Analysis PC2 AU-ROC curve. حسب Mohd Mustaqeem (19106494)

"…Moreover, traditional SDP models lack transparency and interpretability, which impacts stakeholder confidence in the Software Development Life Cycle (SDLC). We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. …"

PROMISE defects prediction attribute aspects. حسب Mohd Mustaqeem (19106494)

"…Moreover, traditional SDP models lack transparency and interpretability, which impacts stakeholder confidence in the Software Development Life Cycle (SDLC). We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. …"

Internal architecture of the SPAM-XAI model. حسب Mohd Mustaqeem (19106494)

"…Moreover, traditional SDP models lack transparency and interpretability, which impacts stakeholder confidence in the Software Development Life Cycle (SDLC). We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. …"

SPAM-XAI compared with previous models. حسب Mohd Mustaqeem (19106494)

"…Moreover, traditional SDP models lack transparency and interpretability, which impacts stakeholder confidence in the Software Development Life Cycle (SDLC). We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. …"

SPAM-XAI confusion matrix using PC2 dataset. حسب Mohd Mustaqeem (19106494)

"…Moreover, traditional SDP models lack transparency and interpretability, which impacts stakeholder confidence in the Software Development Life Cycle (SDLC). We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. …"

Overview of SPAM-XAI model complete architecture. حسب Mohd Mustaqeem (19106494)

"…Moreover, traditional SDP models lack transparency and interpretability, which impacts stakeholder confidence in the Software Development Life Cycle (SDLC). We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. …"

SPAM-XAI using the PC1 dataset. حسب Mohd Mustaqeem (19106494)

"…Moreover, traditional SDP models lack transparency and interpretability, which impacts stakeholder confidence in the Software Development Life Cycle (SDLC). We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. …"

SPAM-XAI using the CM1 dataset. حسب Mohd Mustaqeem (19106494)

"…Moreover, traditional SDP models lack transparency and interpretability, which impacts stakeholder confidence in the Software Development Life Cycle (SDLC). We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. …"

Analysis of CM1 ROC curve. حسب Mohd Mustaqeem (19106494)

"…Moreover, traditional SDP models lack transparency and interpretability, which impacts stakeholder confidence in the Software Development Life Cycle (SDLC). We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. …"

SPAM-XAI confusion matrix using PC1 dataset. حسب Mohd Mustaqeem (19106494)

"…Moreover, traditional SDP models lack transparency and interpretability, which impacts stakeholder confidence in the Software Development Life Cycle (SDLC). We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. …"

Analysis PC1 AU-ROC curve. حسب Mohd Mustaqeem (19106494)

"…Moreover, traditional SDP models lack transparency and interpretability, which impacts stakeholder confidence in the Software Development Life Cycle (SDLC). We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. …"

Image 1_A multimodal AI-driven framework for cardiovascular screening and risk assessment in diverse athletic populations: innovations in sports cardiology.png حسب Minjin Guo (22751300)

"…RSEE projects heterogeneous input data into an exertion-conditioned latent space, aligning model predictions with observed physiological variance and mitigating false positives by explicitly modeling the overlap between athletic remodeling and subclinical pathology.…"

Supporting data for “The role of forest composition heterogeneity on temperate ecosystem carbon dynamic under climate change" حسب Ziyu Lin (9151064)

"…The process includes (1) harmonizing Landsat 5, 7, 8, and Sentinel-2 data using the HLS algorithm, and (2) filling temporal gaps with an optimized object-based STARFM fusion algorithm. …"

Data used to drive the Double Layer Carbon Model in the Qinling Mountains. حسب Huiwen Li (17705280)

"…These compartments help accurately simulate the dynamics of SOC by considering both the fast-cycling and slow-cycling components of organic matter decomposition. …"

Data Sheet 1_Machine learning based assessment of hoarseness severity: a multi-sensor approach centered on high-speed videoendoscopy.docx حسب Tobias Schraut (21488108)

An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows حسب 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. …"

Supplementary file 1_OncoPSM: an interactive tool for cost-effectiveness analysis using partitioned survival models in oncology trial.xlsx حسب Xulong Qiu (22123102)

"…Based on these functions, we constructed Partitioned Survival Model (PSM), calculated the probability of each survival state per cycle, and combined these with utility values to compute the effect per cycle and the incremental effect for the experimental group. …"