Validation gaps in Computer Science

Representative open questions

Showing 30 of 425 — one per source paper, highest-quality first.

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

  The model's performance on the ISIC dataset is evaluated using various metrics (accuracy, AUC, F1-score), but the robustness of these metrics to different conditions (e.g., varying image quality) is not assessed.

• Hybrid Deep Model for Pain Intensity Classification Using Fused ECG, EMG, and GSR Signals (2026) · doi

  The study evaluates pain intensity classification using only 5-fold cross-validation on a single proprietary dataset without reporting dataset size, participant demographics, pain induction methods, or signal sampling rates. External validation on publicly available pain-related physiological signal datasets (e.g., BioVid, UNBC-McMaster) is necessary to assess generalization of the hybrid model across different pain assessment protocols and populations.

• 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 Reynolds-force model was trained and validated exclusively on cylinder flow data at Re ≈ 300−300,000 with the near-wall region remaining laminar; the generalization of the trained neural network closure to separated flows around bluff bodies with different geometries or highly turbulent near-wall regions has not been demonstrated.

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

  Coarse-Grained Reconfigurable Arrays (CGRAs) with hardened arithmetic units and systolic array PE configurations remain underexplored for FPGA-enabled ML in Earth observation. A detailed comparative analysis between CGRA-based implementations (e.g., AMD Versal AI Engines) and traditional FPGA-only solutions for highly quantized semantic segmentation models on remote sensing datasets is needed to establish performance benchmarks.

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

  The paper specifies class probabilities for the linear complexity test (π0=0.010417 through π6=0.020833) that differ substantially from normal approximation, but does not evaluate whether these discrete probability distributions remain valid when applied to PUF entropy sources degraded by device aging or physical tampering.

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

  The SmartBugs-Wild dataset evaluation revealed highly skewed cluster distributions (35,499 contracts in Cluster 1 vs. 149 in Cluster 2) with only 34.15% variance explained by the first two principal components, yet the impact of this structural heterogeneity on model generalization across clusters has not been evaluated. Cross-cluster validation performance between models trained on different structural archetypes of smart contracts should be investigated.

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

  The paper identifies that substantial categorization difficulties arise when multiple regions of scarring are present within a single kidney, but does not provide specific analysis of model performance stratified by scar multiplicity or location. Future work should evaluate DenseNet205 and DenseNet121_Self-ONN_FPN performance on kidneys with single versus multiple scarred regions to determine if architectural modifications are needed for complex multi-scar cases.

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

  The paper identifies that TP53 exhibits unclear connection patterns with several genes (ATG4A, RAD9B, STX6, TRIM13, XRCC4, CES2, SESN2, FBXO22, XRCC6, GRB2, PRKAA1, TAF3, NOX4) across cancer groups and radiation doses, but provides no mechanistic validation of these radiation dose-dependent network rewiring patterns through experimental confirmation or functional genomics approaches.

• COGNITIVE INFRASTRUCTURE AND THE RECURSIVE TRANSFORMATION OF KNOWLEDGE COMMUNICATION: GENERATIVE AI IN SCIENTIFIC PUBLISHING (2026) · doi

  The paper argues that certification mechanisms are exposed as inadequate when generative systems enter writing and evaluation, triggering an arms-race dynamic where detection tools stimulate further innovation in generative capability. However, it does not characterize the specific failure modes of current integrity detection methods against evolving AI text generation techniques or provide benchmarks for measuring detection evasion rates across different manuscript types and disciplinary norms.

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

  The study validates the algorithmic fairness and explainability framework using structural equation modeling (SEM) with composite reliability (CR = 0.84-0.90) and average variance extracted (AVE = 0.57-0.64), but does not report performance metrics on actual credit scoring datasets from developing economies to demonstrate bias reduction in real-world FinTech lending systems.

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

  The Dutch risk assessment algorithms (Top 600, Top 400, and Project Sensing) for predicting serious crime and repeat offenses lack documented validation studies comparing algorithmic predictions against actual crime outcomes; the accuracy rates and false positive/negative ratios of these profiling systems remain unpublished in peer-reviewed literature.

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

  The ANN model was trained and tested exclusively within a single experimental domain (glycerin–water 92% v/v, glass microspheres 212–800 µm, 20% v/v solids), meaning high accuracy reflects interpolation rather than extrapolation; generalization to other rheologies, particle morphologies, volumetric concentrations, or field-scale conditions in inclined suspension systems remains untested.

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

  Hallucination in LLM-based machine translation is noted as a significant limitation especially in low-resource language translation and domain-specific language contexts, but the paper does not characterize the frequency, types, or severity of hallucinations across different language pairs or provide methods to detect and mitigate semantic hallucinations in machine translation outputs.

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

  While the dataset contains 18,827 labeled spectra across 20 aerosol types, generalization performance of the machine learning framework on SPMS measurements from different atmospheric environments, seasons, or geographic locations has not been evaluated. Cross-dataset validation is needed to assess whether the supervised and semi-supervised models maintain classification fidelity for soot and feldspars across diverse real-world deployment scenarios.

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

  The quantum information framework is evaluated exclusively on synthetic datasets (spiral data and modular arithmetic operations); the generalization behavior of von Neumann entropy, purity, and effective rank metrics has not been validated on real-world datasets with natural data distributions, limiting claims about the framework's applicability to practical neural network training scenarios.

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

  The model claims to balance AI-driven technology adoption with adequate cybersecurity safeguards, yet provides no validation dataset, benchmark, or test cases demonstrating how organizations with different maturity levels, infrastructure complexity, or resource constraints should implement the Anti-Sheriff framework operationally.

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

  The auto-formulation of optimization problems from natural language descriptions (references [156], [179], [180]) requires further evaluation on problem classes beyond those covered in the NL4Opt Competition, particularly for complex multi-objective and large-scale combinatorial optimization problems with domain-specific constraints.

• Explainable machine learning for tracking spatial variation in leaf chlorophyll fluorescence within temperate deciduous forest canopies (2026) · doi

  The Random Forest and XGBoost models for predicting chlorophyll fluorescence parameters were trained exclusively on temperate deciduous forest datasets. These models require retraining and validation across diverse ecosystems (croplands, grasslands, wetlands) to assess transferability and determine whether spectral reflectance-ChlF relationships remain consistent across different plant functional types and canopy architectures.

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

  The robustness analysis used purely additive white Gaussian noise at 1 dB, 3 dB, and 5 dB SNR levels, but this does not represent the full range of mechanically-induced disturbances in industrial bearing systems such as load torque ripple, rotational speed fluctuation, structural resonance, or shaft misalignment. Future work should evaluate deep learning models for rolling element bearing fault classification under these realistic non-stationary mechanical phenomena with frequency drift, harmonic amplification, and impulsive components.

• The adoption of artificial intelligence methods in entrepreneurship research: current state and pathways forward (2026) · doi

  The cascading term substitution problem in VOSviewer—where excluding multi-word terms causes shorter term variants to capture occurrences of excluded terms (e.g., 'artificial neural network' → 'neural network' → 'network')—is identified but lacks systematic evaluation of how this substitution cascade distorts co-occurrence networks and clustering in entrepreneurship AI literature reviews.

• A two-stage deep learning model for risk identification in green supply chain finance (2026) · doi

  The GAN-SAE synthetic data augmentation component is applied to the training set, but the paper does not report how synthetic data quality or distribution fidelity affects model robustness when applied to out-of-sample industries in the 2021-2024 external validation; ablation studies isolating GAN contribution versus SAE feature extraction are needed for green supply chain finance applications.

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

  TransFuzz's oracle validation achieves 71.42% precision due to three specific failure modes: (1) oracle design errors causing logical flaws in bug classification, (2) code migration failures that fail to preserve bug characteristics across different PyTorch APIs, and (3) insufficient API documentation leading to LLM misjudgment. These sources of false positives (28.58% rate) require targeted improvements in oracle construction and API information enrichment for silent bug detection in deep learning libraries.

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

  The Q-SpinalNet architecture demonstrated strong performance on mammographic datasets, but the paper does not evaluate the model's generalization capability across different mammography imaging protocols, scanner manufacturers, or diverse patient demographics. Testing the quantum-inspired hybrid framework on multi-institutional mammography datasets with varying acquisition parameters would establish clinical robustness.

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

  While Ocean++ is adapted to pixel-level tracking on VOT-2020 by grafting a segmentation network branch, the integration strategy and potential conflicts between the matching-relation learning mechanism and pixel-level segmentation requirements are not discussed or evaluated.

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

  Regression-based heuristics achieved comparable or superior performance to the genetic algorithm (DV) with significantly reduced computational burden, but the paper does not establish whether this efficiency advantage holds across different project network structures or constraint types. Comparative analysis between regression-based heuristics and GA-based approaches (DV_1000, DV_5000, DV_50000) should be extended to include diverse resource constraint profiles and project topology variations.

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

  The relevance filtering stage uses SMOTE oversampling to achieve 100% recall and 99.30% precision, but the paper does not evaluate whether this extreme precision-recall trade-off introduces cascading errors in downstream entity extraction when applied to court decisions with novel or unusual sentence structures not represented in the training set.

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

  The paper states that AI tool effectiveness depends on user competency but does not empirically investigate or measure how different levels of user expertise in AI-assisted literature search (query formulation, result validation, critical evaluation) affect the quality outcomes when using neural network-based research tools in academic settings.

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

  The DEL framework with CNN-LSTM-GRU base models has not been evaluated against adversarial attacks specifically designed to fool deep ensemble learning systems in fog-IoT environments. Future work must investigate the robustness of the DEL threat identification approach against adversarial examples and evasion techniques in dynamic fog computing scenarios.

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

  Speaker-independent partitioning could not be strictly enforced due to absence of speaker identity annotations in the depression speech dataset, potentially introducing optimistic bias in multiclass depression classification performance estimates and limiting generalizability across different speakers.

• 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 interaction between text length, discourse genre, and schizophrenia linguistic markers appears additive rather than interactive, but this relationship has not been formally tested across controlled genre conditions with minimum-length thresholds. Systematic manipulation of both text length and topic/genre type is needed to establish genre-specific minimum-length requirements for reliable BERT-based classification.