Application gaps in Computer Science

Representative open questions

Showing 30 of 242 — one per source paper, highest-quality first.

• Federated learning for privacy-preserving skin cancer classification using deep neural networks (2026) · doi

  The proposed method outperforms state-of-the-art methods on the ISIC 2018 dataset, but its performance on other datasets (ISIC 2019 and PH2) is not compared.

• Turbulence closure in Reynolds-averaged Navier–Stokes and flow inference around a cylinder using physics-informed neural networks and sparse experimental data (2026) · doi

  The hybrid implicit/explicit coupling strategy uses the baseline Spalart–Allmaras model to provide baseline eddy viscosity while the neural network predicts residual Reynolds-force corrections; the applicability and performance of this hybrid approach with alternative baseline turbulence models (k-ε, k-ω, RSM) remains unexplored.

• FPGA-Enabled Machine Learning Applications in Earth Observation: A Systematic Review (2026) · doi

  Data compression and disaster response remain unexplored onboard use cases for FPGA-enabled ML in Earth observation, despite their relevance to the NewSpace era. Lightweight FPGA implementations of state-of-the-art compression methods for SmallSat imaging payloads generating ~640 Mbps data rates present a critical research opportunity to enable real-time alerts for wildfires and post-disaster damage assessments.

• Blockchain-integrated machine learning framework for transparent smart contract vulnerability detection (2026) · doi

  CatBoost achieved macro-F1 of 0.98918 and 0.99156 accuracy on SmartBugs-Wild cluster classification, substantially outperforming vulnerability detection (F1 ~0.78-0.79). The transferability of cluster-level structural representations to vulnerability detection tasks within the same blockchain-integrated framework has not been explored.

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

  The observed inverse relationship between network size (decreasing node count) and network density (increasing interconnectivity) as radiation dose increases from 0 to 2 Gy is descriptive rather than mechanistically explained; the biological reasons why higher radiation selectively eliminates nodes while strengthening remaining gene-gene interactions in cancer versus control groups are not addressed.

• A Machine Learning Perspective on FinTech-Driven Inclusion: Addressing Algorithm Bias in Credit Scoring Systems in Developing Economies (2026) · doi

  The model fit indices (CFI = 0.943, TLI = 0.927, RMSEA = 0.058) demonstrate theoretical framework validity, but the paper does not address how explainability techniques (LIME, SHAP, attention mechanisms) should be implemented and evaluated within credit scoring algorithms to maintain the measured fairness-trust relationships when deployed across different cultural and regulatory contexts in developing economies.

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

  The study reconstructed concentration profiles for inclined suspensions using TARG (time-averaged radiography) measurements, but the framework has not been extended to predict behavior under dynamic conditions such as flow reversal, oscillatory motion, or time-varying inclination angles relevant to drilling operations.

• AI in Cybersecurity: A Systematic Review and Conceptual Audit Model (2026) · doi

  The firewall rule management example demonstrates limitations of binary checks in identifying overly permissive rules and lateral movement risks, but the paper does not specify what AI techniques (machine learning classification, anomaly detection, knowledge graphs) should be applied to automate detection of these context-dependent vulnerabilities at scale.

• Large Language Models for Combinatorial Optimization: A Systematic Review (2026) · doi

  The hybrid integration of Linear Programming dependency graphs with LLMs for multi-robot task planning (reference [157]) has only been demonstrated for specific robotics scenarios; generalization to heterogeneous robot teams with dynamic constraints and real-time replanning needs evaluation.

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

  Cross-type and cross-power level fault diagnosis (Table 5) showed significantly degraded HDAL performance (88.642% accuracy with 1.983% standard deviation) compared to same-type transfer (91.067%), yet the paper does not analyze which fault classes or reactor parameter combinations are most challenging across different reactor types and power levels. Specific investigation into how reactor type differences and variable power levels affect feature transferability in the HDAL adversarial domain adaptation framework is needed.

• Ocean: Object-aware Anchor-free Tracking with Matching-relation Learning (2026) · doi

  Ocean++ shows adaptability to first-person perspective tracking on the TREK dataset, but the specific challenges of egocentric viewing angles and potential limitations of the matching-relation learning mechanism under varied first-person camera motion and occlusions are not analyzed.

• Automated design of heuristics for resource-constrained project scheduling problem via regression algorithms (2026) · doi

  The paper demonstrates that single regression-designed priority rules require tens of times fewer schedule computations than AllPR on large projects, but does not investigate whether this computational savings generalizes to projects with atypical resource constraint characteristics (e.g., renewable vs. non-renewable resources, multiple constraint types). The efficiency advantage should be validated across heterogeneous constraint configurations in resource-constrained project scheduling.

• Securing Fog-assisted IoT: An Adaptable and Efficient Threat Identification Approach (2026) · doi

  While three use-cases (smart healthcare, Industrial IoT, autonomous vehicles) are mentioned conceptually, the DEL framework has not been implemented or validated in these specific real-world fog-IoT environments. Practical deployment validation with actual patient data flows, production system traffic patterns, and vehicle sensor data streams is necessary.

• A Robust Hybrid Deep Learning Model for Multiclass Depression Classification from Speech Audio (2026) · doi

  Inference latency, computational cost, and real-time deployment feasibility were not empirically evaluated for the hybrid deep learning models; future research must benchmark these metrics to assess practical deployment scenarios for audio-based multiclass depression screening systems.

• On the interface between linguistics, computer science and psychiatry: analyzing textual key-factors affecting BERT-based classification of schizophrenia in social media texts (2026) · doi

  The generalizability of BERT-based schizophrenia classification to populations with varying literacy levels, cultural discourse norms, or non-English languages has not been tested. Cross-linguistic validation and evaluation across diverse demographic populations with different mental-health vocabulary conventions are needed to assess whether morphological and structural markers remain invariant across linguistic and cultural contexts.

• Artificial Intelligence (AI) Based Multi-Layered Approaches for Privacy Preservation in Federated Learning (2026) · doi

  The hybrid privacy-preserving federated learning framework was evaluated exclusively on classification tasks with structured and imaging data (MIMIC-III and COVID-19 datasets); performance characteristics remain unexplored for reinforcement learning and natural language processing paradigms within the federated learning context.

• GaussianSeal: Rooting Adaptive Watermarks for 3D Gaussian Generation Model (2026) · doi

  GaussianSeal is evaluated only on the LGM 3D generative model. The transferability and adaptability of the lightweight bit modulation design to other 3D generative architectures (e.g., NeRF-based models, mesh generation models, or alternative diffusion-based 3D generators) has not been demonstrated or discussed.

• DAFRL: a dynamic adaptive mean field game-based multi-agent cooperative decision-making method (2026) · doi

  The DAFRL algorithm has not been extended to three-dimensional space with altitude coordination for UAV swarm operations. Current validation is limited to 2D interception scenarios, and the integration of altitude dimension and complex airflow disturbance models into the dynamic adaptive mean field game framework requires investigation.

• Lightweight and Explainable Neural Models for Multilingual Movie Script Certification (2026) · doi

  The lightweight neural models for multilingual movie script certification were developed and evaluated on a specific dataset, but their generalization to movie certification systems in legal and media compliance domains beyond entertainment remains unexplored. The paper mentions applicability to 'other high-stakes multilingual domains such as legal and media compliance' but does not provide empirical validation or adaptation strategies for these distinct regulatory contexts.

• Scour depth prediction using machine learning and explainable AI: assessment of bridge vulnerability (2026) · doi

  ANN-GA achieved the highest NNSE value (0.9468) among neural network models, demonstrating effectiveness of genetic algorithm-based optimization; however, the paper does not compare computational training time, convergence speed, or resource requirements between ANN-GA, ANN-PSO, and ensemble methods, which is critical for deploying real-time scour prediction systems.

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

  Zero-shot object detection models show promise for addressing limitations of existing deep learning models in camera trap-based animal studies, but their application in camera trap-based insect behavior analysis and video compression workflows has not been evaluated. Future research should specifically test zero-shot object detection models for identifying previously undocumented insect species and behaviors in compressed camera trap videos.

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

  Relighting and material editing techniques for 3D Gaussian Splatting remain underdeveloped, preventing realistic compositing of reconstructed human-centric subjects into large-scale or virtual environments, and blocking downstream applications requiring photorealistic material and lighting manipulation in volumetric videos.

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

  Comparative analysis shows RMLAA computation time improvements (5.93-6.96 seconds for IEEE test systems) but lacks evaluation on real-time reconfiguration requirements, communication delays in remote terminal units, and the minimum reconfiguration interval needed between successive switch operations in practical distribution systems with dynamic EV loads.

• Sensor Data Fusion in Healthcare Monitoring System with Appropriate Rule-based Model for Error Reduction (2026) · doi

  The study reports user satisfaction scores (8-9 out of 10) for the integrated monitoring system but does not conduct systematic usability testing or investigate which specific system features (cost, accuracy, real-time processing, interface design) drive user preference for the fused sensor approach over individual sensors.

• Uncertainty Assessment in Deep Learning-based Plant Trait Retrievals from Hyperspectral data (2026) · doi

  The spectral saturation problem affecting high LAI value predictions represents a fundamental data-intrinsic limitation that cannot be overcome by distance-based uncertainty estimation alone. More sophisticated sensing strategies and multi-modal approaches beyond purely optical methods are needed to address this physics-based canopy reflectance constraint in hyperspectral plant trait retrieval systems.

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

  User session authentication does not persist credentials to the SQLite database, reducing multi-session continuity in the Flask-based flight delay prediction system. Database schema and authentication middleware must be redesigned to support stateful user sessions and historical prediction tracking.

• International Journal of Intelligent Data and Machine Learning (2026) · doi

  The paper applies probability adjustment techniques to two automotive use cases (SUV purchase and brake service response), but does not explore whether the adjustment formula remains valid across different industry verticals or product categories with fundamentally different response rate distributions and imbalance ratios beyond the 10% and 40% examples provided.

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

  Interoperability between the proposed lightweight blockchain system and heterogeneous IoT devices using different communication protocols (Zigbee, LoRaWAN, NB-IoT) was mentioned as future work but not addressed in the current model. Integration mechanisms and protocol translation requirements for multi-protocol IoT networks remain undefined.

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

  No evaluation of the Tamil text-to-speech module's accuracy, intelligibility, or performance in high-noise fish market environments is provided; the effectiveness of this accessibility feature for non-technical users requires field testing and user satisfaction metrics.

• Artificial Intelligence and Multi-Omics for Anticancer Drug Development and Repurposing (2026) · doi

  Generative models for anticancer drug discovery have not been thoroughly integrated into standard laboratory workflows for practical application. The specific computational architectures (GANs, generative models) required to augment sparse multi-omics datasets and improve drug repositioning predictions need empirical validation in clinical oncology settings.