Mechanism design for crowd sensing with data reuse based on two-sided auction by Changkun JIANG, Lin GAO

“…Crowd sensing is a promising sensing paradigm,which mainly uses a variety of embedded sensors in a large number of mobile devices to accomplish data sensing tasks.One of the key issues in crowd sensing is how to effectively coordinate mobile device users to perform multiple sensory tasks simultaneously.By introducing a new data layer between the sensing task and the user,the similarity of the sensing task and the heterogeneity of the user were effectively utilized,and the joint task selection and user scheduling problems were established on the data layer,aiming at maximizing the social welfare of the whole system.This problem was difficult to solve due to its combinatorial nature and the presence of private information on both the sensing tasks and the users.In order to deal with these problems,a two-sided randomized auction mechanism was proposed,and it was proved that it can satisfy the desirable properties of the computational efficiency,the individual rationality,and the incentive compatibility in expectation.The simulation results show that the proposed stochastic auction mechanism can achieve nearly optimal social welfare,and the social welfare benefits brought by data reuse will increase significantly with the enhancement of task similarity.…”
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Cerebral Venous Thrombosis Risk Factors and Their Relationship with Optic Coherence Tomography: A CaseControl Study by Hossein Ali Ebrahimi meimand, shakiba ahmadi, Farhad Iranmanesh, Mahdiyeh Khazanehha

“…Healthy participants, randomly selected from hospital staff without any known health issues, were examined by an ophthalmologist. …”
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A Robust Cross-Channel Image Watermarking Technique for Tamper Detection and its Precise Localization by Muhammad Ashraf, Adnan Nadeem, Oussama Benrhouma, Muhammad Sarim, Kashif Rizwan, Amir Mehmood

“…Chaotic systems are employed to leverage their sensitivity to initial conditions and control parameters, resulting in high confusion and diffusion properties in the proposed scheme. The protection layer is completely intractable as it is randomly scattered in the entire RGB space, making it very hard to remove without leaving a clear footprint in affected images. …”
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A Three-Dimensional OFDM System with PAPR Reduction Method for Wireless Sensor Networks by Zhenxing Chen, Seog Geun Kang

“…Hence, the proposed algorithm makes the 3D OFDM system be a possible candidate for a physical layer transmission scheme in future WSNs.…”
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Semantic Reasoning Using Standard Attention-Based Models: An Application to Chronic Disease Literature by Yalbi Itzel Balderas-Martínez, José Armando Sánchez-Rojas, Arturo Téllez-Velázquez, Flavio Juárez Martínez, Raúl Cruz-Barbosa, Enrique Guzmán-Ramírez, Iván García-Pacheco, Ignacio Arroyo-Fernández

“…In addition, our MSPT provided meaningful semantic insights: for the GRUs (256-dim, 2048-unit, 1-layer): mean similarity <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>(</mo><msub><mi>μ</mi><mrow><mi>s</mi><mi>t</mi><mi>s</mi></mrow></msub><mo>)</mo></mrow></semantics></math></inline-formula> of 0.641 to the ground truth vs. 0.542 to the random baseline (gap 12.1%; <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>p</mi><mo><</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>180</mn></mrow></msup></mrow></semantics></math></inline-formula>). …”
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Dual-platform integration of HPTLC and firefly algorithm-optimized chemometrics with hammersley sequence sampling for simultaneous quantification of bisoprolol, amlodipine, and mut... by Lateefa A. Al-Khateeb, Ahmed Emad F. Abbas, Mohamed R. Elghobashy, Nisreen F. Abo Talib, Ibrahim A. Naguib, Mohammed Alqarni, Michael K. Halim

“…Two complementary methodologies were developed: high-performance thin-layer chromatography (HPTLC)-densitometry and Firefly Algorithm-optimized partial least squares (FA-PLS) spectrophotometry, both aligned with green analytical chemistry (GAC) and white analytical chemistry (WAC) principles. …”
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The Development of the Open Machine-Learning-Based Anti-Spam (Open-MaLBAS) by Isaac C. Ferreira, Marcelo V. C. Aragao, Edvard M. Oliveira, Bruno T. Kuehne, Edmilson M. Moreira, Otavio A. S. Carpinteiro

“…From the experiments, it was observed that Open-MaLBAS was able to correctly classify 81.48&#x0025; and 98.13&#x0025; of the e-mails in the database, using, respectively, the two models &#x2014; Multi-Layer Perceptron and Random Forest &#x2014; evaluated. …”
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Volumetric evolution of supraglacial lakes in southwestern Greenland using ICESat-2 and Sentinel-2 by T. Feng, T. Feng, X. Ma, X. Ma, X. Liu, X. Liu, X. Liu

“…First, the area of SGLs is extracted using a random forest (RF) model based on spectral features from Sentinel-2 imagery, achieving an intersection over union (IoU) of 90.20 % compared to manually delineated lake extents. …”
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Identifying cardiovascular disease risk in the U.S. population using environmental volatile organic compounds exposure: A machine learning predictive model based on the SHAP method... by Qingan Fu, Yanze Wu, Min Zhu, Yunlei Xia, Qingyun Yu, Zhekang Liu, Xiaowei Ma, Renqiang Yang

“…Six ML models were developed, including Random Forest (RF), Light Gradient Boosting Machine (LightGBM), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), and Support Vector Machines (SVM). …”
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Petrological controls on the engineering properties of carbonate aggregates through a machine learning approach by Javid Hussain, Tehseen Zafar, Xiaodong Fu, Nafees Ali, Jian Chen, Fabrizio Frontalini, Jabir Hussain, Xiao Lina, George Kontakiotis, Olga Koumoutsakou

“…To enhance predictive accuracy, advanced machine learning models, including Random Forest, Gradient Boosting, Multi-Layer Perceptron, and Categorical Boosting, were applied. …”
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Monitoring soil salinization in Arid cotton fields using Unmanned Aerial Vehicle hyperspectral imagery by Jinming Zhang, Jianli Ding, Jiao Tan, Jinjie Wang, Zihan Zhang, Zeyuan Wang, Xiangyu Ge

“…Fractional-order differentiation (FOD) technology was used for spectral preprocessing, combined with the random frog algorithm (RF), uninformative variable elimination (UVE), and bootstrap soft shrinkage (BOSS) selection algorithms to optimize one-dimensional spectral bands. …”
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Comparative Performance of Autoencoders and Traditional Machine Learning Algorithms in Clinical Data Analysis for Predicting Post-Staged GKRS Tumor Dynamics by Simona Ruxandra Volovăț, Tudor Ovidiu Popa, Dragoș Rusu, Lăcrămioara Ochiuz, Decebal Vasincu, Maricel Agop, Călin Gheorghe Buzea, Cristian Constantin Volovăț

“…Traditional ML models, such as Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Extra Trees, Random Forest, and XGBoost, were trained and evaluated. …”
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A Retrospective Machine Learning Analysis to Predict 3-Month Nonunion of Unstable Distal Clavicle Fracture Patients Treated with Open Reduction and Internal Fixation by Ma C, Lu W, Liang L, Huang K, Zou J

“…Five ML models (logistic regression, random forest classifier, extreme gradient boosting, multi-layer perceptron, and category boosting) were developed. …”
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Risk factors and prediction of distant metastasis (DM) of colon adenocarcinoma: a logistic regression and machine learning study based on surveillance, epidemiology, and end result... by Qiang Guo, Junyun Li, Zhe Wei, Jingjing Xu, Shaojun Duan, Jianfeng Li, Yaxi Liu

“…Logistic regression was utilized to find independent risk factors (IRFs) of DM and 12 models including BNB (Bernoulli naïve bayes), DT (Decision tree), GBC (Gradient Boosting Classifier), GNB (Gaussian naïve bayes), KNN (K-nearest neighbor), LDA (Linear Discriminant Analysis), LR (Logistic regression), MLP (Multi-layer perceptron classifier), MNB (Multinomial naïve bayes), QDA (Quadratic discriminant analysis), RFC (Random forest classifier) and SVC (Support vector machine) were established and evaluated on the training set and test set (7:3) of the retrieved patients. …”
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Diagnostic potential of salivary microbiota in persistent pulmonary nodules: identifying biomarkers and functional pathways using 16S rRNA sequencing and machine learning by Xiao Zeng, Qiong Ma, Chun-Xia Huang, Jun-Jie Xiao, Xi Fu, Yi-Feng Ren, Yu-Li Qu, Hong-Xia Xiang, Mao Lei, Ru-Yi Zheng, Yang Zhong, Ping Xiao, Xiang Zhuang, Feng-Ming You, Jia-Wei He

“…Seven advanced machine learning algorithms (logistic regression, support vector machine, multi-layer perceptron, naïve Bayes, random forest, gradient boosting decision tree, and LightGBM) were utilized to evaluate performance and identify key microorganisms, with fivefold cross-validation employed to ensure robustness. …”
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Cerebrospinal Fluid Leakage Combined with Blood Biomarkers Predicts Poor Wound Healing After Posterior Lumbar Spinal Fusion: A Machine Learning Analysis by Pang Z, Ou Y, Liang J, Huang S, Chen J, Huang S, Wei Q, Liu Y, Qin H, Chen Y

“…In the test group, logistic regression analysis, support vector machine (SVM), random forest (RF), decision tree (DT), XGboost, Naïve Bayes (NB), k-Nearest Neighbor (KNN), and Multi-Layer Perceptron (MLP) were used to identify specific variables. …”
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Assessment of war-induced agricultural land use changes in Ukraine using machine learning applied to Sentinel satellite data by Nataliia Kussul, Andrii Shelestov, Bohdan Yailymov, Hanna Yailymova, Guido Lemoine, Klaus Deininger

“…The study integrates Random Forest and Multi-Layer Perceptron classification techniques to improve accuracy, addressing spectral ambiguities and classification noise. …”
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Remote Sensing Based Crop Monitoring Techniques: A Case Study for the Navajo Nation by V. Thangavel, S. Nagarajan, M. Arockiasamy, Md. T. Khan, G. Sklivanitis

“…The training datasets were obtained from the USDA&rsquo;s Crop Data Layer (CDL) and split into 80% for training and 20% for validating the Random Forest supervised classification algorithm. …”
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Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism by Wangyong Guo, Shijin Liu, Liguo Weng, Xingyu Liang

“…Optimizing short-term load forecasting performance is a challenge due to the non-linearity and randomness of electrical load, as well as the variability of system operating patterns. …”
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BA-ELM Gear Fault Diagnosis Method based on Energy Feature of Wavelet Packet Optimal Node by Qin Bo, Liu Yongliang, Wang Jianguo, Qin Yan, Yang Yunzhong

“…In order to solve the problems that gear fault classification model has weak generalization ability,poor accuracy causing by the fault features of gear is difficult to extract and extreme learning machine input weights and threshold of hidden layer nodes randomly selected,a BA- ELM gear fault diagnosis method is puts forward based on energy feature of wavelet packet optimal nodes.First,the gear vibration signals are decomposed by using wavelet packet in this method,the optimal nodes is selected by using the correlation coefficient between each node decomposition signals and original signal,and the energy feature is calculated.Second,the bat algorithm is used to optimize the extreme learning machine input weights and threshold of hidden layer node and the gear fault classification model of BA-ELM is established.Finally,the energy entropy feature vectors of the optimal wavelet packet nodes as the model input is used to identify the different fault states of gear.The experimental results show that,comparing with SVM and ELM fault classification method,the BA-ELM gear fault diagnosis method based on energy feature of wavelet packet optimal nodes has higher classification accuracy and better generalization ability.…”
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