Scalability gaps in Computer Science

Representative open questions

Showing 30 of 128 — one per source paper, highest-quality first.

• William & Mary: Comprehensive AI Governance & Activity Inventory (2026) · doi

  No publicly available HPC strategic plan or capacity roadmap addressing AI/ML workload growth was identified. Given the documented research portfolio including deep learning for nuclear physics, CNNs for coastal monitoring, LLM agent research, and diffusion models, the alignment between current compute capacity (600 TFLOPS combined, 12 GPUs across both campuses) and research demand requires explicit capacity planning documentation.

• Inferring High-Dimensional Dynamic Networks Changing with Multiple Covariates (2026) · doi

  The CVN methodology was applied only to the KiKme gene expression dataset with p=191 nodes and two external covariates (cancer group and radiation dose). The generalizability of the covariate-varying network inference approach to datasets with higher-dimensional gene expression spaces (p>1000) and more than two categorical or continuous covariates remains unexplored.

• Prediction of sedimentation concentration profiles in inclined suspension systems: A data-driven neural network framework (2026) · doi

  Experimental validation was conducted at bench-scale geometry with a single fluid matrix and particle type; applicability of the ANN-based framework to field-scale directional drilling operations with varied fluid rheologies, particle size distributions, and higher solids concentrations requires investigation.

• Research on a strongly generalizable fault diagnosis method based on adversarial transfer learning (2026) · doi

  The HDAL model was evaluated only on 13 operational transient conditions (fault classes) from two reactor domains with 31 image samples per class in Domain A. The generalization capability to nuclear power plants with significantly larger numbers of fault classes, different sensor configurations, or reactors from different manufacturer designs remains unexplored in adversarial transfer learning for fault diagnosis.

• A lazy and modular approach to int-blasting (2026) · doi

  The Eager algorithm implementation cannot handle 5,653 out of 25,223 SMT-LIB benchmarks due to stack-overflow errors from deeply nested bit-vector terms. The paper does not investigate alternative techniques or optimizations to handle deeply nested term structures, which represents a scalability limitation for the eager int-blasting approach.

• LLM-Powered Silent Bug Fuzzing in Deep Learning Libraries via Versatile and Controlled Bug Transfer (2026) · doi

  The window size ablation study for Bug Transfer-Guided Test Generation (Fig. 11a) identifies a plateau effect at size 10 where marginal bug discovery gains diminish beyond this threshold. However, the generalizability of this optimal window size across different cross-library transfer scenarios (PyTorch→TensorFlow, PyTorch→JAX) and varying API similarity distributions remains untested, limiting the scalability of batch testing strategy recommendations.

• Enhancing Breast Cancer Diagnosis through Machine Learning: A Robust Approach for Early Detection (2026) · doi

  While the web application was deployed using Streamlit and Flask frameworks, the scalability of the Random Forest breast cancer diagnosis system for real-time processing of large-scale datasets has not been evaluated. Cloud deployment optimization using Kubernetes for handling concurrent users and large mammographic image datasets in clinical environments is proposed but not yet implemented.

• A Motion-Based Compression and Tracking System for Video Camera Trap-Based Insect Behaviour Studies (2026) · doi

  The Raspberry Pi 5 (8 GB) encountered out-of-memory errors when processing large datasets using the stand-alone H.265 codec, but the scalability limits of the proposed integrated motion-based compression approach have not been systematically characterized across different edge device memory configurations. The maximum dataset size, video resolution, and frame rate combinations that can be processed on various resource-constrained platforms (Raspberry Pi 4/5, Jetson Nano) without memory failures should be experimentally determined.

• A general framework for Gaussian Splatting-based human-centric volumetric videos (2026) · doi

  The frame-by-frame independent Gaussian point cloud representation for dynamic volumetric videos ignores extensive geometric and appearance redundancy between frames, resulting in storage and transmission costs that grow linearly with sequence length, making application to long sequences or resource-constrained mobile/XR scenarios impractical without novel cross-frame correspondence mechanisms.

• Adaptive distribution network reconfiguration with renewable energy and EV integration using reverse-multiverse learning archimedes algorithm (2026) · doi

  The RMLAA algorithm was validated only on IEEE 33-bus and 69-bus test systems; validation on larger-scale distribution networks (>100 buses) with multiple microgrids and higher penetration levels of renewable energy sources and EV charging stations is needed to assess scalability and real-world applicability.

• Criminal Face Sketch Recognition and Construction (2026) · doi

  The paper mentions integration with 3D face mapping and real-time CCTV surveillance matching as future features, but provides no methodological details on how sketch-based recognition will scale to large-scale surveillance databases or how the matching algorithm will handle temporal variations in appearance (aging, facial hair, expressions) across extended surveillance periods.

• Smart Prediction of Weather-Induced Flight Delays Applying Deep Learning (2026) · doi

  The system is deployed locally with SQLite and Flask but lacks cloud scalability testing. Deployment on AWS or Azure infrastructure with containerization (Docker/Kubernetes) should be evaluated to assess performance of the hybrid XGBoost-ANN model under high-throughput real-time flight delay prediction workloads.

• ENHANCING IOT SECURITY USING LIGHTWEIGHT BLOCKCHAIN FOR DATA INTEGRITY AND TRACEABILITY (2026) · doi

  The throughput performance of 50 transactions/second was demonstrated only in a single experimental configuration. Scalability limits of the proposed lightweight blockchain model across varying numbers of IoT devices, transaction volumes, and network sizes (e.g., 100 to 10,000+ devices) are not established.

• Deep Learning Based Fish Species and Freshness Detection Using Convolutional Neural Networks (2026) · doi

  The paper does not specify the computational requirements, inference latency, or energy consumption of the MobileNet model when deployed on Raspberry Pi hardware; real-time performance metrics for on-device deep learning inference in embedded systems need quantification.

• Cleansera: A Context-Aware, Algorithm-Centric Data Cleaning System with RAG-Enhanced Intelligence (2026) · doi

  Cleansera's cleaning pipeline includes seven sequential stages (Schema Validation, Duplicate Detection, Missing Value Treatment, Format Standardization, Outlier Handling, Semantic Validation, Quality Checkpoint), but the paper provides no empirical performance benchmarks or scalability analysis on datasets varying in size (from thousands to millions of records) or complexity (number of columns, data types, missing value percentages).

• Unified URL and QR Based Phishing Detection Framework (2026) · doi

  The system demonstrated 100+ hours continuous stability without crashes, but no evaluation of performance degradation under high-volume concurrent requests (e.g., scanning 1000+ URLs/QR codes simultaneously) or with emerging phishing techniques post-deployment is documented.

• Employee Performance Classification and Monitoring using Machine zearning Models (2026) · doi

  The system's stability was validated only with repeated testing sessions on an unspecified dataset size and employee population; scalability to corporate/educational deployments with hundreds of concurrent users and varying hardware configurations (different webcam quality, network bandwidth) remains untested.

• Real-Time Bank Transaction Fraud Detection Using Kafka and Machine Learning (2026) · doi

  The system's scalability using Kubernetes and Docker containerization for managing multiple Kafka brokers and database instances has been mentioned conceptually but lacks empirical validation; performance benchmarks under varying transaction volume loads and latency constraints are absent.

• A Comparative Analysis of Machine Learning and Deep Learning Approaches to Enhanced Fake News Detection (2026) · doi

  While the paper establishes performance benchmarks for machine learning and deep learning approaches to fake news detection, it does not address how these models should handle the scalability challenge of processing greater volumes of fake news in real-time deployment scenarios across social media platforms and online news sites.

• An AI Powered Passive Reconnaissance Kit To Identifies Potential Vulnerabilities By Correlating Open Source Intelligence (OSINT) (2026) · doi

  The system stores collected intelligence locally in JSON format on a microSD card for offline privacy, but no evaluation is provided on the scalability of this local storage approach for continuous scanning of large networks (100+ targets) or the latency introduced by local risk scoring computations versus network transmission constraints.

• An Approximate Decomposition of a Multivariate Polynomial and Its Application (2026) · doi

  Algorithm 1 requires computing coefficients c_{−j_d}^{s_d} with a specific hierarchical dependency structure amenable to parallelization (Remark 9), but no parallel implementation complexity analysis, communication costs, or actual parallel performance metrics are provided for multicore or distributed computing environments.

• AI-Powered Resume Screening and Ranking System (2026) · doi

  The system's performance on large-scale applicant pools (beyond the datasets tested) and its computational efficiency with varying resume volumes are not documented. The paper lacks analysis of how processing time, memory usage, and ranking accuracy scale when the system processes thousands of resumes simultaneously for high-volume recruitment scenarios.

• AI-Based Blood Groups Prediction and Classification Through Image Processing Using CNN (2026) · doi

  The AI-based blood group classification system has not been optimized for deployment on mobile and embedded devices using lightweight models such as TensorFlow Lite or ONNX, limiting accessibility in resource-limited and remote healthcare settings.

• FitCluster AI: A Semi-Personalized Fitness and Diet Recommendation System Using Rule-Based and Lightweight Machine Learning Techniques (2026) · doi

  The system's real-time recommendation generation was observed to be fast, but no comparative performance benchmarking is provided against traditional machine learning-based fitness systems or other lightweight rule-based approaches in terms of inference latency, memory footprint, or scalability to high-concurrency user loads.

• Human Activity Recognition (2026) · doi

  The modular architecture supports adding new gestures (e.g., swipe for navigation) without specifying how gesture confusion matrices or inter-gesture discrimination metrics would be measured when the gesture vocabulary expands beyond the currently implemented set.

• An Adaptive Deep Learning Framework with Enhanced Attention for Precise Load Forecasting in Cloud Computing Environments (2026) · doi

  All computational cost evaluations were conducted on a single Dell XPS 15 laptop (CPU-only, <5% GPU utilization) with 16GB RAM. The framework's scalability and practical deployment efficiency on edge devices with constrained memory (<4GB), on GPU-accelerated cloud nodes, and in distributed inference scenarios with multiple forecasting agents has not been investigated.

• AI-Driven Predictive Maintenance Framework For Industrial IoT Using Hybrid Deep Learning Models (2026) · doi

  Real-time deployment on edge devices with optimized lightweight architectures is proposed as future work, but no quantitative targets for model compression, inference latency, memory footprint, or hardware constraints for edge deployment are provided.

• GeoMarX: empowering geospatial marking and annotation for complex targets in urban environments (2026) · doi

  System performance scalability under varying server capacity and network conditions for large-scale geospatial annotation projects has not been empirically evaluated. Quantitative assessment of GeoMarX scalability thresholds (e.g., number of concurrent users, project area size, annotation volume) is needed.

• Toward autonomous weed management systems in sugarcane crops and an assessment of technological readiness (2026) · doi

  The detection model evaluation is restricted to input resolution analysis up to 2560×2560 pixels for selected architectures, but there is no investigation of how resolution dependencies affect detection accuracy and computational efficiency for real-time weed management systems deployed on resource-constrained autonomous platforms in sugarcane fields.

• A unified deep learning framework for cross-platform harmonization of multi-tracer PET quantification in neurodegenerative disease (2026) · doi

  The framework's generalizability to scanner configurations combining different attenuation correction methods (bone-inclusive vs. bone-exclusive Dixon models, synthetic CT generation variants) across integrated PET/MRI and standalone PET/CT systems in multi-center neurodegenerative disease studies requires systematic evaluation.