Search alternatives:
identification algorithm » optimization algorithm (Expand Search), detection algorithm (Expand Search)
scale processing » sample processing (Expand Search), image processing (Expand Search), scale processes (Expand Search)
local scale » global scale (Expand Search)
identification algorithm » optimization algorithm (Expand Search), detection algorithm (Expand Search)
scale processing » sample processing (Expand Search), image processing (Expand Search), scale processes (Expand Search)
local scale » global scale (Expand Search)
-
1
The proposed pixel-matching algorithm process.
Published 2023“…The processes involved in recognizing edges are filtering, boosting, recognizing, and localizing. …”
-
2
-
3
The SIM analysis with different algorithms.
Published 2023“…The processes involved in recognizing edges are filtering, boosting, recognizing, and localizing. …”
-
4
The MSE analysis with different algorithms.
Published 2023“…The processes involved in recognizing edges are filtering, boosting, recognizing, and localizing. …”
-
5
The SNR analysis with different algorithms.
Published 2023“…The processes involved in recognizing edges are filtering, boosting, recognizing, and localizing. …”
-
6
The SNR analysis with different algorithms.
Published 2023“…The processes involved in recognizing edges are filtering, boosting, recognizing, and localizing. …”
-
7
The MSE analysis with different algorithms
Published 2023“…The processes involved in recognizing edges are filtering, boosting, recognizing, and localizing. …”
-
8
-
9
-
10
Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment
Published 2019“…<div><p>An image classification algorithm based on adaptive feature weight updating is proposed to address the low classification accuracy of the current single-feature classification algorithms and simple multifeature fusion algorithms. …”
-
11
The image segmentation using Canny.
Published 2023“…The processes involved in recognizing edges are filtering, boosting, recognizing, and localizing. …”
-
12
Preprocessing procedures and supervised classification applied to a database of systematic soil survey
Published 2019“…The aim of this work was to evaluate the performance of preprocessing procedures and supervised classification approaches for predicting map units from 1:100,000-scale conventional semi-detailed soil surveys. …”
-
13
On the predictibility of A-minor motifs from their local contexts
Published 2022“…In most other cases, these signals are not sufficient for predicting the A-minor motif, however we show that they are good signals for this purpose. All the classification and prediction pipelines rely on automated processes, for which we describe the underlying algorithms and parameters.…”
-
14
Video_1_ES-ImageNet: A Million Event-Stream Classification Dataset for Spiking Neural Networks.MP4
Published 2021“…<p>With event-driven algorithms, especially spiking neural networks (SNNs), achieving continuous improvement in neuromorphic vision processing, a more challenging event-stream dataset is urgently needed. …”
-
15
-
16
PegasosQSVM: A Quantum Machine Learning Approach for Accurate Fake News Detection
Published 2025“…Its successful implementation paves the way for further refinement of quantum machine learning techniques in fake news classification. The PegasosQSVM algorithm encounters, however, some implementation issues on real world Quantum Processing Units(QPU). …”
-
17
An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows
Published 2025“…Experimental Methodology Framework Local Processing Pipeline Architecture Data Flow: Storage I/O → Memory Buffer → CPU/GPU Processing → Cache Coherency → Storage I/O ├── Input Vector: mmap() system call for zero-copy file access ├── Processing Engine: OpenMP parallelization with NUMA-aware thread affinity ├── Memory Management: Custom allocator with hugepage backing └── Output Vector: Direct I/O bypassing kernel page cache Cloud Processing Pipeline Architecture Data Flow: Local Storage → Network Stack → TLS Tunnel → CDN Edge → Origin Server → Processing Grid → Response Pipeline ├── Upload Phase: TCP window scaling with congestion control algorithms ├── Network Layer: Application-layer protocol with adaptive bitrate streaming ├── Server-side Processing: Containerized microservices on Kubernetes orchestration ├── Load Balancing: Consistent hashing with geographic affinity routing └── Download Phase: HTTP/2 multiplexing with server push optimization Dataset Schema and Semantic Structure Primary Data Vectors Field Data Type Semantic Meaning Measurement Unit test_type Categorical Processing paradigm identifier {local_processing, cloud_processing} photo_count Integer Cardinality of input asset vector Count avg_file_size_mb Float64 Mean per-asset storage footprint Mebibytes (2^20 bytes) total_volume_gb Float64 Aggregate data corpus size Gigabytes (10^9 bytes) processing_time_sec Integer Wall-clock execution duration Seconds (SI base unit) cpu_usage_watts Float64 Thermal design power consumption Watts (Joules/second) ram_usage_mb Integer Peak resident set size Mebibytes network_upload_mb Float64 Egress bandwidth utilization Mebibytes energy_consumption_kwh Float64 Cumulative energy expenditure Kilowatt-hours co2_equivalent_g Float64 Carbon footprint estimation Grams CO₂e test_date ISO8601 Temporal execution marker RFC 3339 format hardware_config String Node topology identifier Alphanumeric encoding Statistical Distribution Characteristics The dataset exhibits non-parametric distribution patterns with significant heteroscedasticity across computational load vectors. …”
-
18
-
19
-
20