An Algorithm for Smoker Detection by Hiba Youni, Saleem Omar, Mafaz Alwahhab, Enas Mqdad

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Model Reduction and Chattering Mitigation in Multi-Model Predictive Control for Quadrotor UAVs by Ghulam E. Mustafa Abro, Sufyan Ali Memon, Saddaf Rubab, Kaznah Alshammari, Faheem Khan

“…In order to resolve this discrepancy, we propose a Multi-Model Predictive Control (MMPC) approach that integrates Principal Component Analysis (PCA) for model reduction and employs linear quadrotor attitude models. …”
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Parallel Attribute Reduction Algorithm for Complex Heterogeneous Data Using MapReduce by Tengfei Zhang, Fumin Ma, Jie Cao, Chen Peng, Dong Yue

“…Thereafter, a quick parallel attribute reduction algorithm using MapReduce was developed. …”
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Regeneration Filter: Enhancing Mosaic Algorithm for Near Salt & Pepper Noise Reduction by Ratko M. Ivković, Ivana M. Milošević, Zoran N. Milivojević

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Unsustainable artificial intelligence and algorithmically facilitated emissions: The case for emissions-reduction-by-design by Jutta Haider, Malte Rödl, James White

“…It introduces the notion of algorithmically facilitated emissions to initiate a shift from a logic of ‘climate collapse by design’ to a logic of ‘emissions reduction by design’. …”
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Improved HLLL Lattice Basis Reduction Algorithm to Solve GNSS Integer Ambiguity by Kezhao Li, Chendong Tian, Yingxiang Jiao, Zhe Yue

“…Compared with the LLL reduction algorithm and HLLL reduction algorithm, the experimental results show that the PHLLL algorithm has higher reduction efficiency and effectiveness. …”
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Optimization Algorithm of Workflow’s Accuracy Based  on Serial Reduction under Constraint Time by LUO Zhi-yong, ZHU Zi-hao, YOU Bo, MIAO Shi-di

“…Finally，in the typical case，the traditional one-way target algorithm and the string reduction algorithm are used to solve the corresponding path respectively， and analyzed the other parameters that affect the performance of SＲA. …”
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Neighborhood conditional mutual information entropy attribute reduction algorithm for hybrid data by Haibo LAN

“…Attribute reduction is an important research content of the rough set theory.Its main purpose is to eliminate irrelevant attributes in information systems, reduce data dimensions and improve data knowledge discovery performance.However, most of the attribute reduction methods based on a rough set do not consider the dependence between attributes, which makes the final attribute reduction result have some redundant attributes.An attribute reduction algorithm based on neighborhood conditional mutual information entropy was proposed.Firstly, based on the traditional neighborhood entropy, a hybrid neighborhood mutual information entropy model and a hybrid neighborhood conditional mutual information entropy model were proposed for hybrid data.Then, the two entropy models were used to evaluate the attribute dependence and attribute heuristic search of the hybrid information system, and an attribute reduction algorithm was designed.Finally, through the experimental analysis of UCI data sets, it was proved that the algorithm had higher attribute reduction performance.…”
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Improved Crosstalk Reduction on Multiview 3D Display by Using BILS Algorithm by Xiaoyan Wang, Chunping Hou

“…In this paper, we present a system-introduced crosstalk measurement method and derive an improved crosstalk reduction method. The proposed measurement method is applied to measure the exact crosstalk among subpixels corresponding to different view images and the obtained results are very effective for crosstalk reduction method. …”
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Incremental attribute reduction algorithm for dominance-based neighborhood relative decision entropy by CHEN Baoguo, CHEN Lei, DENG Ming, LI Xiaoyan, CHEN Jinlin

“…Verify the effectiveness of the incremental algorithm through these experimental results.ConclusionsThe experimental results show that the proposed incremental algorithm has better attribute reduction performance on dynamic datasets, significantly improving the efficiency of dynamic attribute reduction while ensuring the number of attribute selections and classification accuracy. …”
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Blind signal separation algorithm based on temporal predictability and differential search algorithm by Lei CHEN, Li-yi ZHANG, Yan-ju GUO, Yong HUANG, Jing-yi LIANG

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Active noise reduction system by Wojciech CIESIELKA

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Energy Optimization in Ultrasound Tomography Through Sensor Reduction Supported by Machine Learning Algorithms by Bartłomiej Baran, Tomasz Rymarczyk, Dariusz Majerek, Piotr Szyszka, Dariusz Wójcik, Tomasz Cieplak, Marcin Gąsior, Marcin Marczuk, Edmund Wąsik, Konrad Gauda

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Real power loss reduction by Rock Dove optimization and Fuligo Septica optimization algorithms by Kanagasabai L.

“…This paper aims to use the Rock Dove (RD) optimization algorithm and the Fuligo Septica optimization (FSO) algorithm for power loss reduction. …”
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Prediction of Hypertension Patients with Machine Learning Algorithm by Eko Priyono

“…These algorithms can predict hypertension risk based on clinical data, such as age, medical history, and lifestyle. …”
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Location prediction algorithm based on movement tendency by Wen LI, Shi-xiong XIA, Feng LIU, Lei ZHANG, Guan YUAN

“…A location prediction algorithm based on movement tendency (LP-MT) was proposed, which not only buils moving object's historical activity model borrowing from Markov thinking, but also added movement tendency as an important reference of location prediction. …”
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Neural network-based link prediction algorithm by Yonghao PAN, Hongtao YU, Shuxin LIU

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Spatial Distribution of Poverty Clusters and Its Prediction Algorithms: A Visual Analytics Approach to Understanding the Disparities of Poverty Across Zones by Ngong'ho Bujiku Sende, Snehanshu Saha, Leon Fidele Ruganzu Uwimbabazi

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A hybrid machine learning algorithm approach to predictive maintenance tasks: A comparison with machine learning algorithms by Jorge Paredes, Danilo Chávez, Ramiro Isa-Jara, Diego Vargas

“…This data can provide valuable insights into the behavior of a specific machine, enabling optimization or the prediction of potential malfunctions. Supervised machine learning algorithms are capable of predicting the remaining useful life (RUL) of a machine. …”
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Applications of Machine Learning Algorithms in Geriatrics by Adrian Stancu, Cosmina-Mihaela Rosca, Emilian Marian Iovanovici

“…The integration of machine learning (ML)-type algorithms in geriatrics represents a direction for optimizing prevention, diagnosis, prediction, monitoring, and treatment. …”
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