Analysing Threshold Value in Fire Detection Algorithm Using MODIS Data by Bowo E. Cahyono, Peter Fearns, Brendon McAtee

“…This paper describe the principle of fire investigation applied on MODIS data. The main used algorithm in this research is contextual algorithm which has been developed by NASA scientist team. …”
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Multi-Focus Image Fusion Using Energy Valley Optimization Algorithm by Harun Akbulut

“…The obtained experimental results are compared with the well-known Genetic Algorithm (GA), Differential Evolution Algorithm (DE), and Artificial Bee Colony Optimization Algorithm (ABC). …”
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An automated parallel genetic algorithm with parametric adaptation for distributed data analysis by Laila Al-Terkawi, Matteo Migliavacca

“…Abstract Unleashing the potential of large-scale data analysis requires advanced computational methods capable of managing the immense size and complexity of distributed data. Genetic algorithms (GAs), known for their adaptability, benefit significantly from parallelization, prompting ongoing enhancements to boost performance further. …”
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COMPUTATIONAL VISION FOR TOMATO CLASSIFICATION USING A DECISION TREE ALGORITHM by Caroline S. da Fonseca, Bilton G. Nhantumbo, Yuri M. Ferreira, Layana A. da Silva, Anderson G. Costa

“…Principal component analysis was employed to evaluate the influence of colorimetric characteristics within each class, and the decision tree algorithm was applied to classify the fruits into the respective categories. …”
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A Hybrid Optimization Algorithm for the Synthesis of Sparse Array Pattern Diagrams by Youzhi Liu, Linshu Huang, Xu Xie, Huijuan Ye

“…The algorithm enhances global search capability through the introduction of a quantum potential well model, while incorporating adaptive mutation operations to prevent premature convergence, thereby improving optimization accuracy during later iterations. …”
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N-Dimensional Reduction Algorithm for Learning from Demonstration Path Planning by Juliana Manrique-Cordoba, Miguel Ángel de la Casa-Lillo, José María Sabater-Navarro

“…This paper presents an <i>n</i>-dimensional reduction algorithm for Learning from Demonstration (LfD) for robotic path planning, addressing the complexity of high-dimensional data. …”
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Calculating place-based transit accessibility: Methods, tools and algorithmic dependence by Christopher Higgins, Matthew Palm, Amber DeJohn, Luna Xi, James Vaughan, Steven Farber, Michael Widener, Eric Miller

“…With the growing emphasis on accessibility-based planning, analysts should approach the calculation of accessibility with care and recognize the potential for algorithmic dependence in their calculated accessibility results.…”
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From energy to cellular forces in the Cellular Potts Model: An algorithmic approach. by Elisabeth G Rens, Leah Edelstein-Keshet

“…Using the fact that a force field is easily derived from a scalar energy (F = -∇H), we develop a simple algorithm to associate effective forces with cell shapes in the CPM. …”
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Wrapped Up in the Cis-Tem: Trans Liveability in the Age of Algorithmic Violence by Christoffer Koch Andersen

“…Algorithms pervade our reality and promise to universally enhance our lives, but what happens when this promise is reserved for cisgender people while subjecting trans people to legacies of anti-trans violence that implicate trans liveability? …”
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Distributed K-Means algorithm based on a Spark optimization sample. by Yongan Feng, Jiapeng Zou, Wanjun Liu, Fu Lv

“…To address the instability and performance issues of the classical K-Means algorithm when dealing with massive datasets, we propose SOSK-Means, an improved K-Means algorithm based on Spark optimization. …”
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Comparative Analysis of LiDAR Inertial Odometry Algorithms in Blueberry Crops by Ricardo Huaman, Clayder Gonzalez, Sixto Prado

“…In recent years, LiDAR Odometry (LO) and LiDAR Inertial Odometry (LIO) algorithms for robot localization have considerably improved, with significant advancements demonstrated in various benchmarks. …”
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Image forgery detection algorithm based on U-shaped detection network by Zhuzhu WANG

“…Aiming at the defects of traditional image tampering detection algorithm relying on single image attribute,low applicability and current high time-complexity detection algorithm based on deep learning,an U-shaped detection network image forgery detection algorithm was proposed.Firstly,the multi-stage feature information in the image by using the continuous convolution layers and the max-pooling layers was extracted by U-shaped detection network,and then the obtained feature information to the resolution of the input image through the upsampling operation was restored.At the same time,in order to ensure higher detection accuracy while extracting high-level semantic information of the image,the output features of each stage in U-shaped detection network would be merged with the corresponding output features through the upsampling layer.Further the hidden feature information between tampered and un-tampered regions in the image upon the characteristics of the general network was explored by U-shaped detection network,which could be realized quickly by using its end-to-end network structure and extracting the attributes of strong correlation information among image contexts that could ensure high-precision detection results.Finally,the conditional random field was used to optimize the output of the U-shaped detection network to obtain a more exact detection results.The experimental results show that the proposed algorithm outperforms those traditional forgery detection algorithms based on single image attribute and the current deep learning-based detection algorithm,and has good robustness.…”
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THE ALGORITHM OF THE CASE FORMATION DURING THE DEVELOPMENT OF CLINICAL DISCIPLINES IN MEDICAL SCHOOL by Andrey A. Garanin, Reseda M. Garanina

“…The aim of the study is to develop the algorithm of formation of the case on discipline «Clinical Medicine». …”
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Optimization of Deep Neural Networks Using a Micro Genetic Algorithm by Ricardo Landa, David Tovias-Alanis, Gregorio Toscano

“…Our approach applies the paradigm of transfer learning, enabling training without the need for extensive datasets. A micro genetic algorithm requires fewer computational resources due to its reduced population size, while still preserving a substantial degree of the search capabilities found in algorithms with larger populations. …”
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Precise Retrieval of Sentinel-1 Data by Minimizing the Redundancy With Greedy Algorithm by Kaiwen Yang, Lei Zhang, Jicang Wu, Jinsong Qian

“…Aiming to address this issue, we present here an optimized retrieval method grounded in a greedy algorithm, which can substantially reduce redundant data by approximately 20&#x2013;65&#x0025; while ensuring comprehensive data coverage over the areas of interest. …”
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Grouped Byzantine fault tolerant consensus algorithm based on aggregated signatures by Yong Wang, Qiancheng Wan, Yifan Wu, Lijie Chen

“…Experimental results show that the GABFT algorithm significantly improves system throughput and scalability while reducing latency and communication overhead, making it well-suited for large-scale networks.…”
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Development and application of piano accompanying system based on New Fingerprint algorithm by Cheng Lv

“…To solve the audio retrieval when users are learning online piano, a New Fingerprint (NF) is designed based on the button behavior of users while playing the piano. Taking NF algorithm as the core, combined with music signal analysis technology, automatic piano transcription technology and alignment technology, an intelligent piano audio analysis module is constructed. …”
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CSA: Utility Optimization Scheduling Algorithm for IoT Blockchain Sharding Committees by Xin Cong, Qi Jing, Lingling Zi, Changjiang Lin

“…CSA dynamically adjusts the committee members to achieve near-optimal solutions while addressing operational constraints. Theoretical analysis demonstrates the convergence bounds of the algorithm and its robustness against Sybil and eclipse attacks, ensuring high entropy for committee selection. …”
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A Clustering Algorithm for Large Datasets Based on Detection of Density Variations by Adrián Josué Ramírez-Díaz, José Francisco Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa

“…However, while these algorithms can adapt to large datasets by building clusters with arbitrary shapes by identifying low-density regions, they usually struggle to identify density variations. …”
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Robust Crater Detection Algorithm Based on Maximum Entropy Threshold Segmentation by Peng Wu, Rongjun Mu, Yanpeng Deng

“…The proposed method has the following advantages: (1) it has strong robustness and is capable of extracting complete craters under multiple illumination conditions, which makes it suitable for the extraction of large-scale planetary and lunar images; (2) the extracted crater edges are clear and complete and do not merge with the surrounding environment edge; and (3) it avoids the problem of parameter sensitivity that is present in a traditional CDA algorithm. The proposed method was verified using an image taken by the Chang’e-2 lunar probe, and a comparison with the traditional method based on morphology and adaptive Canny edge detection shows that the number of craters detected increases by more than 35%, while the computational efficiency is improved by more than 40%.…”
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