analysis (GSEA), and immune-related differential analysis. …”
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Transcriptome signature for multiple biotic and abiotic stress in barley (Hordeum vulgare L.) identifies using machine learning approach by Bahman Panahi

“…The C4.5 algorithm demonstrated superior accuracy in predicting stress-responsive signatures. …”
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Mapping Soil Available Nitrogen Using Crop-Specific Growth Information and Remote Sensing by Xinle Zhang, Yihan Ma, Shinai Ma, Chuan Qin, Yiang Wang, Huanjun Liu, Lu Chen, Xiaomeng Zhu

“…These remote sensing variables were combined with soil sample data, crop type information, and crop growth period data as predictive factors and input into a Random Forest (RF) model optimized using the Optuna hyperparameter tuning algorithm. The accuracy of different strategies was evaluated using 5-fold cross-validation. …”
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BIMSSA: enhancing cancer prediction with salp swarm optimization and ensemble machine learning approaches by Pinakshi Panda, Sukant Kishoro Bisoy, Amrutanshu Panigrahi, Abhilash Pati, Bibhuprasad Sahu, Zheshan Guo, Haipeng Liu, Prince Jain

“…Then, majority voting was used to build an ensemble of the top three algorithms. The ensemble ML-based model BIMSSA was evaluated using microarray data from four different cancer types: Adult acute lymphoblastic leukemia and Acute myelogenous leukemia (ALL-AML), Lymphoma, Mixed-lineage leukemia (MLL), and Small round blue cell tumors (SRBCT).ResultsIn terms of accuracy, the proposed BIMSSA (Boruta + IMRMR + SSA) achieved 96.7% for ALL-AML, 96.2% for Lymphoma, 95.1% for MLL, and 97.1% for the SRBCT cancer datasets, according to the empirical evaluations.ConclusionThe results show that the proposed approach can accurately predict different forms of cancer, which is useful for both physicians and researchers.…”
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Needle detection and localisation for robot‐assisted subretinal injection using deep learning by Mingchuan Zhou, Xiangyu Guo, Matthias Grimm, Elias Lochner, Zhongliang Jiang, Abouzar Eslami, Juan Ye, Nassir Navab, Alois Knoll, Mohammad Ali Nasseri

“…The algorithm was evaluated by comparing the depth of the top and bottom edge of the predicted bounding box to the ground truth. …”
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Building radiomics models based on ACR TI-RADS combining clinical features for discriminating benign and malignant thyroid nodules by Xingxing Chen, Xingxing Chen, Lili Zhang, Bin Chen, Jiajia Lu

“…A total of 107 radiomics features were extracted from the US images, and the radiomics score (Rad-score) was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Different models were created using logistic regression, including the clinic-ACR score (Clin+ACR), clinic-Rad score (Clin+Rad), ACR score-Rad score (ACR+Rad), and combined clinic-ACR score-Rad score (Clin+ACR+Rad). …”
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Immunotyping of thyroid cancer for clinical outcomes and implications by Jin Xu, Zhen Luo, Dayong Xu, Mujing Ke, Cheng Tan

“…Methods The Cancer Genome Atlas (TCGA)-THCA dataset was downloaded. The CIBERSORT algorithm was used to determine immune cell infiltration and a Weighted Gene Co-expression Network Analysis (WGCNA) was executed to obtain immune cell-related genes. …”
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Experimenting with learning-based image orientation approaches for photogrammetric mapping of <em>Posidonia oceanica</em> meadows by F. Menna, A. Calantropio, A. Pansini, G. Ceccherelli, E. Nocerino

“…A comparative analysis of traditional algorithms and AI-driven approaches for image orientation is presented on datasets that differ by acquisition protocols, depth, season, platform type, and imaging system. …”
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Deepmol: an automated machine and deep learning framework for computational chemistry by João Correia, João Capela, Miguel Rocha

“…Despite its potential to revolutionize the field, researchers are often encumbered by obstacles, such as the complexity of selecting optimal algorithms, the automation of data pre-processing steps, the necessity for adaptive feature engineering, and the assurance of model performance consistency across different datasets. …”
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2D Animal Skeletons Keypoint Detection: Research Progress and Future Trends by Pengfei Ma, Ronghua Gao, Qifeng Li, Qinyang Yu, Rong Wang, Chengrong Lai, Weiwei Huang, Peng Hao, Zhaoyang Wang, Xuwen Li

“…The paper not only summarizes different model algorithms, datasets, and evaluation metrics related to animal keypoint detection but also integrates various application scenarios, highlighting distinct features under different focal points. …”
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Analysis of Electron Distribution Functions From the Magnetospheric Multiscale (MMS) Mission Using the Gaussian Mixture Model by Beniamino Sanò, Nathan N. Maes Anno, David L. Newman, Marty Goldman, Francesco Valentini, Denise Perrone, Giovanni Lapenta

“…By varying the inter‐beam average difference and the beam standard deviation we evaluate the ability of the GMM in recognizing correctly the beam. …”
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Neuropathic pain: proposal of a mechanism-based treatment by Laura Demartini, Cesare Bonezzi

“…Some authors have suggested that the poor results in the treatment of neuropathic pain may be related to the different mechanisms present in each patient and have tried to correlate them with clinical characteristics in order to evaluate possible targeted treatments. …”
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IMPLEMENTATION OF A METHOD FOR SYNTHESIZING GROUPS OF SYMMETRIC DOUBLE-OPERAND OPERATIONS OF CRYPTOGRAPHIC INFORMATION CODING FOR BLOCK ENCRYPTION SYSTEMS by Nataliia Lada, Yuliia Rudnytska

“…Synthesis of symmetric cryptographic coding operations belonging to different mathematical groups provides increase of algorithm's crypto stability. …”
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Dynamic Adaptive Parametric Social Network Analysis Using Reinforcement Learning: A Case Study in Topic-Aware Influence Maximization by Mohammad Hossein Ahmadikia, Mehdy Roayaei

“…Current network analysis algorithms often rely on search methods or centrality measures but face challenges such as 1) The solution space is large, resulting in high computational complexity. 2) Algorithms may be instance-dependent, relying considerably on network structure and characteristics, which may result in varying performance across different networks. 3) Most existing centrality measures are inherently static, which fail to capture the dynamic nature inherent in network analysis problems. …”
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