Inclusion and exclusion criteria. by Bilal Abou Al Ardat (17125080)

Subjects:

Related studies on IDS using deep learning. by Arshad Hashmi (13835488)

“…<div><p>Due to the recent advances in the Internet and communication technologies, network systems and data have evolved rapidly. The emergence of new attacks jeopardizes network security and make it really challenging to detect intrusions. …”

The architecture of the BI-LSTM model. by Arshad Hashmi (13835488)

“…<div><p>Due to the recent advances in the Internet and communication technologies, network systems and data have evolved rapidly. The emergence of new attacks jeopardizes network security and make it really challenging to detect intrusions. …”

Comparison of accuracy and DR on UNSW-NB15. by Arshad Hashmi (13835488)

“…<div><p>Due to the recent advances in the Internet and communication technologies, network systems and data have evolved rapidly. The emergence of new attacks jeopardizes network security and make it really challenging to detect intrusions. …”

Comparison of DR and FPR of UNSW-NB15. by Arshad Hashmi (13835488)

“…<div><p>Due to the recent advances in the Internet and communication technologies, network systems and data have evolved rapidly. The emergence of new attacks jeopardizes network security and make it really challenging to detect intrusions. …”

Data_Sheet_1_Development and Potential Usefulness of the COVID-19 Ag Respi-Strip Diagnostic Assay in a Pandemic Context.xlsx by Pascal Mertens (263437)

Data_Sheet_1_A machine learning based radiomics approach for predicting No. 14v station lymph node metastasis in gastric cancer.docx by Tingting Ma (228102)

Supplementary file 1_A real-world disproportionality analysis of FDA adverse event reporting system (FAERS) events for lecanemab.docx by Linlin Yan (4480570)

“…Using the Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS) algorithms, we conducted a comprehensive analysis of lecanemab-related AEs, restricting the analysis to AEs with the role code of primary suspect (PS).…”

Table 2_Data transformation of unstructured electroencephalography reports by natural language processing: improving data usability for large-scale epilepsy studies.xlsx by Yoon Gi Chung (9604061)

“…</p>Methods<p>The proposed algorithm consists of two distinct phases: a deep learning-based text classification followed by a series of rule-based keyword extraction procedures. …”

Supplementary file 1_Data transformation of unstructured electroencephalography reports by natural language processing: improving data usability for large-scale epilepsy studies.do... by Yoon Gi Chung (9604061)

“…</p>Methods<p>The proposed algorithm consists of two distinct phases: a deep learning-based text classification followed by a series of rule-based keyword extraction procedures. …”

PROTOCOL - Effects of Ashwagandha (Withania somnifera) on Physical Performance: Systematic Review and Bayesian Meta-Analysis by Diego A. Bonilla (9086201)

“…Selected publications that met all the requirements will go on to the next phase of data analysis and synthesis, where a table of their results and findings comparison will be developed and complemented by the authors considering the items mentioned before (see data items).…”

Using artificial intelligence in the development of diagnostic models of coronary artery disease based on ECG features: A scoping review by Rong Yang (21661040)

“…</p><p dir="ltr">Extracted Fields:</p><table><tr><td><p dir="ltr">​​Category​​</p></td><td><p dir="ltr">​​Variables​​</p></td></tr><tr><td><p dir="ltr">Study Metadata​​</p></td><td><p dir="ltr">Authors, year, country, design, objectives</p></td></tr><tr><td><p dir="ltr">Population & Data Source​​</p></td><td><p dir="ltr">Cohort characteristics (age, sex), database (e.g., PTB-XL), sample size</p></td></tr><tr><td><p dir="ltr">AI Model Details​​</p></td><td><p dir="ltr">Algorithm (e.g., 1D-CNN, SVM), ECG features (e.g., QTc, spectral entropy)</p></td></tr><tr><td><p dir="ltr">Performance & Validation​​</p></td><td><p dir="ltr">Metrics (Acc, Sens, Spec), validation method (internal/external), generalizability</p></td></tr><tr><td><p dir="ltr">Critical Appraisal​​</p></td><td><p dir="ltr">Clinical relevance, limitations, implementation barriers</p></td></tr></table><p dir="ltr">7. …”

Earthquake Early Warning Dataset by Kevin Fauvel (7335443)

“…It is an archive of real-time three component positions for 240 stations in the western U.S. from California to Alaska and spanning from October 2018 to the present day. The raw GPS data (observations of phase and range to visible satellites) are processed with an algorithm called FastLane [5] and converted to 1 Hz sampled positions. …”

Table_1_Fueling the flames of colon cancer – does CRP play a direct pro-inflammatory role?.docx by Anne Helene Køstner (11358318)

“…</p>Methods<p>Formalin-fixed, paraffin-embedded (FFPE) tissue samples from 43 stage II and III CC patients, including 20 patients with serum CRP 0-1 mg/L and 23 patients with serum CRP >30 mg/L were immunohistochemically (IHC) stained with a conformation-specific mCRP antibody and selected immune and stromal markers. A digital analysis algorithm was developed for evaluating mCRP distribution within the primary tumors and adjacent normal colon mucosa.…”

An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows by Pierre-Alexis DELAROCHE (22092572)

“…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. …”