Reversible data hiding algorithm in encrypted images based on prediction error and bitplane coding by Haiyong WANG, Mengning JI

“…With the increasing use of cloud backup methods for storing important files, the demand for privacy protection has also grown.Reversible data hiding in encrypted images (RDHEI) is an important technology in the field of information security that allows embedding secret information in encrypted images while ensuring error-free extraction of the secret information and lossless recovery of the original plaintext image.This technology not only enhances image security but also enables efficient transmission of sensitive information over networks.Its application in cloud environments for user privacy protection has attracted significant attention from researchers.A reversible data hiding method in encrypted images based on prediction error and bitplane coding was proposed to improve the embedding rate of existing RDHEI algorithms.Different encoding methods were employed by the algorithm depending on the distribution of the bitplanes, resulting in the creation of additional space in the image.The image was rearranged to allocate the freed-up space to the lower-order planes.Following this, a random matrix was generated using a key to encrypt the image, ensuring image security.Finally, the information was embedded into the reserved space.The information can be extracted and the image recovered by the receiver using different keys.The proposed algorithm achieves a higher embedding rate compared to five state-of-the-art RDHEI algorithms.The average embedding rates on BOWS-2, BOSSBase, and UCID datasets are 3.769 bit/pixel, 3.874 bit/pixel, and 3.148 bit/pixel respectively, which represent an improvement of 12.5%, 6.9% and 8.6% compared to the best-performing algorithms in the same category.Experimental results demonstrate that the proposed algorithm effectively utilizes the redundancy of images and significantly improves the embedding rate.…”
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Refinement of an Algorithm to Detect and Predict Freezing of Gait in Parkinson Disease Using Wearable Sensors by Allison M. Haussler, Lauren E. Tueth, David S. May, Gammon M. Earhart, Pietro Mazzoni

“…The purpose of this paper is to explore how the existing pFOG algorithm can be refined to improve the detection and prediction of FOG. …”
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SOC and SOH Prediction of Lithium‐Ion Batteries Based on LSTM–AUKF Joint Algorithm by Yancheng Song, Jiaqi Lu, Huai Zhang, Guangjun Liu

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Prediction of Sonic Log Values Using a Gradient Boosting Algorithm in the 'AB' Field by Rasif Nahari, Utama Widya, Ardhya Garini Sherly, Fitri Indriani Rista, Pratama Novian Putra Dhea

“…In this study, Python was used to develop predictions for missing data, demonstrating the capability of machine learning to enhance data reliability and improve petroleum exploration processes. …”
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Residential Building Energy Usage Prediction Using Bayesian-Based Optimized XGBoost Algorithm by Nabaa Riyadh Baqer, Parviz Rashidi-Khazaee

“…Therefore, engineers and designers try to find a reliable tool to help them analyze and predict the energy consumption of buildings in the early design stages before construction. …”
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Adaptive random early detection algorithm based on network traffic level grade prediction by Debin WEI, Chengsheng PAN, Li YANG, Zuoren YAN

“…In view of the problem that the calculation of average queue length and maximum packet drop probability in random early detection algorithm and its variants reflect the changes of network traffic slowly, an adaptive random early detection algorithm based on network traffic level grade prediction was proposed.Based on the statistical characteristics of self-similar network traffic, the transition probability table of network traffic level grade was established, and a grade prediction method of self-similar network traffic level with low complexity and high accuracy was proposed.Furthermore, the prediction results were applied to calculate the average queue length in equal interval and adjust the maximum packet drop probability.Under the condition of fixed and variable bottleneck link capacity, it is found that regardless of the degree of self-similarity of network traffic, the proposed algorithm can improve the throughput and packet loss rate, especially when the Hurst parameter is large and the traffic is light.…”
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Machine Learning Techniques for Heart Disease Prediction Using a Multi-Algorithm Approach by Muhammad Kunta Biddinika, Alya Masitha, Herman Herman, Vita Arfiana Nurul Fatimah

“…This analysis explores the efficiency of machine learning systems for heart disease identification through a multi-algorithm approach. The main objective is to identify the best performing algorithm for accurate disease prediction, improving clinical decision making. …”
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Using Cuckoo Search Algorithm to Predict Corporate Financial Risks and Alleviate Economic Uncertainty by Muqiao Cai

Subjects: “…Corporate financial risk prediction…”
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Development and validation of prediction algorithm to identify tuberculosis in two large California health systems by Heidi Fischer, Lei Qian, Zhuoxin Li, Katia Bruxvoort, Jacek Skarbinski, Yuching Ni, Jennifer H. Ku, Bruno Lewin, Saadiq Garba, Parag Mahale, Sally F. Shaw, Brigitte Spence, Sara Y. Tartof

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Predicting the Recurrence of Differentiated Thyroid Cancer Using Whale Optimization-Based XGBoost Algorithm by Keshika Shrestha, H. M. Jabed Omur Rifat, Uzzal Biswas, Jun-Jiat Tiang, Abdullah-Al Nahid

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A New Dual-Mode GEP Prediction Algorithm Based on Irregularity and Similar Period by Lei Yang, Zexin Xu, Rui Xu, Jianfan Lu, Zhenlin Xu, Kangshun Li

“…Therefore, a new dual-mode GEP prediction algorithm based on irregularity and similar period is proposed. …”
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An Algorithm for Restoring a Function from Different Functionals for Predicting Rare Events in the Economy by Yu. A. Korablev

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Comparative analysis and application of rockburst prediction model based on secretary bird optimization algorithm by Tengjie Yang, Tengjie Yang, Xinqiang Gao, Xinqiang Gao, Lichuan Wang, Yongqing Xue, Haobo Fan, Zhengguo Zhu, Zhengguo Zhu, Jingbo Zhao, Beiyi Dong, Beiyi Dong

Subjects: “…rockburst prediction…”
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A Location Prediction Algorithm with Daily Routines in Location-Based Participatory Sensing Systems by Ruiyun Yu, Xingyou Xia, Shiyang Liao, Xingwei Wang

“…This paper proposes a social-relationship-based mobile node location prediction algorithm using daily routines (SMLPR). The SMLPR algorithm models application scenarios based on geographic locations and extracts social relationships of mobile nodes from nodes' mobility. …”
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Network queue scheduling algorithm based on self-similar traffic level grading prediction by Debin WEI, Ting SHEN, Li YANG, Yaowen QI

“…Self-similarity characteristic of network traffic will lead to the continuous burstness of data in the network.In order to effectively reduce the queue delay and packet loss rate caused by network traffic burst,improve the transmission capacity of different priority services,and guarantee the service quality requirements,a queue scheduling algorithm P-DWRR based on the self-similarity of network traffic was proposed.A dynamic weight allocation method and a service quantum update method based on the self-similar traffic level grading prediction results were designed,and the service order of the queue according was determined to the service priority and queue waiting time,so as to reduce the queuing delay and packet loss rate.The simulation results show that the P-DWRR algorithm can reduce the queueing delay,delay jitter and packet loss rate on the basis of satisfying the different service priority requirements of the network,and its performance is better than that of DWRR and VDWRR.…”
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Application of the XGBoost Algorithm for Predicting the Target Effective Temperature in Closed Broiler Chicken Cage by Hartono

“…This article examines how the XGBoost algorithm can be used to predict the target effective temperature in closed-house broiler chicken systems. …”
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Research on building energy consumption prediction algorithm based on customized deep learning model by Zheng Liang, Junjie Chen

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Slope stability prediction under seismic loading based on the EO-LightGBM algorithm by Ning Ma, Ning Ma, Yuqi Zhang, Zaizhen Yao

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Sustainable soil organic carbon prediction using machine learning and the ninja optimization algorithm by Anis Ben Ghorbal, Azedine Grine, Marwa M. Eid, Marwa M. Eid, El-Sayed M. El-kenawy, El-Sayed M. El-kenawy

Subjects: “…soil organic carbon prediction…”
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Application of machine learning algorithm incorporating dietary intake in prediction of gestational diabetes mellitus by Tianze Ding, Peijie Liu, Jie Jia, Hui Wu, Jie Zhu, Kefeng Yang

“…Conclusion: XGBoost and LightGBM algorithms outperform logistic regression in predicting GDM among Chinese pregnant women. …”
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