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Journal articles on the topic 'Depth data encoding algorithm'

Author: Grafiati

Published: 6 September 2023

Last updated: 31 July 2025

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1

Liu, Ziqi, Yong Xue, Jiaqi Zhao, et al. "A Multi-Strategy Siberian Tiger Optimization Algorithm for Task Scheduling in Remote Sensing Data Batch Processing." Biomimetics 9, no. 11 (2024): 678. http://dx.doi.org/10.3390/biomimetics9110678.

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With advancements in integrated space–air–ground global observation capabilities, the volume of remote sensing data is experiencing exponential growth. Traditional computing models can no longer meet the task processing demands brought about by the vast amounts of remote sensing data. As an important means of processing remote sensing data, distributed cluster computing’s task scheduling directly impacts the completion time and the efficiency of computing resource utilization. To enhance task processing efficiency and optimize the allocation of computing resources, this study proposes a Multi-

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Gao, Binxi. "Multimodal Data Mining Based on Self Attention Feature Alignment." Highlights in Science, Engineering and Technology 115 (October 28, 2024): 169–74. http://dx.doi.org/10.54097/9r70rn59.

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This study explores the application of multimodal data mining in text and image vector processing, aiming to improve the depth and breadth of data analysis by integrating information from different data types. We use the FairFace dataset combined with the CLIP model encoding layer to obtain text and image vectors, and use the K-Means clustering algorithm to achieve vector dimensionality reduction. Subsequently, we introduced the bipartite graph matching algorithm to achieve maximum matching between text vectors and image vectors, and calculated the contrastive learning loss and similarity loss

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Ranga, Deepak, Aryan Rana, Sunil Prajapat, Pankaj Kumar, Kranti Kumar, and Athanasios V. Vasilakos. "Quantum Machine Learning: Exploring the Role of Data Encoding Techniques, Challenges, and Future Directions." Mathematics 12, no. 21 (2024): 3318. http://dx.doi.org/10.3390/math12213318.

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Quantum computing and machine learning (ML) have received significant developments which have set the stage for the next frontier of creative work and usefulness. This paper aims at reviewing various data-encoding techniques in Quantum Machine Learning (QML) while highlighting their significance in transforming classical data into quantum systems. We analyze basis, amplitude, angle, and other high-level encodings in depth to demonstrate how various strategies affect encoding improvements in quantum algorithms. However, they identify major problems with encoding in the framework of QML, includi

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Yang, Hui, Qiuming Liu, and Chao Song. "Adaptive QP algorithm for depth range prediction and encoding output in virtual reality video encoding process." PLOS ONE 19, no. 9 (2024): e0310904. http://dx.doi.org/10.1371/journal.pone.0310904.

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In order to reduce the encoding complexity and stream size, improve the encoding performance and further improve the compression performance, the depth prediction partition encoding is studied in this paper. In terms of pattern selection strategy, optimization analysis is carried out based on fast strategic decision-making methods to ensure the comprehensiveness of data processing. In the design of adaptive strategies, different adaptive quantization parameter adjustment strategies are adopted for the equatorial and polar regions by considering the different levels of user attention in 360 deg

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Liu, Peizhuo. "The Reconstruction of RDP-Based RAID-6 under Double Disk Failures: An In-depth Analysis." Highlights in Science, Engineering and Technology 87 (March 26, 2024): 52–58. http://dx.doi.org/10.54097/v57j7f19.

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As the data volume increases rapidly, the necessity for data storage systems is also rising significantly. The RAID technologies have been widely applied in data storage, due to their reliability and reconstruction ability under disk failures. Furthermore, as the disks in RAIDs increase, disk failures occur more frequently, arousing a considerable amount of data loss. Given this situation, this paper proposes a reconstruction algorithm for double-disk failures and an extension coding scheme which is called RDP+ based on Row-diagonal parity (RDP) to address single-disk failures which are the mo

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Gois, Marcilyanne M., Paulo Matias, André B. Perina, Vanderlei Bonato, and Alexandre C. B. Delbem. "A Parallel Hardware Architecture based on Node-Depth Encoding to Solve Network Design Problems." International Journal of Natural Computing Research 4, no. 1 (2014): 54–75. http://dx.doi.org/10.4018/ijncr.2014010105.

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Many problems involving network design can be found in the real world, such as electric power circuit planning, telecommunications and phylogenetic trees. In general, solutions for these problems are modeled as forests represented by a graph manipulating thousands or millions of input variables, making it hard to obtain the solutions in a reasonable time. To overcome this restriction, Evolutionary Algorithms (EAs) with dynamic data structures (encodings) have been widely investigated to increase the performance of EAs for Network Design Problems (NDPs). In this context, this paper proposes a p

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Chang, Wanjun, and Dongfang Zhang. "A Novel Semantic Segmentation Approach Using Improved SegNet and DSC in Remote Sensing Images." International Journal on Semantic Web and Information Systems 19, no. 1 (2023): 1–17. http://dx.doi.org/10.4018/ijswis.332769.

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An improved SegNet semantic segmentation model is proposed to address the issue of traditional classification algorithms and shallow learning algorithms not being suitable for extracting information from high-resolution remote sensing images. During the research process, space remote sensing images obtained from the GF-1 satellite were used as the data source. In order to improve the operational efficiency of the encoding network, the pooling layer in the encoding network is removed and the ordinary convolutional layer is replaced with a depth-wise separable convolution. By decoding the last l

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Mou, Dingkang, and Yumin Dong. "Color image encryption algorithm based on novel dynamic DNA encoding and chaotic system*." Physica Scripta 99, no. 6 (2024): 065201. http://dx.doi.org/10.1088/1402-4896/ad3ff1.

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Abstract To enhance the security of image data, prevent unauthorized access, tampering, and leakage, maintain personal privacy, protect intellectual property rights, and ensure the integrity of images during transmission and storage. This study introduces an innovative color image encryption scheme based on dynamic DNA encoding operations and chaotic systems. By simulating a quantum random walk, a random key is generated to enhance the security of the confidential system. In addition, we integrated the enhanced Josephus problem into DNA coding rules to create dynamic DNA coding rules. At the s

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Wang, Fengqin, Zhiying Wang, and Qiuwen Zhang. "Efficient CU Decision Algorithm for VVC 3D Video Depth Map Using GLCM and Extra Trees." Electronics 12, no. 18 (2023): 3914. http://dx.doi.org/10.3390/electronics12183914.

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The new generation of 3D video is an international frontier research hotspot. However, the large amount of data and high complexity are core problems to be solved urgently in 3D video coding. The latest generation of video coding standard versatile video coding (VVC) adopts the quad-tree with nested multi-type tree (QTMT) partition structure, and the coding efficiency is much higher than other coding standards. However, the current research work undertaken for VVC is less for 3D video. In light of this context, we propose a fast coding unit (CU) decision algorithm based on the gray level co-oc

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He, Shuqian, Zhengjie Deng, and Chun Shi. "Fast Decision Algorithm of CU Size for HEVC Intra-Prediction Based on a Kernel Fuzzy SVM Classifier." Electronics 11, no. 17 (2022): 2791. http://dx.doi.org/10.3390/electronics11172791.

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High Efficiency Video Coding (HEVC) achieves a significant improvement in compression efficiency at the cost of extremely high computational complexity. Therefore, large-scale and wide deployment applications, especially mobile real-time video applications under low-latency and power-constrained conditions, are more challenging. In order to solve the above problems, a fast decision method for intra-coding unit size based on a new fuzzy support vector machine classifier is proposed in this paper. The relationship between the depth levels of coding units is accurately expressed by defining the c

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Zhou, Yiran, Yijian Wu, Xiaohu Guo, and Wenyong Gui. "Extended Depth-of-Field Imaging Using Multi-Scale Convolutional Neural Network Wavefront Coding." Electronics 12, no. 19 (2023): 4028. http://dx.doi.org/10.3390/electronics12194028.

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Wavefront encoding (WFC) is a depth-of-field (DOF) extension technology that combines optical encoding and digital decoding. The system extends DOF at the expense of intermediate image quality and then decodes it through an image restoration algorithm to obtain a clear image. Affected by point spread differences, traditional decoding methods are often accompanied by artifacts and noise amplification problems. In this paper, based on lens-combined modulated wavefront coding (LM-WFC), we simulate the imaging process under different object distances, generate a simulation data set of WFC, and tra

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Lilhore, Umesh Kumar, Osamah Ibrahim Khalaf, Sarita Simaiya, et al. "A depth-controlled and energy-efficient routing protocol for underwater wireless sensor networks." International Journal of Distributed Sensor Networks 18, no. 9 (2022): 155013292211171. http://dx.doi.org/10.1177/15501329221117118.

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Underwater wireless sensor network attracted massive attention from researchers. In underwater wireless sensor network, many sensor nodes are distributed at different depths in the sea. Due to its complex nature, updating their location or adding new devices is pretty challenging. Due to the constraints on energy storage of underwater wireless sensor network end devices and the complexity of repairing or recharging the device underwater, this is highly significant to strengthen the energy performance of underwater wireless sensor network. An imbalance in power consumption can cause poor perfor

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Sihare, Muskan. "Evaluation of Machine Learning Methods for Prediction Student Performance." International Journal for Research in Applied Science and Engineering Technology 12, no. 1 (2024): 534–44. http://dx.doi.org/10.22214/ijraset.2024.58001.

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Abstract: Significant findings and useful insights have emerged from the study of using machine learning techniques to predict student performance. used large datasets with a wide variety of demographic, socioeconomic, and academic performance data to conduct in-depth evaluations of several machine learning methods. Our study highlighted the importance of careful data preprocessing, which involves fundamental steps like classifying student performance and doing in-depth exploratory data analysis (EDA). To ensure the validity of our model assessments, we meticulously split the dataset into trai

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Petrova, Natalia, and Natalia Mokshina. "Using FIBexDB for In-Depth Analysis of Flax Lectin Gene Expression in Response to Fusarium oxysporum Infection." Plants 11, no. 2 (2022): 163. http://dx.doi.org/10.3390/plants11020163.

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Plant proteins with lectin domains play an essential role in plant immunity modulation, but among a plurality of lectins recruited by plants, only a few members have been functionally characterized. For the analysis of flax lectin gene expression, we used FIBexDB, which includes an efficient algorithm for flax gene expression analysis combining gene clustering and coexpression network analysis. We analyzed the lectin gene expression in various flax tissues, including root tips infected with Fusarium oxysporum. Two pools of lectin genes were revealed: downregulated and upregulated during the in

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Yang, Chunlin, Zexian Li, Hongmei Yao, Zhaobing Fan, Guofeng Zhang, and Jianshe Liu. "Dictionary-based Block Encoding of Sparse Matrices with Low Subnormalization and Circuit Depth." Quantum 9 (July 22, 2025): 1805. https://doi.org/10.22331/q-2025-07-22-1805.

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Block encoding severs as an important data input model in quantum algorithms, enabling quantum computers to simulate non-unitary operators effectively. In this paper, we propose an efficient block-encoding protocol for sparse matrices based on a novel data structure, called the dictionary data structure, which classifies all non-zero elements according to their values and indices. Non-zero elements with the same values, lacking common column and row indices, belong to the same classification in our block-encoding protocol's dictionary. When compiled into the {U(2), CNOT} gate set, the protocol

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Tăbuşand and Can Kaya. "Information Theoretic Modeling of High Precision Disparity Data for Lossy Compression and Object Segmentation." Entropy 21, no. 11 (2019): 1113. http://dx.doi.org/10.3390/e21111113.

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In this paper, we study the geometry data associated with disparity map or depth map images in order to extract easy to compress polynomial surface models at different bitrates, proposing an efficient mining strategy for geometry information. The segmentation, or partition of the image pixels, is viewed as a model structure selection problem, where the decisions are based on the implementable codelength of the model, akin to minimum description length for lossy representations. The intended usage of the extracted disparity map is to provide to the decoder the geometry information at a very sma

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Qasim, Osama Abd, and Sajjad Golshannavaz. "Enhancing data security using a multi-layer encryption system." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 2 (2025): 1961. https://doi.org/10.11591/ijece.v15i2.pp1961-1967.

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This study highlights the interesting potential of a new multilayer cryptography scheme for reliable data protection in the field of cybersecurity. To do so, an intensive examination of a multi-layer encryption mechanism is proposed to reinforce the defenses in opposition to online threats to touchy data. The strategy is multilevel, with a superior digital dictionary serving as the foundation for the primary layer. The laborious procedures that went into making this dictionary, including rotation differences, ASCII conversion, and chaotic matrix era, upload to its encoding trouble. A modified

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Qasim, Osama Abd, and Sajjad Golshannavaz. "Enhancing data security using a multi-layer encryption system." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 2 (2025): 1961–67. https://doi.org/10.11591/ijece.v15i2.pp1961-1967.

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This study highlights the interesting potential of a new multilayer cryptography scheme for reliable data protection in the field of cybersecurity. To do so, an intensive examination of a multi-layer encryption mechanism is proposed to reinforce the defenses in opposition to online threats to touchy data. The strategy is multilevel, with a superior digital dictionary serving as the foundation for the primary layer. The laborious procedures that went into making this dictionary, including rotation differences, ASCII conversion, and chaotic matrix era, upload to its encoding trouble. A modified

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Jiang, Ming-xin, Xian-xian Luo, Tao Hai, Hai-yan Wang, Song Yang, and Ahmed N. Abdalla. "Visual Object Tracking in RGB-D Data via Genetic Feature Learning." Complexity 2019 (May 2, 2019): 1–8. http://dx.doi.org/10.1155/2019/4539410.

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Visual object tracking is a fundamental component in many computer vision applications. Extracting robust features of object is one of the most important steps in tracking. As trackers, only formulated on RGB data, are usually affected by occlusions, appearance, or illumination variations, we propose a novel RGB-D tracking method based on genetic feature learning in this paper. Our approach addresses feature learning as an optimization problem. As owning the advantage of parallel computing, genetic algorithm (GA) has fast speed of convergence and excellent global optimization performance. At t

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Han, Lei, Xiaohua Huang, Zhan Shi, and Shengnan Zheng. "Depth Estimation from Light Field Geometry Using Convolutional Neural Networks." Sensors 21, no. 18 (2021): 6061. http://dx.doi.org/10.3390/s21186061.

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Depth estimation based on light field imaging is a new methodology that has succeeded the traditional binocular stereo matching and depth from monocular images. Significant progress has been made in light-field depth estimation. Nevertheless, the balance between computational time and the accuracy of depth estimation is still worth exploring. The geometry in light field imaging is the basis of depth estimation, and the abundant light-field data provides convenience for applying deep learning algorithms. The Epipolar Plane Image (EPI) generated from the light-field data has a line texture conta

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Narozhnyi, Volodymyr, and Vyacheslav Kharchenko. "Semantic clustering method using integration of advanced LDA algorithm and BERT algorithm." INNOVATIVE TECHNOLOGIES AND SCIENTIFIC SOLUTIONS FOR INDUSTRIES, no. 1 (27) (July 2, 2024): 140–53. http://dx.doi.org/10.30837/itssi.2024.27.140.

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The subject of the study is an in-depth semantic data analysis based on the modification of the Latent Dirichlet Allocation (LDA) methodology and its integration with the bidirectional encoding representation of transformers (BERT). Relevance. Latent Dirichlet Allocation (LDA) is a fundamental topic modeling technique that is widely used in a variety of text analysis applications. Although its usefulness is widely recognized, traditional LDA models often face limitations, such as a rigid distribution of topics and inadequate representation of semantic nuances inherent in natural language. The

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Choi, Jiho, and Sang Jun Lee. "Neural Radiance Fields for Fisheye Driving Scenes Using Edge-Aware Integrated Depth Supervision." Sensors 24, no. 21 (2024): 6790. http://dx.doi.org/10.3390/s24216790.

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Neural radiance fields (NeRF) have become an effective method for encoding scenes into neural representations, allowing for the synthesis of photorealistic views of unseen views from given input images. However, the applicability of traditional NeRF is significantly limited by its assumption that images are captured for object-centric scenes with a pinhole camera. Expanding these boundaries, we focus on driving scenarios using a fisheye camera, which offers the advantage of capturing visual information from a wide field of view. To address the challenges due to the unbounded and distorted char

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Algazy, Kunbolat, Kairat Sakan, Andrey Varennikov, and Nursulu Kapalova. "Application of satisfiability problem solvers for assessing the strength of hash algorithms." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 3 (2025): 3191. https://doi.org/10.11591/ijece.v15i3.pp3191-3201.

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This article presents a methodology for assessing the strength of cryptographic algorithms and provides experimental data obtained from studying the cryptographic strength of the developed hash function HBC-256 using modern satisfiability problem (SAT) solvers. Various SAT solvers implementing the conflict-driven clause learning (CDCL) algorithm, based on the Davis-Putnam-Logemann-Loveland (DPLL) algorithm, were used to conduct the cryptanalysis of the HBC-256 hash function. The most effective was the parallel SAT solver Parkissat, and thus it was used for more in-depth research. A series of e

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Hasan, Syahril, Saiful Do Abdullah, and Arisandy Ambarita. "Penerapan Algoritma Huffman Coding Dalam Menghemat Ruang Penyimpanan Data Multimedia File (Teks dan Gambar) Berbasis Python." Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika 7, no. 2 (2024): 118–27. http://dx.doi.org/10.47324/ilkominfo.v7i2.268.

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Abstrak: Kompresi adalah teknik yang digunakan untuk mengompres data agar sesuai dengan ukuran pada media yang digunakan. seperti backup data, transmisi data, dan keamanan data. Algoritma Huffman dalam kompresi teks dapat menghasilkan pengurangan ukuran file yang signifikan tanpa kehilangan informasi, struktur pohon Huffman dan pengkodean karakter memberikan wawasan yang mendalam tentang cara algoritma bekerja. Metode Pengembangan yang gunakan adalah Algoritma Huffman yang di mulai dengan Pengumpulan Frekuensi, Pembuatan Tree Huffman, Pembuatan Kode Huffman, Pembuatan Tabel Kompresi, dan Kompr

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Yi, Luying, Xiangyu Guo, Liqun Sun, and Bo Hou. "Structural and Functional Sensing of Bio-Tissues Based on Compressive Sensing Spectral Domain Optical Coherence Tomography." Sensors 19, no. 19 (2019): 4208. http://dx.doi.org/10.3390/s19194208.

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In this paper, a full depth 2D CS-SDOCT approach is proposed, which combines two-dimensional (2D) compressive sensing spectral-domain optical coherence tomography (CS-SDOCT) and dispersion encoding (ED) technologies, and its applications in structural imaging and functional sensing of bio-tissues are studied. Specifically, by introducing a large dispersion mismatch between the reference arm and sample arm in SD-OCT system, the reconstruction of the under-sampled A-scan data and the removal of the conjugated images can be achieved simultaneously by only two iterations. The under-sampled B-scan

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Huang, Zedong, Jinan Gu, Jing Li, Shuwei Li, and Junjie Hu. "Depth Estimation of Monocular PCB Image Based on Self-Supervised Convolution Network." Electronics 11, no. 12 (2022): 1812. http://dx.doi.org/10.3390/electronics11121812.

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To improve the accuracy of using deep neural networks to predict the depth information of a single image, we proposed an unsupervised convolutional neural network for single-image depth estimation. Firstly, the network is improved by introducing a dense residual module into the encoding and decoding structure. Secondly, the optimized hybrid attention module is introduced into the network. Finally, stereo image is used as the training data of the network to realize the end-to-end single-image depth estimation. The experimental results on KITTI and Cityscapes data sets show that compared with so

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An, Chang, Yuan Zhang, and Zhifu Chai. "Study on Wind-eroded Concave Surface Morphology Based on Laser Radar and Color Space Encoding." Journal of Physics: Conference Series 3004, no. 1 (2025): 012072. https://doi.org/10.1088/1742-6596/3004/1/012072.

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Abstract This article takes a test area in the eastern edge of Greenbulongtan, Barunbireli Town, Alxa Left Banner, Alxa League, Inner Mongolia Autonomous Region, where a polylactic acid fiber (PLA) sand barrier is installed, as the research object. By using the three-dimensional scanning technology and software such as Sketch Up with the portable device iPhone12 Pro Max equipped with a LiDAR, the boundary length, erosion depth, erosion coefficient, and erosion intensity of the concave surface of the sand barrier in the test area were measured through the method of model size measurement. In ad

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Wei, Lijun. "Comprehensive evaluation and enhancement of Reed-Solomon codes in RAID6 data storage systems." Applied and Computational Engineering 32, no. 1 (2024): 66–70. http://dx.doi.org/10.54254/2755-2721/32/20230185.

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This paper provides an in-depth examination and optimization of Reed-Solomon codes within the context of Redundant Array of Independent Disks 6 (RAID6) data storage configurations. With the swift advancement of digital technology, the need for secure and efficient data storage methods has sharply escalated. This study delves into the application of Reed-Solomon codes, which are acclaimed for their unparalleled ability to rectify multiple errors, and their crucial role in maintaining RAID6 system operation even under multiple disk failures. The intricacies of Reed-Solomon codes are scrutinized,

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Lim, Olivier, Stéphane Mancini, and Mauro Dalla Mura. "Feasibility of a Real-Time Embedded Hyperspectral Compressive Sensing Imaging System." Sensors 22, no. 24 (2022): 9793. http://dx.doi.org/10.3390/s22249793.

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Hyperspectral imaging has been attracting considerable interest as it provides spectrally rich acquisitions useful in several applications, such as remote sensing, agriculture, astronomy, geology and medicine. Hyperspectral devices based on compressive acquisitions have appeared recently as an alternative to conventional hyperspectral imaging systems and allow for data-sampling with fewer acquisitions than classical imaging techniques, even under the Nyquist rate. However, compressive hyperspectral imaging requires a reconstruction algorithm in order to recover all the data from the raw compre

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Wang, Jihua, and Huayu Wang. "A study of 3D model similarity based on surface bipartite graph matching." Engineering Computations 34, no. 1 (2017): 174–88. http://dx.doi.org/10.1108/ec-10-2015-0315.

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Purpose This study aims to compute 3D model similarity by extracting and comparing shape features from the neutral files. Design/methodology/approach In this work, the clear text encoding document STEP (Standard for The Exchange of Product model data) of 3D models was analysed, and the models were characterized by two-depth trees consisting of both surface and shell nodes. All surfaces in the STEP files can be subdivided into three kinds, namely, free, analytical and loop surfaces. Surface similarity is defined by the variation coefficients of distances between data points on two surfaces, and

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Matin, Amir, and Xu Wang. "Compressive Coded Rotating Mirror Camera for High-Speed Imaging." Photonics 8, no. 2 (2021): 34. http://dx.doi.org/10.3390/photonics8020034.

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We develop a novel compressive coded rotating mirror (CCRM) camera to capture events at high frame rates in passive mode with a compact instrument design at a fraction of the cost compared to other high-speed imaging cameras. Operation of the CCRM camera is based on amplitude optical encoding (grey scale) and a continuous frame sweep across a low-cost detector using a motorized rotating mirror system which can achieve single pixel shift between adjacent frames. Amplitude encoding and continuous frame overlapping enable the CCRM camera to achieve a high number of captured frames and high tempor

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Nguyen, T. V., and A. G. Kravets. "Evaluation and Prediction of Trends in the Development of Scientific Research Based on Bibliometric Analysis of Publications." INFORMACIONNYE TEHNOLOGII 27, no. 4 (2021): 195–201. http://dx.doi.org/10.17587/it.27.195-201.

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The article proposes an approach to analyzing and predicting the thematic evolution of research by identifying an upward trend in keywords. Statistical analysis of the vocabulary of publications allows us to trace the depth of penetration of new ideas and methods, which can be set by the frequency of occurrence of words encoding whole concepts. The article presents a developed method for analyzing research trends and an article ranking algorithm based on the structure of a direct citation network. Data for the study was extracted from the Web of Science Core Collection, 6696 publications were

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Agand, Pedram. "Knowledge Distillation from Single-Task Teachers to Multi-Task Student for End-to-End Autonomous Driving." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23375–76. http://dx.doi.org/10.1609/aaai.v38i21.30388.

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In the domain of end-to-end autonomous driving, conventional sensor fusion techniques exhibit inadequacies, particularly when facing challenging scenarios with numerous dynamic agents. Imitation learning hampers the performance by the expert and encounters issues with out-of-distribution challenges. To overcome these limitations, we propose a transformer-based algorithm designed to fuse diverse representations from RGB-D cameras through knowledge distillation. This approach leverages insights from multi-task teachers to enhance the learning capabilities of single-task students, particularly in

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Phermphoonphiphat, Ekasit, Tomohiko Tomita, Takashi Morita, Masayuki Numao, and Ken-Ichi Fukui. "Soft Periodic Convolutional Recurrent Network for Spatiotemporal Climate Forecast." Applied Sciences 11, no. 20 (2021): 9728. http://dx.doi.org/10.3390/app11209728.

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Many machine-learning applications and methods are emerging to solve problems associated with spatiotemporal climate forecasting; however, a prediction algorithm that considers only short-range sequential information may not be adequate to deal with periodic patterns such as seasonality. In this paper, we adopt a Periodic Convolutional Recurrent Network (Periodic-CRN) model to employ the periodicity component in our proposals of the periodic representation dictionary (PRD). Phase shifts and non-stationarity of periodicity are the key components in the model to support. Specifically, we propose

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Frangky, Frangky, Rudolf Sinaga, and M. Raihansyah. "Analisis Segmentasi Pasien Berdasarkan Persepsi Kualitas Pelayanan dengan Algoritma Clustering." Explorer 5, no. 1 (2025): 52–58. https://doi.org/10.47065/explorer.v5i1.1818.

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Patient segmentation based on perceptions of service quality is a crucial step in improving patient experiences, optimizing resources, and enhancing healthcare service quality. However, understanding patients' needs and priorities in depth poses a challenge, particularly for hospitals serving populations with diverse demographic backgrounds. This study aims to cluster patients in a private hospital in Jambi City based on their perceptions of service quality using the K-Means algorithm. Data were collected from a 2022-2023 survey, covering patient demographics and perceptions of service quality

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Liao, Qing, Haoyu Tan, Wuman Luo, and Ye Ding. "Diverse Mobile System for Location-Based Mobile Data." Wireless Communications and Mobile Computing 2018 (August 1, 2018): 1–17. http://dx.doi.org/10.1155/2018/4217432.

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The value of large amount of location-based mobile data has received wide attention in many research fields including human behavior analysis, urban transportation planning, and various location-based services. Nowadays, both scientific and industrial communities are encouraged to collect as much location-based mobile data as possible, which brings two challenges: (1) how to efficiently process the queries of big location-based mobile data and (2) how to reduce the cost of storage services, because it is too expensive to store several exact data replicas for fault-tolerance. So far, several de

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Prayudani, Santi, Yous Sibarani, Azrizal Salam, and Arif Ridho Lubis. "Perbandingan Kinerja Model Pembelajaran Mesin Random Forest dan K-Nearest Neighbor (KNN) untuk Prediksi Risiko Kredit pada Layanan Pinjaman Online." Journal Software, Hardware and Information Technology 5, no. 2 (2025): 118–27. https://doi.org/10.24252/shift.v5i2.204.

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This study aims to compare the performance of two popular machine learning algorithms, Random Forest and K-Nearest Neighbor (KNN), in predicting creditworthiness in online lending systems. The research uses the publicly available Loan Approval Prediction Dataset from Kaggle, which contains borrower profiles such as employment status, number of dependents, annual income, loan amount, loan term, and credit score. Data preprocessing included cleaning, handling missing values, outlier removal, and transformation through normalization and encoding. The dataset was divided into 80% training data and

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Tasnim, Nusrat, and Joong-Hwan Baek. "Deep Learning-Based Human Action Recognition with Key-Frames Sampling Using Ranking Methods." Applied Sciences 12, no. 9 (2022): 4165. http://dx.doi.org/10.3390/app12094165.

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Nowadays, the demand for human–machine or object interaction is growing tremendously owing to its diverse applications. The massive advancement in modern technology has greatly influenced researchers to adopt deep learning models in the fields of computer vision and image-processing, particularly human action recognition. Many methods have been developed to recognize human activity, which is limited to effectiveness, efficiency, and use of data modalities. Very few methods have used depth sequences in which they have introduced different encoding techniques to represent an action sequence into

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Pribadi, Firman, and Jaeni B. Wastap. "Arts Journalism In The Digital Era: Challenges, Transformations, And Innovative Strategies." ARRUS Journal of Social Sciences and Humanities 5, no. 1 (2025): 761–74. https://doi.org/10.35877/soshum3523.

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Arts and culture journalism in Indonesia faces major challenges in the era of digital transformation, which is characterized by the lack of regeneration of journalists, the dominance of digital algorithms, and economic pressures that shift the focus of news from the substance of art to entertainment. This research aims to analyze the main challenges in the regeneration of arts and culture journalists, understand the impact of media transformation on art reporting, and offer strategic solutions for the revitalization of arts and culture journalism. Using a case study-based descriptive qualitati

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Gatti, Giancarlo, Daniel Huerga, Enrique Solano, and Mikel Sanz. "Random access codes via quantum contextual redundancy." Quantum 7 (January 13, 2023): 895. http://dx.doi.org/10.22331/q-2023-01-13-895.

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We propose a protocol to encode classical bits in the measurement statistics of many-body Pauli observables, leveraging quantum correlations for a random access code. Measurement contexts built with these observables yield outcomes with intrinsic redundancy, something we exploit by encoding the data into a set of convenient context eigenstates. This allows to randomly access the encoded data with few resources. The eigenstates used are highly entangled and can be generated by a discretely-parametrized quantum circuit of low depth. Applications of this protocol include algorithms requiring larg

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Zhang, Xiaolong, and Wei Wu. "Wireless Communication Physical Layer Sensing Antenna Array Construction and Information Security Analysis." Journal of Sensors 2021 (October 25, 2021): 1–11. http://dx.doi.org/10.1155/2021/9007071.

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Due to the complexity of wireless communication networks and the open nature of wireless links, complex upper layer network encryption cryptographic algorithms are also difficult to implement effectively in complex mobile wireless communication and interconnection networks, and traditional cryptography-based security policies are gradually not well able to meet the security management needs of today’s mobile Internet information era. In this paper, the physical characteristics of the channel in the wireless channel are extracted and used to generate keys, and then, the keys are negotiated so t

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Хромушин, Oleg Khromushin, Хромушин, Viktor Khromushin, Китанина, and K. Kitanina. "About the use of the recognition algorithm of the text in database." Journal of New Medical Technologies. eJournal 10, no. 1 (2016): 0. http://dx.doi.org/10.12737/18445.

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This article presents the features of the use of the recognition algorithm of the text by method &#34;slitherring widening window&#34; for coding the plural reasons to deaths. The used algorithm dynamically &#34;adjusts&#34; degree of the coincidence and finds the most similar variant, as well as allows to recognize the text with grammatical errors and with ceased word in wording of the reason to deaths.  The authors propose three variants to realization of the recognition algorithm of the text, that increase the speed of action. The first variant is based on the eliminat

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Jia Zhao. "Communication and Influence of Traditional Culture Based on Social Network Analysis." Journal of Electrical Systems 20, no. 6s (2024): 450–61. http://dx.doi.org/10.52783/jes.2670.

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Social media significantly impacts traditional cultures, yet existing methods struggle to capture the delicate complexities of online cultural discussions. This lack of depth limits understanding of how traditions evolve and adapt in the digital age. To address this, CaCCGAN: Social Culture Analysis, a framework leveraging social network analysis (SNA) is proposed. Content-Aware Cycle-Consistent Generative Adversarial Network (CaCCGAN) explores communication and influence dynamics surrounding traditional culture on social media platforms. CaCCGAN gathers real-time data on traditional culture f

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Arcolezi, Héber H., and Sébastien Gambs. "Revealing the True Cost of Locally Differentially Private Protocols: An Auditing Perspective." Proceedings on Privacy Enhancing Technologies 2024, no. 4 (2024): 123–41. http://dx.doi.org/10.56553/popets-2024-0110.

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While the existing literature on Differential Privacy (DP) auditing predominantly focuses on the centralized model (e.g., in auditing the DP-SGD algorithm), we advocate for extending this approach to audit Local DP (LDP). To achieve this, we introduce the LDP-Auditor framework for empirically estimating the privacy loss of locally differentially private mechanisms. This approach leverages recent advances in designing privacy attacks against LDP frequency estimation protocols. More precisely, through the analysis of numerous state-of-the-art LDP protocols, we extensively explore the factors inf

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Jia, Yin, Balakrishnan Ramalingam, Rajesh Elara Mohan, Zhenyuan Yang, Zimou Zeng, and Prabakaran Veerajagadheswar. "Deep-Learning-Based Context-Aware Multi-Level Information Fusion Systems for Indoor Mobile Robots Safe Navigation." Sensors 23, no. 4 (2023): 2337. http://dx.doi.org/10.3390/s23042337.

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Hazardous object detection (escalators, stairs, glass doors, etc.) and avoidance are critical functional safety modules for autonomous mobile cleaning robots. Conventional object detectors have less accuracy for detecting low-feature hazardous objects and have miss detection, and the false classification ratio is high when the object is under occlusion. Miss detection or false classification of hazardous objects poses an operational safety issue for mobile robots. This work presents a deep-learning-based context-aware multi-level information fusion framework for autonomous mobile cleaning robo

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Mezher, Mohammad A., Almothana Altamimi, and Ruhaifa Altamimi. "An enhanced Genetic Folding algorithm for prostate and breast cancer detection." PeerJ Computer Science 8 (June 21, 2022): e1015. http://dx.doi.org/10.7717/peerj-cs.1015.

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Cancer’s genomic complexity is gradually increasing as we learn more about it. Genomic classification of various cancers is crucial in providing oncologists with vital information for targeted therapy. Thus, it becomes more pertinent to address issues of patient genomic classification. Prostate cancer is a cancer subtype that exhibits extreme heterogeneity. Prostate cancer contributes to 7.3% of new cancer cases worldwide, with a high prevalence in males. Breast cancer is the most common type of cancer in women and the second most significant cause of death from cancer in women. Breast cancer

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Yu, Xianjia, Sahar Salimpour, Jorge Peña Queralta, and Tomi Westerlund. "General-Purpose Deep Learning Detection and Segmentation Models for Images from a Lidar-Based Camera Sensor." Sensors 23, no. 6 (2023): 2936. http://dx.doi.org/10.3390/s23062936.

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Over the last decade, robotic perception algorithms have significantly benefited from the rapid advances in deep learning (DL). Indeed, a significant amount of the autonomy stack of different commercial and research platforms relies on DL for situational awareness, especially vision sensors. This work explored the potential of general-purpose DL perception algorithms, specifically detection and segmentation neural networks, for processing image-like outputs of advanced lidar sensors. Rather than processing the three-dimensional point cloud data, this is, to the best of our knowledge, the first

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Hasanujjaman, Arnab Banerjee, Utpal Biswas, and Mrinal K. Naskar. "Design and Development of a Hardware Efficient Image Compression Improvement Framework." Micro and Nanosystems 12, no. 3 (2020): 217–25. http://dx.doi.org/10.2174/1876402912666200128125733.

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Background: In the region of image processing, a varied number of methods have already initiated the concept of data sciences optimization, in which, numerous global researchers have put their efforts upon the reduction of compression ratio and increment of PSNR. Additionally, the efforts have also separated into hardware and processing sections, that would help in emerging more prospective outcomes from the research. In this particular paper, a mystical concept for the image segmentation has been developed that helps in splitting the image into two different halves’, which is further termed a

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Koffka, Khan. "Advancements in 360-Degree Virtual Reality Video Streaming: A Comprehensive Overview." Advancements in 360-Degree Virtual Reality Video Streaming: A Comprehensive Overview 8, no. 12 (2023): 13. https://doi.org/10.5281/zenodo.10350684.

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This paper presents a thorough examination of the recent advancements in 360-degree virtual reality (VR) video streaming technology, offering a comprehensive overview of its transformative impact on immersive digital experiences. The evolution of virtual reality is explored within the context of its rapid technological growth, setting the stage for an in-depth analysis of 360-degree video streaming. The underlying technologies, encompassing panoramic content capture, encoding, and efficient data transmission, are discussed in detail. Addressing challenges such as bandwidth constraints and late

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Murale, C., M. Sundarambal, and R. Nedunchezhian. "Analysis on Ensemble Methods for the Prediction of Cardiovascular Disease." Journal of Medical Imaging and Health Informatics 11, no. 10 (2021): 2529–37. http://dx.doi.org/10.1166/jmihi.2021.3839.

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Coronary Heart disease is one of the dominant sources of death and morbidity for the people worldwide. The identification of cardiac disease in the clinical review is considered one of the main problems. As the amount of data grows increasingly, interpretation and retrieval become even more complex. In addition, the Ensemble learning prediction model seems to be an important fact in this area of study. The prime aim of this paper is also to forecast CHD accurately. This paper is intended to offer a modern paradigm for prediction of cardiovascular diseases with the use of such processes such as

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