Temporal dependent rate-distortion optimization based on distortion backward propagation by Hongwei GUO, Ce ZHU, Xu YANG, Lei LUO

“…Rate-distortion optimization (RDO) is a crucial technique in block based hybrid video encoders.However, the widely used independent RDO is far from obtaining optimal coding performance.To improve the rate-distortion (R-D) performance of high efficiency video coding (HEVC), a temporal dependent RDO algorithm was proposed.Firstly, the formula to calculate temporal distortion propagation factor was derived by using an exponential R-D function.Then, the coding distortion and motion compensation predicted error were obtained by pre-encoding, and the temporal distortion propagation factor was estimated by using distortion backward propagation.Finally, the Lagrange multiplier and quantization parameter of coding tree unit were adaptively adjusted to optimize bit resources allocation.Experimental results show that compared with the original RDO method in HEVC under the low-delay configuration, the proposed algorithm achieves an average 4.4% bit rate reduction for all test sequences, and up to 13.0% bit rate reduction for test sequence BasketballDrill, at the same reconstructed video quality.…”
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Temporal dependent rate-distortion optimization based on distortion backward propagation by Hongwei GUO, Ce ZHU, Xu YANG, Lei LUO

“…Rate-distortion optimization (RDO) is a crucial technique in block based hybrid video encoders.However, the widely used independent RDO is far from obtaining optimal coding performance.To improve the rate-distortion (R-D) performance of high efficiency video coding (HEVC), a temporal dependent RDO algorithm was proposed.Firstly, the formula to calculate temporal distortion propagation factor was derived by using an exponential R-D function.Then, the coding distortion and motion compensation predicted error were obtained by pre-encoding, and the temporal distortion propagation factor was estimated by using distortion backward propagation.Finally, the Lagrange multiplier and quantization parameter of coding tree unit were adaptively adjusted to optimize bit resources allocation.Experimental results show that compared with the original RDO method in HEVC under the low-delay configuration, the proposed algorithm achieves an average 4.4% bit rate reduction for all test sequences, and up to 13.0% bit rate reduction for test sequence BasketballDrill, at the same reconstructed video quality.…”
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Exploration of the clinicopathological and prognostic significance of BRCA1 in gastric cancer by Hongrong Zhang, Qi Xu, Hongxing Kan, Yinfeng Yang, Yunquan Cai

“…To explore potential biomarkers for GC, GC patient transcriptome data were subjected to a comprehensive approach involving machine learning, binary nomogram prediction model construction, the topological algorithm of CytoHubba, and Kaplan–Meier and Mendelian randomization (MR) analyses. …”
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Quantitative Detection of Quartz Sandstone SiO2 Grade Using Polarized Infrared Absorption Spectroscopy with Convolutional Neural Network Model by Banglong Pan, Hongwei Cheng, Shuhua Du, Hanming Yu, Shaoru Feng, Yi Tang, Juan Du, Huaming Xie

“…Then, generalized regression neural network (GRNN), partial least squares regression (PLSR), and convolutional neural network (CNN) were employed to establish a hyperspectral prediction model of SiO2 grade. The results show that the quantitative model by the PCA-CNN algorithm has the better prediction precision for the reciprocal logarithm data, with a coefficient of determination (R2), root mean square error (RMSE), and ratio of performance to interquartile range (RPIQ) of 0.907, 0.023, and 5.11, respectively. …”
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Empirical Reduced-Order Modeling for Boundary Feedback Flow Control by Seddik M. Djouadi, R. Chris Camphouse, James H. Myatt

“…This paper deals with the practical and theoretical implications of model reduction for aerodynamic flow-based control problems. …”
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Intelligence model-driven multi-stress adaptive reliability enhancement testing technology by Shouqing Huang, Beichen He, Jing Wang, Xiaoyang Li, Rui Kang, Fangyong Li

“…In terms of mathematical models, we propose a Tuna Swarm Optimization–Gaussian Process Regression (TSO-GPR) model, which combines the global search capability of the tuna swarm optimization algorithm and the accurate prediction capability of Gaussian process regression, effectively handling the complex nonlinear relationships between multiple stresses and failure characteristic. …”
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GenAI-Based Jamming and Spoofing Attacks on UAVs by Burcu Sonmez Sarikaya, Serif Bahtiyar

“…Creating effective intrusion detection systems against such attacks has been a significant challenge since there is a lack of sufficient attack data that can be used to design an intrusion detection system with advanced computing algorithms. In this research, we propose a novel framework to create attacks data for UAVs by using generative artificial intelligence algorithms. …”
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Elemental numerical descriptions to enhance classification and regression model performance for high-entropy alloys by Yan Zhang, Cheng Wen, Pengfei Dang, Xue Jiang, Dezhen Xue, Yanjing Su

“…Moreover, these new numerical descriptions for phase classification can be directly applied to regression model predictions of HEAs, reducing the error by 22% and improving the R 2 value from 0.79 to 0.88 in hardness prediction. …”
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No-Reference Stereoscopic IQA Approach: From Nonlinear Effect to Parallax Compensation by Ke Gu, Guangtao Zhai, Xiaokang Yang, Wenjun Zhang

“…Third, the saliency based parallax compensation, resulted from different stereoscopic image contents, is considerably valid to improve the prediction performance of image quality metrics. Experimental results confirm that our proposed stereoscopic image quality assessment paradigm has superior prediction accuracy as compared to state-of-the-art competitors.…”
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Multiobjective Optimization of Milling Parameters for Ultrahigh-Strength Steel AF1410 Based on the NSGA-II Method by Jin Xu, Fuwu Yan, Yan Li, Zhenchao Yang, Long Li

“…The influence of milling parameters (milling speed, each tooth feed, radial depth of cut, and axial depth of cut) on milling force and surface roughness is studied by ANOVA and established prediction model. Multiobjective optimization of milling parameters is accomplished based on nondominated sorting genetic algorithm II (NSGA-II) with milling force, surface roughness, and material removal rate as optimization objectives. …”
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Artificial intelligence (AI) in restorative dentistry: current trends and future prospects by Mariya Najeeb, Shahid Islam

“…Key challenges include data privacy concerns, algorithmic bias, interpretability of AI decision-making processes, and the need for standardized AI training programs in dental education. …”
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FPGA-Accelerated Sparse Subset Segmentation Using ADMM for High-Resolution Imagery by Rupali Karthikeyan, Deep Amit Lodaya, Rama Muni Reddy Yanamala, Rayappa David Amar Raj, K. Krishna Prakasha, T. Subeesh, V. Anandkumar, Archana Pallakonda

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Disinformation in the Digital Age: Climate Change, Media Dynamics, and Strategies for Resilience by Andrea Tomassi, Andrea Falegnami, Elpidio Romano

“…Our findings indicate that social media algorithms and user dynamics can amplify false scientific claims, as seen in case studies of viral misinformation campaigns on vaccines and climate change. …”
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Dual smart sensor data-based deep learning network for premature infant hypoglycemia detection by Muhammad Shafiq, J. Kavitha, Dhruva R. Rinku, N. K. Senthil Kumar, Kamal Poon, Amar Y. Jaffar, V. Saravanan

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Dysfluent Speech Classification Using Variational Mode Decomposition and Complete Ensemble Empirical Mode Decomposition Techniques With NGCU-Based RNN by N. A. Vinay, K. N. Vidyasagar, S. Rohith, S. Supreeth, S. N. Prasad, S. Pramod Kumar, S. H. Bharathi

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Roman urdu hate speech detection using hybrid machine learning models and hyperparameter optimization by Waqar Ashiq, Samra Kanwal, Adnan Rafique, Muhammad Waqas, Tahir Khurshaid, Elizabeth Caro Montero, Alicia Bustamante Alonso, Imran Ashraf

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OliveTreeCrownsDb: A high-resolution UAV dataset for detection and segmentation in agricultural computer visionMendeley Data by Youness Hnida, Mohamed Adnane Mahraz, Ali Achebour, Ali Yahyaouy, Jamal Riffi, Hamid Tairi

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Solar Agro Savior: Smart Agricultural Monitoring Using Drones and Deep Learning Techniques by Manu Mundappat Ramachandran, Bisni Fahad Mon, Mohammad Hayajneh, Najah Abu Ali, Elarbi Badidi

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An integration of ensemble deep learning with hybrid optimization approaches for effective underwater object detection and classification model by G. Abirami, S. Nagadevi, J. D. Dorathi Jayaseeli, T. Prabhakara Rao, R S M Lakshmi Patibandla, Rajanikanth Aluvalu, K Srihari

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Scoring model for exploring factors influencing mortality in dengue patients at a tertiary care hospital: a retrospective study by Abhishek S. Rao, B. H. Karthik Pai, K. Adithi, Lakshmi Belur Keshav, Karan Malhotra, Sneha Nayak, H. K. Sachidananda

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