Methodology gaps in Computer Science

Representative open questions

Showing 30 of 651 — one per source paper, highest-quality first.

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

  The federated learning approach using the ring topology outperforms centralized training on the ISIC dataset, but the effect of different topologies (e.g., FedAvg, FedProx) is not fully explored.

• Causal K-means clustering (2026) · doi

  The causal K-means clustering method requires separate independent samples for constructing the nuisance parameter estimator μ̂ to satisfy Assumption A2; the paper does not explore whether the sample-splitting requirement can be relaxed or how performance degrades when a single sample is used for both clustering and nuisance estimation.

• 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 wake observation region Ω2 for the objective function is fixed at x ∈ [1, 4], y ∈ [0, 2]; the sensitivity of model performance to the choice of observation domain size, location, and exclusion of near-wall regions across varying Reynolds numbers has not been systematically analyzed.

• AN ITERATIVE GLMM–XGBOOST ALGORITHM WITH GROUP-AWARE CONDITIONAL PERMUTATION IMPORTANCE FOR EXPLAINING MULTILEVEL ITEM RESPONSE DATA (2026) · doi

  The conditional GLMM–XGBoost advantage over conditional GLMM is described as 'comparatively modest' with empirical Bayes predictions incorporated in both, but the paper does not isolate or quantify the specific contribution of the XGBoost non-parametric component versus random-effects conditioning to this improvement.

• Cross-Currency Basis Swaps Referencing Backward-Looking Rates (2026) · doi

  The pricing framework explicitly handles multi-period CCBS with tenor structure T_n, but numerical implementation details for computing the nested expectations in terms I₂ₜ, I₃ₜ, and I₄ₜ (which involve products of multiple Gamma functions with correlated parameters) are not provided. Computational complexity and convergence criteria for Monte Carlo or tree-based approximations remain unaddressed.

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

  Knowledge Distillation (KD) as a compression strategy appears in only one surveyed study despite its strong potential for FPGA-constrained Earth observation deployment. The synergy between KD-guided weight pruning and hardware-aware neural network design for maximizing resource utilization on edge FPGA platforms remains largely unexplored in remote sensing applications.

• Securing IoT Devices with PUFs: Mitigating Aging and Tampering through Cryptography and Machine Learning (2026) · doi

  The linear complexity test requires M to be chosen between 500 and 5000 for the limiting distribution to provide reasonable approximation, but the paper does not specify how to select the optimal M value within this range for PUF-based cryptographic key generation in IoT devices with varying hardware constraints.

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

  The confusion matrices show that XGBoost commits errors between neighboring vulnerability classes (denial_of_service and arithmetic) more frequently than CatBoost and Random Forest, but the feature space geometry and decision boundary characteristics causing these misclassifications in smart contract vulnerability detection have not been examined. Analyzing the geometric properties of feature representations for confused vulnerability pairs would clarify this phenomenon.

• Can deep learning-based segmentation and classification improve the detection of renal cortical abnormalities? (2026) · doi

  The paper does not investigate how CLAHE preprocessing parameters (clip limit, tile grid size) were optimized or whether these parameters are transferable across different DMSA imaging equipment and acquisition protocols. Future work should systematically evaluate CLAHE preprocessing robustness for DenseNet205 classification across DMSA systems with varying imaging parameters and reconstruction algorithms.

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

  The distinction between sparse edges present in all nine subgraphs (core network unaffected by covariates) versus covariate-influenced edges is determined by the tuning parameters γ1 and γ2 (set at 5×10⁻⁵ and 5×10⁻⁶), but systematic guidelines for selecting these penalty parameters in CVN estimation across different biological datasets and covariate structures are not provided.

• Umjetna inteligencija: od kritičkih promišljanja do njezine primjene u kaznenom pravosuđu (2026) · doi

  The Italian Delia algorithm processes over 1.5 million variables combining geographic crime data with witness interviews and CCTV footage, but no comparative analysis exists documenting which specific variables contribute most to prediction accuracy or how the system performs across different geographic regions and crime categories.

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

  The gamma-ray attenuation measurements demonstrate localized relative errors during the passage of the settling front where concentration–time curves exhibit steep gradients; methods to reduce these transient region discrepancies in spatiotemporal concentration profiles using advanced signal processing or improved ANN architectures (e.g., physics-informed neural networks) have not been evaluated.

• Large language model based machine translation for universal multilingual understanding and translation quality enhancement (2026) · doi

  The paper identifies that smaller specialized models like ALMA-13B-LoRA are more effective for low-resource and exotic languages such as Icelandic, but does not provide systematic investigation of which architectural features or fine-tuning strategies enable this superiority for low-resource language translation in multilingual LLMs.

• Leveraging machine learning to enhance aerosol classification using Single-Particle Mass Spectrometry (2026) · doi

  The supervised stacked autoencoder achieved 91.1% overall accuracy, but performance degradation on chemically complex classes (feldspar cSA, feldspar cSOA, cellulose) with high reconstruction errors has not been systematically addressed. Architectural modifications to the autoencoder that explicitly handle compositionally heterogeneous spectra with elevated SSE values require investigation.

• Quantum Information Framework for Neural Network Generalization: A Comprehensive Experimental Analysis (2026) · doi

  Weight entropy is computed using histogram binning with a fixed bin count of 50; the sensitivity of classical weight entropy calculations to bin selection and alternative entropy estimators (e.g., kernel density estimation) in the context of quantum information framework validation has not been addressed.

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

  The model proposes that AI-enhanced auditing should incorporate human judgment into the multi-layered audit process, but does not specify the decision points where human expertise is required, the criteria for human override of AI recommendations, or how to validate the quality of human-AI collaborative decisions in cybersecurity auditing.

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

  The integration of Large Language Models with automated planning and scheduling (APS) systems requires investigation of how LLMs can effectively contribute to constraint specification, plan generation, and solution validation within APS frameworks, as noted in reference [166] on prospects of incorporating LLMs in APS.

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

  While HDAL combines DANN's adversarial strategy with DSAN's LMMD subdomain alignment for fine-grained fault feature alignment, the paper does not investigate whether additional domain alignment objectives (e.g., class-aware alignment, partial domain adaptation) could further improve performance on the difficult-to-distinguish Conditions 12 and 13 in cross-reactor adversarial transfer learning.

• Machine-learning-based reconstruction of Ming-dynasty defensive corridors in Yuxian (2026) · doi

  The comprehensive cost surface construction weights multiple single-factor cost functions through weighted summation, but the paper does not specify the weighting scheme methodology, sensitivity analysis of weight values, or whether different weighting strategies (inverse distance weighting, analytical hierarchy process) produce significantly different corridor network predictions.

• Comparative analysis of deep learning algorithms for rolling element bearing fault classification under variable loads and speeds (2026) · doi

  The paper observed an abrupt loss climb between epochs 8-12 during CNN training (loss reaching 20-25 with accuracy dropping to 0.2-0.3), followed by stabilization, with this behavior varying significantly across different fold splits. Future investigation should characterize the causes of this non-monotonic training behavior in bearing fault classification networks and develop regularization or optimization strategies to prevent or mitigate these training instabilities.

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

  The repeated validation analysis (Fig. 11b) examines LLM stability across multiple checks per prompt for silent bug classification, but the paper does not specify how many repeated checks are empirically necessary to reach stable predictions or how performance degrades with incomplete API documentation across different deep learning libraries. Determining optimal repetition thresholds for reliable silent bug confirmation is needed.

• Quantum-SpinalNet: a hybrid deep learning approach for mammographic breast cancer detection (2026) · doi

  While the paper claims quantum-inspired entanglement and superposition principles in the DQNN improve feature interaction modeling, there is no comparative analysis of the computational complexity, inference time, or quantum advantage of the DQNN classifier versus classical deep neural network classifiers on the same mammographic segmentation task.

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

  The comparison with unsupervised learning-based visual trackers on TrackingNet shows inferior performance, but no investigation is provided into whether the matching-relation learning approach can be extended with unsupervised or self-supervised components to reduce annotation requirements while maintaining anchor-free tracking accuracy.

• From unstructured text to structured reasoning: a hybrid knowledge graph for Indonesian sentencing analysis (2026) · doi

  Evidence entity extraction achieves 87.1-89.4% F1-score with acknowledged boundary ambiguity between items (e.g., surveillance video vs. video evidence); the paper does not propose or test methods to resolve these boundaries through hierarchical evidence ontologies or context-dependent entity linking in the knowledge graph.

• Neural Network Tools in the Arsenal of a University Teacher (2026) · doi

  The paper identifies that AI-based tools perform worse than traditional methods at precision in identifying relevant articles but better at finding unique publications, yet provides no empirical framework for measuring or optimizing the trade-off between relevance accuracy and uniqueness discovery in hybrid AI-traditional search methodologies for academic literature.

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

  The paper lacks detailed analysis of which specific attack patterns (by attack class, payload size, temporal characteristics) are missed by the DEL models, contributing to false negatives. Root cause analysis of detection failures and targeted improvements to CNN-LSTM-GRU architectures for missed threat categories is needed.

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

  Explainable AI techniques such as SHAP or LIME have not been integrated to enhance model interpretability; future work prioritizes XAI implementation to provide clinically actionable feature importance explanations for multiclass depression severity predictions from speech audio.

• 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

  Morphological and phonotactic errors are hypothesized as critical grammatical markers of schizophrenia based on procedural memory impairment, but the paper does not systematically extract or weight these morphological features in the BERT classification pipeline. A dedicated feature engineering approach should quantify and preserve morphological error patterns while filtering semantic mental-health vocabulary.

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

  The 2.5x computational overhead of the hybrid federated learning approach compared to standard federated learning limits deployment feasibility; systematic evaluation of privacy-accuracy-efficiency tradeoffs across varying privacy budgets (ε values) and computational constraints has not been conducted.

• Predicting Employee Attrition: A Machine Learning Approach in Human Resource Analytics (2026) · doi

  The paper identifies that managerial relationships (Years with Current Manager) contribute significantly to attrition predictions, but does not investigate whether manager-specific characteristics (leadership style, retention rate of their team, tenure in role) would improve predictive performance beyond tenure-based features in the employee attrition model.