Vol. 6 No. 2 (2026): July−December 2026
Articles

Data-Centric Artificial Intelligence across Forecasting, Anomaly Detection, and Multimodal Systems: A Structured Review of Representation, Evaluation, and Deployment

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  • Noah Collins
Noah Collins
Department of Mathematics, Grand Valley State University, Allendale, MI, USA

Published 2026-07-13

Keywords

  • data-centric AI,
  • forecasting,
  • anomaly detection,
  • multimodal learning,
  • feature engineering,
  • deployment efficiency
    • ...More
    Less

How to Cite

Collins, N. (2026). Data-Centric Artificial Intelligence across Forecasting, Anomaly Detection, and Multimodal Systems: A Structured Review of Representation, Evaluation, and Deployment. Spectrum of Research, 6(2). Retrieved from https://spectrumofresearch.com/index.php/sr/article/view/56

Copyright (c) 2026 Spectrum of Research

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Comparative model studies often emphasize learner choice even though target definition, data quality, representation, validation design, and compute constraints determine what a benchmark result means. This structured narrative review organizes a deduplicated, client-supplied bibliography across forecasting and temporal modeling, anomaly and event detection, multimodal perception, feature and representation learning, optimization, retrieval, and efficient deployment. Title-level thematic coding is used to construct a traceable evidence map while avoiding claims about unreported effect sizes. Across domains, the synthesis highlights horizon-aware evaluation, prevalence-sensitive metrics, corruption and missing-modality tests, leakage-resistant splits, transparent baseline ladders, and latency-resource reporting. The review concludes that data and evaluation design should be treated as experimental factors rather than background conditions.

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References

  1. [1] Tabassi, E. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0) (NIST AI 100-1). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.AI.100-1
  2. [2] Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., ... Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
  3. [3] Bai, Y. (2025, September). Deep Learning-based Action Recognition for Temporal Analysis and Intervention Effectiveness Assessment in Autism Spectrum Disorder Children's Video Therapy. In Proceedings of the 2025 International Symposium on Artificial Intelligence and Computational Social Sciences (pp. 307-314).
  4. [4] Cao, H., & Long, L. (2026). Empirical Evaluation of Multi-Source Monitoring Signal Effectiveness and Lead Time for Performance Degradation Prediction in Kubernetes-Based Microservices. Journal of Advanced Computing Systems, 6(4), 15-26.
  5. [5] Chung, P. T. (2025, December). Data Mining Methods for Biomechanical Property Prediction of Biomedical Materials Based on Optimized Feature Dimensionality Reduction. In Proceedings of the 2025 6th International Conference on Computer Science and Management Technology (pp. 174-180).
  6. [6] Ge, L., & Rao, G. (2025). MultiStream-FinBERT: A Hybrid Deep Learning Framework for Corporate Financial Distress Prediction Integrating Accounting Metrics, Market Signals, and Textual Disclosures. Pinnacle Academic Press Proceedings Series, 3, 107-122.
  7. [7] Huang, Y. (2025, August). Deep learning-enhanced dynamic margin period of risk prediction for counterparty credit risk management: A multi-modal approach integrating market sentiment analysis and real-time exposure assessment. In Proceedings of the 2nd International Conference on Intelligent Computing and Data Analysis (pp. 328-335).
  8. [8] Lei, Y. (2026). StatFuse: Bridging Statistical Inference and Neural Prediction for Interpretable Forecasting. Journal of Science, Innovation & Social Impact, 2(1), 205-216.
  9. [9] Li, J., Liu, M., & Li, M. (2026). Comparative Evaluation of Ensemble Learning Algorithms for Visitor Engagement Prediction and Content Recommendation Optimization in Virtual Museum Environments. Journal of Advanced Computing Systems, 6(2), 64-74.
  10. [10] Liu, X. (2026). Covariate Feature Importance and Cross-Indication Transferability Analysis for Phase III Oncology Trial Outcome Prediction. Journal of Science, Innovation & Social Impact, 2(2), 117-130.
  11. [11] Peng, Y. and Gao, S., 2026. An Empirical Comparison of XGBoost and LightGBM for Capital Expenditure Deviation Prediction in US FERC-Regulated Rate-Base Transmission Projects. Journal of Sustainability, Policy, and Practice, 2(4), pp.90-102.
  12. [12] Rao, G., Trinh, T. K., Chen, Y., Shu, M., & Zheng, S. (2024). Jump Prediction in Systemically Important Financial Institutions' CDS Prices. Spectrum of Research, 4(2).
  13. [13] Shang, W., Xu, W., & Zhou, Y. (2026). Feature Weight Optimization in Machine Learning Classifiers for Conflict Escalation Early Warning: Evidence from Diplomatic Signals and News Text. Journal of Sustainability, Policy, and Practice, 2(3), 26-37.
  14. [14] Shi, W., & Cheng, Z. (2024). Enhanced Adaptive Threshold Algorithms for Real-Time Cardiovascular Risk Prediction from Wearable HRV Data. Journal of Advanced Computing Systems, 4(1), 46-57.
  15. [15] Shi, X. (2026). Fairness-Aware Multimodal Fusion for Early Chronic Disease Risk Prediction: A Temporal Deep Learning Approach. Journal of Science, Innovation & Social Impact, 2(1), 217-231.
  16. [16] Tian, Y. (2026). Gradient Boosting-Based Demand Variability Estimation for Improved Safety Stock Calculation in Multi-Echelon Aerospace Spare Parts Inventory. Journal of Science, Innovation & Social Impact, 2(2), 175-186.
  17. [17] Wang, Y. (2026). A Comparative Study of Forecasting Techniques for Reducing Food Waste in Retail Operations. Journal of Science, Innovation & Social Impact, 2(1), 18-30.
  18. [18] Wang, Y. (2026). Accuracy Evaluation of Machine Learning-Based Hospital Resource Demand Forecasting During Infectious Disease Surges: A Comparative Analysis. Journal of Science, Innovation & Social Impact, 2(1), 314-327.
  19. [19] Wang, Z. (2025). Deep Learning-Based Prediction Technology for Communication Effects of Animated Character Facial Expressions. Journal of Sustainability, Policy, and Practice, 1(4), 105-116.
  20. [20] Wang, J. (2025). Multi-Source Data Fusion for Short-Term Demand Forecasting of Seasonal Retail Products: An Empirical Study Using Weather and Social Media Signals. Journal of Science, Innovation & Social Impact, 1(1), 340-349.
  21. [21] Wang, Y. (2025, December). Practical AI Approaches for Community Infection Early Warning: From Public Data to Actionable Insights. In Proceedings of the 2025 6th International Conference on Computer Science and Management Technology (pp. 1545-1552).
  22. [22] Wen, S., & Tang, T. (2025). A Comparative Evaluation of URL-Sharing, Content Similarity, and Temporal Synchronicity Signals for Detecting Coordinated Inauthentic Behavior in Multilingual Political Discourse. Journal of Global Engineering Review, 3(2), 69-78.
  23. [23] Wu, C., Guan, H., & Weng, H. (2024). Forecasting hospital resource demand using gradient boosting: An operational analytics approach for bed allocation and patient flow management. Journal of Computing Innovations and Applications, 2(1), 74-85.
  24. [24] Zhang, Y., & Li, M. (2026). Accuracy Evaluation of Multi-Factor Forecasting Methods for Customer Service Workload Prediction Under Holiday and Promotional Fluctuations: Evidence from US Service Industry Data. Journal of Advanced Computing Systems, 6(5), 12-20.
  25. [25] Zhang, F., Ye, H., & Wei, C. (2024). Leveraging Multi-Modal Attention Mechanisms for Interpretable Biomarker Discovery and Early Disease Prediction. Journal of Computing Innovations and Applications, 2(2), 111-121.
  26. [26] Zhang, D., & Zheng, Q. (2025). Machine Learning-Based Building Energy Consumption Prediction and Carbon Reduction Potential Assessment in US Metropolitan Areas. Journal of Industrial Engineering and Applied Science, 3(5), 27-40.
  27. [27] Zhang, C., & Xiao, P. (2026). Optimizing Breast Cancer Recurrence Time Prediction with Attention-Enhanced LSTM Networks. Journal of Advanced Computing Systems, 6(1), 80-98.
  28. [28] Zhao, F., & Tang, T. (2026). AI-Based Sentiment Analysis for Stock Market Prediction: A Systematic Literature Review. Journal of Sustainability, Policy, and Practice, 2(3), 115-124.
  29. [29] Zhao, F., Zhang, M., Zhou, S., & Lou, Q. (2024). Application of deep reinforcement learning for cryptocurrency market trend forecasting and risk management.
  30. [30] Bai, Y. (2026). Context-Aware Classification of Verbal Operants in Children with ASD Using Deep Learning. Journal of Science, Innovation & Social Impact, 2(1), 232-243.
  31. [31] Cao, H., Hu, J., & Luo, C. (2025). Behavioural Feature Analysis for Anomalous Click Detection in Mobile Advertising Environments: Toward In-App Browser-Specific Detection. Journal of Computing Innovations and Applications, 3(2), 96-105.
  32. [32] Cao, H., & Shi, W. (2026). Statistical Anomaly Detection Approach for Field Mapping Validation in Enterprise Payroll Data Migration. Journal of Computing Innovations and Applications, 4(1), 137-153.
  33. [33] Chen, Y., & Lai, J. (2026). Multi-Metric Trustworthiness Evaluation of AI-Assisted Medical Imaging Diagnosis: Integrating Confidence Calibration and Distribution Shift Detection. Journal of Global Engineering Review, 4(1), 113-126.
  34. [34] Chung, P. T. (2025). Attention-Enhanced YOLO for Real-Time Defect Detection in 3D-Printed Dental Prostheses. Journal of Science, Innovation & Social Impact, 1(2), 119-134.
  35. [35] Chung, P. T. (2026). Comparative Evaluation of Machine Learning Algorithms for Spectrophotometric Dental Shade Classification. Journal of Sustainability, Policy, and Practice, 2(1), 204-214.
  36. [36] Guo, Y., & Wei, C. (2026). Latency-Adaptive Feature Fusion Weight Allocation Under Bandwidth Constraints for V2X Cooperative 3D Object Detection. Journal of Advanced Computing Systems, 6(3), 22-31.
  37. [37] Guo, Y. (2025). Performance Evaluation of Lightweight Detection Algorithms on Compact LiDAR-Camera Configurations for Freight Transportation. Journal of Science, Innovation & Social Impact, 1(1), 398-409.
  38. [38] Han, J. (2025, October). Multi-source Text Mining for Risk Signal Detection in Asset-Backed Securities Market: An NLP-driven Data Analytics Approach. In Proceedings of the 2025 International Symposium on Machine Learning and Social Computing (pp. 497-506).
  39. [39] Han, M., & Lai, J. (2026). Temporal Feature Engineering and Threshold Optimization for Early Warning in Healthcare Claims Anomaly Detection. Journal of Advanced Computing Systems, 6(4), 27-49.
  40. [40] Huang, Y. (2025). Enhanced Feature Engineering and Algorithm Optimization for Real-Time Detection of Synthetic Identity Fraud and Money Laundering in Financial Transactions. Journal of Science, Innovation & Social Impact, 1(1), 384-397.
  41. [41] Jia, R., Lu, X., & Whitmore, S. (2024). Feature-Based Detection of Bot Traffic and Click Fraud in Mobile Advertising: A Comparative Analysis. Journal of Computing Innovations and Applications, 2(1), 140-152.
  42. [42] Lei, Y., & Wu, Z. (2025). A Real-Time Detection Framework for High-Risk Content on Short Video Platforms Based on Heterogeneous Feature Fusion. 2025 International Forum on Computing and Intelligent Systems, 93-106.
  43. [43] Li, J. (2025). Enhanced CNN-based Feature Extraction and Classification for Chinese Artwork Styles. Journal of Science, Innovation & Social Impact, 1(2), 135-148.
  44. [44] Liu, Y. (2026). Anomaly Detection and Cross-Center Consistency Assessment for Multi-Site Clinical Trial Quality Control. Journal of Science, Innovation & Social Impact, 2(1), 190-204.
  45. [45] Liu, W., Rao, G., & Lian, H. (2023). Anomaly Pattern Recognition and Risk Control in High-Frequency Trading Using Reinforcement Learning. Journal of Computing Innovations and Applications, 1(2), 47-58.
  46. [46] Liu, Y. (2025). Interpretable Early Detection of Adverse Drug Reactions: Integrating Robust Anomaly Scoring with Temporal Lag Analysis and Causal Verification. Journal of Science, Innovation & Social Impact, 1(1), 471-484.
  47. [47] Long, L., Zou, D., & Shi, W. (2026). NLP-Driven Psychological Contract Risk Detection in Cross-Cultural Teams: An XGBoost Approach with Cultural Adaptation. Artificial Intelligence and Machine Learning Review, 7(2), 43-53.
  48. [48] Lu, X. (2026). Multimodal Deep Learning for Advertising Content Safety: A Comprehensive Study on Detection and Governance Strategies. Journal of Science, Innovation & Social Impact, 2(1), 64-79.
  49. [49] Min, S., & Wei, C. (2023). Comparative Analysis of Filter-based Feature Selection Methods for High-Dimensional Data in Classification Tasks. Journal of Advanced Computing Systems, 3(8), 25-38.
  50. [50] Rao, G., Zheng, S., & Guo, L. (2025). Dynamic Reinforcement Learning for Suspicious Fund Flow Detection: A Multi-layer Transaction Network Approach with Adaptive Strategy Optimization. Applied and Computational Engineering, 145, 1-11.
  51. [51] Shi, X., & Weng, H. (2024). Comparative Analysis of Unsupervised Learning Approaches for Anomalous Billing Pattern Detection in Healthcare Payment Integrity. Journal of Computing Innovations and Applications, 2(1), 111-127.
  52. [52] Wu, X., Li, Y., Liu, Y., Pang, S., Wang, L., Wu, C., & Huo, H. (2019). Jointly detecting and retrieving vehicles from road image sequences based on CNN. In 2019 IEEE Intelligent Vehicles Symposium (IV) (pp. 2530-2535). IEEE.
  53. [53] Xiong, K., Wu, Z., & Jia, X. (2025). Deepcontainer: a deep learning-based framework for real-time anomaly detection in cloud-native container environments. Journal of Advanced Computing Systems, 5(1), 1-17.
  54. [54] Ye, H. (2025, April). AI-Enhanced Detection of Dynamic Structural Changes in Inflammatory Protein Interfaces: A Case Study of CD11b/Mac-1 Interactions. In 2025 6th International Conference on Computer Engineering and Application (ICCEA) (pp. 2173-2180). IEEE.
  55. [55] Ye, H. (2025). Bayesian Optimization-Based AI Framework for Nanobody Screening: Minimizing Experimental Failures in ELISA Detection Systems. Journal of Sustainability, Policy, and Practice, 1(4), 16-31.
  56. [56] Yuan, D., & Zhang, D. (2025, May). APAC-sensitive anomaly detection: Culturally-aware AI models for enhanced AML in US securities trading. In 2025 International Conference on Computer, AI, Systems and Automation (pp. 108-121). Pinnacle Academic Press.
  57. [57] Zhang, Q. (2025). Comparative Analysis of Pre-Trained Language Models for Medical Document Classification and Priority-Based Workflow Routing. Journal of Sustainability, Policy, and Practice, 1(4), 205-221.
  58. [58] Zhang, H., & Shi, W. (2026). Comparative Evaluation of Automated Data Consistency Detection Methods in Corporate SEC Filings. Journal of Advanced Computing Systems, 6(4), 1-14.
  59. [59] Zhang, Q. (2025). Enhanced Feature Fusion and Transfer Learning for Multi-Format Government Document Classification. Journal of Science, Innovation & Social Impact, 1(1), 427-441.
  60. [60] Zhang, C. (2025). Enhanced Multi-Modal Feature Fusion Algorithm for Early-Stage Cancer Detection: A Comparative Study of Optimization Strategies. Journal of Science, Innovation & Social Impact, 1(1), 318-328.
  61. [61] Zhang, Q. (2026). Improving Classification Accuracy for Unstructured Medical Documents via Multi-Engine OCR and Deep Learning Collaboration. Journal of Advanced Computing Systems, 6(2), 1-14.
  62. [62] Zhang, S., Wang, Y., & Weng, H. (2024). Industrial IoT Anomaly Detection Using Improved Autoencoder Architecture. Artificial Intelligence and Machine Learning Review, 5(1), 67-78.
  63. [63] Zhang, J. (2024). Performance Evaluation and Comparison of Machine Learning Algorithms for Anomalous Login Behavior Detection in Enterprise Networks. Artificial Intelligence and Machine Learning Review, 5(2), 77-90.
  64. [64] Zhong, M. (2025, September). Adaptive Anomaly Detection Threshold for Financial Data Quality Monitoring Based on Time Series Features. In Proceedings of the 2025 International Symposium on Artificial Intelligence and Computational Social Sciences (pp. 578-587).
  65. [65] Zhong, M. (2026). Optimization of Anomaly Detection Algorithms for Consumer Credit Default Rates Based on Time-Series Feature Extraction. Journal of Sustainability, Policy, and Practice, 2(1), 44-54.
  66. [66] Bai, Y. (2025). Adaptive Confidence-Weighted Feature Fusion for Robust Multimodal Autism Screening in Heterogeneous Pediatric Populations. Journal of Science, Innovation & Social Impact, 1(1), 485-498.
  67. [67] Bai, Y. (2025). Effectiveness Evaluation of Adaptive Difficulty Adjustment Algorithms with Multimodal Feedback for Social Skills Training in Children with Autism Spectrum Disorder. Journal of Sustainability, Policy, and Practice, 1(4), 117-129.
  68. [68] Cheng, Z. (2025). AI Enabled Cardiovascular Disease Risk Prediction through Multimodal Data Fusion: A Predictive Analytics Approach. Journal of Sustainability, Policy, and Practice, 1(2), 98-109.
  69. [69] Guo, Z., Man, Y., Sheng, J., Lin, B., Ahmed, A., Jiang, B., ... & Zhang, C. (2026). Event-VStream: Event-Driven Real-Time Understanding for Long Video Streams. arXiv preprint arXiv:2601.15655.
  70. [70] Guo, Y. (2025). Reliability Assessment and Adaptive Fusion Algorithm for Multi-Sensor Data in Autonomous Driving under Adverse Weather Conditions. Journal of Sustainability, Policy, and Practice, 1(4), 143-155.
  71. [71] Han, M. (2026). Anatomy-Aware Contrastive Pre-training: Leveraging Spatial Consistency for Label-Efficient Medical Image Diagnosis Across Multi-Modal Imaging. Journal of Sustainability, Policy, and Practice, 2(1), 55-70.
  72. [72] Han, M., Jin, Z., & Zou, D. (2026). Comparative Evaluation of Self-Supervised Pretraining Strategies for Few-Shot Medical Image Analysis. Artificial Intelligence and Machine Learning Review, 7(2), 23-42.
  73. [73] Huang, Y. (2024). Adaptive Importance Sampling for Jump-Diffusion CVA A Variance-Reduction Framework. Academia Nexus Journal, 3(3).
  74. [74] Huo, H., Li, Y., Wu, C., Wu, X., Tian, Z., & Liu, Y. (2019). Semantic segmentation and scene reconstruction for traffic simulation using CNN. In 2019 2nd China Symposium on Cognitive Computing and Hybrid Intelligence (CCHI) (pp. 117-122). IEEE.
  75. [75] Jiang, Z., & Wang, M. (2025). Evaluation and Analysis of Chart Reasoning Accuracy in Multimodal Large Language Models: An Empirical Study on Influencing Factors. Pinnacle Academic Press Proceedings Series, 3, 43-58.
  76. [76] Li, Z., & Wang, Z. (2024). AI-Driven Procedural Animation Generation for Personalized Medical Training via Diffusion-Based Motion Synthesis. Artificial Intelligence and Machine Learning Review, 5(3), 111-123.
  77. [77] Li, Y. (2026). Benchmarking Robustness-Efficiency Trade-offs of Camera-LiDAR Fusion Strategies for 3D Object Detection Under Environmental Corruptions. Journal of Science, Innovation & Social Impact, 2(2), 104-116.
  78. [78] Li, Y., Wu, C., Li, L., Liu, Y., & Zhu, J. (2021). Caption generation from road images for traffic scene modeling. IEEE Transactions on intelligent transportation systems, 23(7), 7805-7816.
  79. [79] Li, L., Li, Y., Wu, C., Dong, H., Jiang, P., & Wang, F. (2021). Detail fusion GAN: High-quality translation for unpaired images with GAN-based data augmentation. In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 1731-1736). IEEE.
  80. [80] Wang, Z. (2025). Cultural-Intelligent Dynamic Medical Animation Generation for Cross-Lingual Telemedicine Communication Enhancement. Journal of Science, Innovation & Social Impact, 1(1), 209-221.
  81. [81] Wang, Z. (2025, April). DeepMotionNet: AI-Driven Predictive Animation State Transitions for Reducing Perceptual Latency in Competitive FPS Games. In 2025 6th International Conference on Computer Engineering and Application (ICCEA) (pp. 01-08). IEEE.
  82. [82] Wang, X., Liu, M., & Long, L. (2026). Effectiveness Evaluation of Attention Mechanism Strategies in Deep Learning-Based Single Image Super-Resolution. Journal of Global Engineering Review, 4(1), 89-98.
  83. [83] Wang, Z., & Chu, Z. (2025). GAN-Based Intelligent Keyframe Interpolation Method for Character Animation: An Automated In-betweening Approach. Journal of Science, Innovation & Social Impact, 1(2), 29-40.
  84. [84] Wei, C., & Pan, Z. (2026). Accelerating Clinical Trial Recruitment Through Automated Eligibility Screening with Multi-Modal Deep Learning. Journal of Computing Innovations and Applications, 4(1), 1-11.
  85. [85] Wu, C., Li, Y., Li, L., Wang, L., & Liu, Y. (2020). Caption Generation from Road Images for Traffic Scene Construction. In 2020 IEEE Intelligent Vehicles Symposium (IV) (pp. 1271-1276). IEEE.
  86. [86] Wu, X., Li, Y., Hao, Z., Wu, C., Wang, X., & Liu, Y. (2019). Image style transformation based on structure GAN. In 2019 Chinese Automation Congress (CAC) (pp. 2002-2007). IEEE.
  87. [87] Xu, S., Zhao, F., & Wang, X. (2025). An Empirical Comparison of Generation Quality and Diversity Between Discrete Diffusion and Autoregressive Text Generation. Artificial Intelligence and Machine Learning Review, 6(2), 16-26.
  88. [88] Yu, M., & Li, Z. (2025). An Empirical Comparison of Discrete Video Tokenization Schemes for Video Question Answering and Video Captioning. Artificial Intelligence and Machine Learning Review, 6(2), 27-50.
  89. [89] Zhang, Q. (2026). Adaptive OCR Engine Selection and Evaluation for Multi-Format Government Document Digitization. Artificial Intelligence and Machine Learning Review, 7(1), 29-39.
  90. [90] Zou, D., Chen, Z., & Ling, Z. (2025). A Comparative Evaluation of Deep Learning Paradigms for Low-Light Image Enhancement: From CNNs to Diffusion Models. Journal of Computing Innovations and Applications, 3(2), 85-95.
  91. [91] Chen, Y., & Hu, J. (2026). Graph Neural Network-Based Cascading Disruption Path Identification in Multi-Tier Rare Earth Processing Networks. Journal of Global Engineering Review, 4(1), 99-112.
  92. [92] Cheng, Z. (2025). Graph Attention-Based Feature Selection for Multi-Omics Drug Target Prediction in Cardiovascular Diseases. Journal of Science, Innovation & Social Impact, 1(1), 294-306.
  93. [93] Chung, P. T. (2025, December). Enhancing Dental Polymer Formulation through Interpretable Machine Learning: A Comparative Analysis of Feature Selection and Algorithm Performance. In Proceedings of the 2025 6th International Conference on Computer Science and Management Technology (pp. 234-241).
  94. [94] Dong, Z., & Zhang, F. (2025). Deep Learning-Based Noise Suppression and Feature Enhancement Algorithm for LED Medical Imaging Applications. Journal of Science, Innovation & Social Impact, 1(1), 9-18.
  95. [95] Hu, J., Wang, X., & Lai, J. (2026). Benchmarking Learned Cardinality Estimation Techniques for Analytical Query Processing in Data Warehouses. Journal of Computer Technology and Applied Mathematics, 3(3), 1-8.
  96. [96] Ming, Y., Fu, H., Jiang, Y., & Yu, H. (2020). Variational Bayesian Group-Level Sparsification for Knowledge Distillation. IEEE Access, 8, 126628-126636.
  97. [97] Tang, T., & Yu, M. (2024). A Comparative Empirical Study of Semantic Signal Enhancement Methods for User Interest Features in CTR Prediction: Applicability of TF-IDF Weighting, Sentence-BERT Embeddings, and LDA Topic Fusion. Journal of Computing Innovations and Applications, 2(1), 165-174.
  98. [98] Tang, T., & Yu, M. (2024). A Comparative Evaluation of LLM-Generated Semantic Tags versus Classical Text Features (TF-IDF, LDA, BERT Embeddings) for User-Interest Enrichment in Short-Video Recommendation. Artificial Intelligence and Machine Learning Review, 5(1), 129-140.
  99. [99] Tang, T., Fu, X., & Luo, C. (2026). An Empirical Comparison of High-Order Feature Interaction Operators for Conversion Rate Prediction in Sparse, High-Cardinality Message-Ads Traffic: Accuracy, Efficiency, and Offline--Online Consistency. Journal of Science, Innovation & Social Impact, 2(3), 12-22.
  100. [100] Tu, W., Wan, G., Shang, Z., & Du, B. (2025). Efficient relational context perception for knowledge graph completion. Applied Intelligence, 55(15), 1005.
  101. [101] Wang, M., Jiang, Z., & Zhou, S. (2024). Cross-cultural semantic differences in emoji usage on social media platforms. Journal of Advanced Computing Systems, 4(5), 55-66.
  102. [102] Wang, Z. (2026). Optimizing Transaction Matching Performance Using Hybrid Collaborative Filtering and Deep Learning: An Empirical Analysis of Feature Engineering and Similarity Metrics. Journal of Science, Innovation & Social Impact, 2(1), 111-123.
  103. [103] Weng, H. (2025). Deep Embedding Clustering with Adaptive Feature Selection for Banking Customer Segmentation. Spectrum of Research, 5(2).
  104. [104] Weng, H. (2026). Predictive Power versus Feature Stability: An Empirical Comparison of Filter, Embedded, and Wrapper Feature Selection Methods for Consumer Credit Pre-Approval. Academia Nexus Journal, 5(1).
  105. [105] Wu, C., & Pan, Z. (2024). An Integrated Graph Neural Network and Reinforcement Learning Framework for Intelligent Drug Discovery. Journal of Advanced Computing Systems, 4(6), 19-29.
  106. [106] Wu, Z., Feng, Z., & Dong, B. (2024). Optimal feature selection for market risk assessment: A dimensional reduction approach in quantitative finance. Journal of Computing Innovations and Applications, 2(1), 20-31.
  107. [107] Xiao, P., & Fu, X. (2026). Comparative Evaluation of Post-Hoc Feature Attribution Methods on Tabular Financial Data: Faithfulness, Stability, and Computational Efficiency. Journal of Science, Innovation & Social Impact, 2(3), 1-11.
  108. [108] Zhang, Z., & Wu, Z. (2023). Context-aware feature selection for user behavior analytics in zero-trust environments. Journal of Advanced Computing Systems, 3(5), 21-33.
  109. [109] Zhong, M. (2026). Multi-Dimensional Feature Analysis and Evaluation Methods for Anomalous Fund Flow Identification in Cross-Border Financial Transactions. Journal of Science, Innovation & Social Impact, 2(2), 1-13.
  110. [110] Bai, Y., & Xiao, P. (2026). Adaptive Prompt Selection and Fading Optimization for Autism Skill Acquisition: A Reinforcement Learning Approach. Journal of Advanced Computing Systems, 6(1), 32-44.
  111. [111] Cai, Y. (2025, June). NLP-Enhanced Predictive Analytics for UHNW Client Investment Behavior: A Risk-Aware Portfolio Optimization Approach in Volatile Markets. In Proceedings of the 2025 2nd International Conference on Digital Economy, Blockchain and Artificial Intelligence (pp. 185-191).
  112. [112] Chen, Y., Chen, Z., & Zou, D. (2025). CarbonShift: Harnessing Grid Carbon Variability for Geo-Distributed Workload Scheduling. Artificial Intelligence and Machine Learning Review, 6(4), 18-31.
  113. [113] Chen, Y., & Chen, Z. (2025). Multi-Objective Deep Reinforcement Learning for Carbon-Aware Spatiotemporal Workload Scheduling in Geo-Distributed Data Centers. Journal of Advanced Computing Systems, 5(10), 18-30.
  114. [114] Chung, P. T. (2026). Multi-Objective Optimization of Process Parameters for Dental Resin 3D Printing Using Improved NSGA-II Algorithm. Journal of Science, Innovation & Social Impact, 2(1), 276-287.
  115. [115] Crowford, A., Cai, Y., & Langford, V. (2024). Machine Learning-Enhanced Dynamic Asset Allocation in Target-Date Investment Strategies for Pension Funds. Journal of Computing Innovations and Applications, 2(2), 122-135.
  116. [116] Dong, Z., & Jia, R. (2025). Adaptive Dose Optimization Algorithm for LED-based Photodynamic Therapy Based on Deep Reinforcement Learning. Journal of Sustainability, Policy, and Practice, 1(3), 144-155.
  117. [117] Dong, B., Zhang, D., & Xin, J. (2024). Deep reinforcement learning for optimizing order book imbalance-based high-frequency trading strategies. Journal of Computing Innovations and Applications, 2(2), 33-43.
  118. [118] Fu, X., & Zhao, F. (2025). An Empirical Comparison of Few-Shot Example Selection Strategies for In-Context Learning on Public Reasoning and QA Benchmarks. Journal of Computing Innovations and Applications, 3(2), 119-128.
  119. [119] Han, J., & Jia, R. (2026). AI-Enhanced Cross-Asset Liquidity Contagion Pathway Identification and Dynamic Hedging Strategy Optimization: Evidence from US Equity, Bond, and Derivatives Markets. Journal of Computing Innovations and Applications, 4(1), 89-96.
  120. [120] Lei, Y. (2025, October). Intelligent Prediction and Dynamic Scheduling Optimization Strategy for Cloud Computing Resources under Burst Load Scenarios. In Proceedings of the 2025 International Symposium on Machine Learning and Social Computing (pp. 59-67).
  121. [121] Li, Y. (2026). Performance Benchmarking and Optimization Strategies for Depth Estimation Algorithms in Unstructured Environments. Journal of Sustainability, Policy, and Practice, 2(2), 32-43.
  122. [122] Long, L., & Hu, J. (2026). Multi-Objective Particle Swarm Optimization for Site Selection and Policy Subsidy Maximization of Foreign Renewable Energy Enterprises in the United States. Artificial Intelligence and Machine Learning Review, 7(2), 54-69.
  123. [123] Lu, X. (2025, August). Adaptive Optimization of Advertising Creative Visual Elements Based on Multi-dimensional User Behavior Data. In Proceedings of the 2025 International Conference on Generative Artificial Intelligence for Business (pp. 360-368).
  124. [124] Rao, G., Wang, Z., & Liang, J. (2025). Reinforcement Learning for Pattern Recognition in Cross-Border Financial Transaction Anomalies: A Behavioral Economics Approach to AML. Applied and Computational Engineering, 142, 116-127.
  125. [125] Shi, W., & Bai, Y. (2024). Adaptive Learning Rate Optimization for Personalized Educational Interventions in Autism Spectrum Disorder: A Multi-Objective Reinforcement Learning Approach. Artificial Intelligence and Machine Learning Review, 5(4), 128-138.
  126. [126] Shi, W., & Wang, J. (2026). Intelligent Path Optimization for Carbon-Constrained Last-Mile Delivery: A Reinforcement Learning and Heuristic Approach. Journal of Advanced Computing Systems, 6(1), 19-31.
  127. [127] Wang, J. (2025, October). Artificial Intelligence-Driven Seasonal Consumption Forecasting and Resource Allocation Optimization in Luxury Brand Marketing. In Proceedings of the 2025 2nd International Conference on Digital Economy and Computer Science (pp. 1119-1127).
  128. [128] Wang, Y. (2025). Data-Driven Analysis of Transportation Route Efficiency and Carbon Emission Correlation in Retail Distribution Networks. Journal of Science, Innovation & Social Impact, 1(1), 253-264.
  129. [129] Wang, Z. (2025, October). Machine Learning-Driven Investor-Asset Matching Optimization in Commercial Real Estate Investment Decisions. In Proceedings of the 2025 2nd International Conference on Digital Economy and Computer Science (pp. 1110-1118).
  130. [130] Weng, H., Zhang, S., & Min, S. (2024). Multi-Constraint Optimization for Real-Time Bidding: A Reinforcement Learning Approach. Artificial Intelligence and Machine Learning Review, 5(1), 93-104.
  131. [131] Weng, H., & Li, X. (2024). Renewable-Aware Cooperative Scheduling for Distributed AI Training Across Geo-Distributed Data Centers. Artificial Intelligence and Machine Learning Review, 5(2), 91-100.
  132. [132] Wu, Z., Wang, S., Ni, C., & Wu, J. (2024). Adaptive traffic signal timing optimization using deep reinforcement learning in urban networks. Artificial Intelligence and Machine Learning Review, 5(4), 55-68.
  133. [133] Wu, Z., Cheng, C., & Zhang, C. (2025). Cloud-Enabled AI Analytics for Urban Green Space Optimization: Enhancing Microclimate Benefits in High-Density Urban Areas. 2025 International Forum on Computing and Intelligent Systems, 123-133.
  134. [134] Xiao, P., Wang, Y., & Montgomery, I. (2024). Deep Reinforcement Learning for Route Optimization in E-commerce Return Management. Journal of Computing Innovations and Applications, 2(2), 100-110.
  135. [135] Ye, H. (2025). Deep Reinforcement Learning-Driven Efficacy-Toxicity Balance Optimization Strategy for Personalized Drug Combination in Cancer Patients. Journal of Science, Innovation & Social Impact, 1(1), 307-317.
  136. [136] Zhang, C. (2025, October). Comparative Study of AI Algorithms in Personalized Ovarian Stimulation Protocol Optimization: Predictive Performance Analysis Based on Patient Baseline Characteristics. In Proceedings of the 4th International Conference on Artificial Intelligence and Intelligent Information Processing (pp. 654-662).
  137. [137] Zhang, C. (2024). Deep Learning Dose Optimization with Uncertainty Quantification for Intensity-Modulated Radiotherapy: A 3D Radiomics Approach. Artificial Intelligence and Machine Learning Review, 5(2), 116-129.
  138. [138] Fu, H., Ming, Y., Jiang, Y., & Fan, C. (2018, August). Effective SVD-based deep network compression for automatic speech recognition. In International Workshop on Face and Facial Expression Recognition from Real World Videos (pp. 35-47). Cham: Springer International Publishing.
  139. [139] Lei, Y., & Holloway, V. (2024). Adaptive Learning-Enhanced Convex Optimization for Energy-Efficient Cloud Resource Scheduling. Journal of Advanced Computing Systems, 4(11), 73-85.
  140. [140] Li, M., Wang, X., & Yu, M. (2025). Comparative Evaluation of Zero-Shot and Few-Shot Performance of Large Language Models in Low-Resource Language Machine Translation. Journal of Global Engineering Review, 3(2), 59-68.
  141. [141] Long, X. (2025, September). Machine Learning-Based Power Consumption Prediction and Dynamic Adjustment Strategies for Enterprise Servers. In Proceedings of the 2025 8th International Conference on Computer Information Science and Artificial Intelligence (pp. 1310-1319).
  142. [142] Luo, C., & Wang, X. (2026). Latency–Throughput Tradeoffs of ONNX Runtime, TensorRT-LLM, vLLM, and Triton: An Empirical Comparison on 1B–3B Parameter LLM Inference. Spectrum of Research, 6(1).
  143. [143] Ming, Y., & Fu, H. (2020, July). Variational bayesian sparsification for distillation compression. In 2020 IEEE International Conference on Multimedia and Expo (ICME) (pp. 1-6). IEEE.
  144. [144] Pan, Z. (2026). Integrating Causal Inference with Multi-Channel Attribution for Equitable Pharmaceutical Marketing Effectiveness Evaluation. Journal of Science, Innovation & Social Impact, 2(1), 31-45.
  145. [145] Cai, Y. (2025). NLP-Quantified ESG News Sentiment and Portfolio Outcomes Evidence from Real-Time Signals. Annals of Applied Sciences, 6(1).
  146. [146] Chen, Z., & Wang, M. (2025). Evaluating the Quality of Large Language Model-Generated Explanations in Recommendation Tasks: A Multi-Dimensional Comparative Analysis. Journal of Global Engineering Review, 3(1), 54-63.
  147. [147] Fu, X., & Zou, D. (2025). A Comparative Empirical Study of Over-Refusal Behavior in Closed-Source Large Language Models on Pseudo-Harmful Prompts. Artificial Intelligence and Machine Learning Review, 6(3), 34-45.
  148. [148] Fu, X., Tang, T., & Luo, C. (2026). An Empirical Comparison of ReAct, Reflexion, Plan-and-Solve, and Tree-of-Thought Planning Strategies on Financial Question Answering and Numerical Reasoning Tasks. Journal of Science, Innovation & Social Impact, 2(3), 23-34.
  149. [149] Guan, H. (2025). Context-Aware Semantic Ambiguity Resolution in Cross-Cultural Dialogue Understanding. Journal of Sustainability, Policy, and Practice, 1(2), 136-147.
  150. [150] Lai, J., & Li, Z. (2026). A Comparative Empirical Evaluation of Single-Agent and Multi-Agent LLM Prompting Strategies for Automated Formative Feedback in Education. Journal of Intelligence and Engineering Technology, 1(2), 29-38.
  151. [151] Lai, J., & Li, Z. (2026). Does an LLM-Based Feedback Agent Move the Needle? An Empirical Study of Student Correctness and Hint Dependency on the ASSISTments 2017 Interaction Logs. Academia Nexus Journal, 5(1).
  152. [152] Li, M., Zhao, F., & Tang, T. (2024). How Prompt Specificity Affects Edge Case Handling in LLM-Generated Code: An Empirical Evaluation. Artificial Intelligence and Machine Learning Review, 5(4), 139-149.
  153. [153] Li, Z., & Chen, Z. (2025). Performance Evaluation of Prompt Generation Strategies for AI Agents in Online Programming Education. Journal of Advanced Computing Systems, 5(9), 14-27.
  154. [154] Shi, X. (2024). Spatiotemporal Preference Modeling for Ride-Hailing and Context-Aware Recommendations A Machine-Learning Framework. Spectrum of Research, 4(2).
  155. [155] Wang, X., Fu, X., & Zou, D. (2025). Passage, Sentence, or Proposition? An Empirical Comparison of Retrieval Granularity Effects on LLM Answer Accuracy in Retrieval-Augmented Generation. Journal of Global Engineering Review, 3(1), 81-90.
  156. [156] Wang, M., Xiao, P., & Yu, M. (2026). Sparse, Dense, or Hybrid? Comparing Retrieval Strategies for Biomedical Question Answering with Retrieval-Augmented Generation. Journal of Science, Innovation & Social Impact, 2(2), 141-152.
  157. [157] Weng, H., Yang, Q., Fu, H., Pan, H., & Lv, X. (2026). When Retrieval Hurts Code Completion: A Diagnostic Study of Stale Repository Context. arXiv preprint arXiv:2605.14478.
  158. [158] Zhang, Y. (2026). A Comparative Study of Machine Learning Methods for Automated Customer Service Dialogue Quality Assessment. Journal of Science, Innovation & Social Impact, 2(1), 328-338.
  159. [159] Zhang, Y. (2026). Language-Driven Interactive Annotation for Pulmonary Nodules in Chest CT: An LLM Prompt-Translation and Multi-Round Refinement Approach. Journal of Science, Innovation & Social Impact, 2(2), 55-66.
  160. [160] Zhou, Y., & Long, L. (2026). Causal Effect Evaluation of Personalized Reminder Strategies on Government Welfare Program Enrollment: A Propensity Score Matching Approach. Journal of Computing Innovations and Applications, 4(1), 106-116.
  161. [161] Cao, H., & Liu, M. (2026). Empirical Evaluation of Constraint Discovery Techniques for Business Logic Consistency Verification in Payroll Data Migration. Journal of Sustainability, Policy, and Practice, 2(3), 92-103.
  162. [162] Guo, Y. (2026). A Multi-Dimensional Coverage Metric with Evolutionary Search for Safety-Critical Scenario Generation in Autonomous Driving Testing. Journal of Science, Innovation & Social Impact, 2(2), 82-93.
  163. [163] Guo, Y., Li, Y., Chen, S., Wu, Y., Poll, O., Ren, Z., ... & Baran, P. S. (2025). Scalable total synthesis of saxitoxin and related natural products. Nature, 646(8084), 351-357.
  164. [164] Han, M. (2025). Intelligent Recognition of Anomalous Behaviors in Medical Insurance Through Deep Learning. Journal of Science, Innovation & Social Impact, 1(1), 410-426.
  165. [165] Kang, A., Zhang, K., & Chen, Y. (2025). AI-Assisted Analysis of Policy Communication during Economic Crises: Correlations with Market Confidence and Recovery Outcomes. Pinnacle Academic Press Proceedings Series, 3, 159-173.
  166. [166] Kang, A., & Ma, X. (2025). AI-Based Pattern Recognition and Characteristic Analysis of Cross-Border Money Laundering Behaviors in Digital Currency Transactions. Pinnacle Academic Press Proceedings Series, 5, 1-19.
  167. [167] Kang, A., Li, Z., & Meng, S. (2023). AI-Enhanced Risk Identification and Intelligence Sharing Framework for Anti-Money Laundering in Cross-Border Income Swap Transactions. Journal of Advanced Computing Systems, 3(5), 34-47.
  168. [168] Kang, A., Min, S., & Yuan, D. (2024). Comparative Analysis of Foreign Exchange Market Shock Transmission and Recovery Resilience Among Major Economies Under Geopolitical Conflicts: Evidence from the Russia-Ukraine Crisis. Journal of Computing Innovations and Applications, 2(1), 46-61.
  169. [169] Li, Y. (2025, December). Comparative Analysis of Illumination Normalization Methods for Autonomous Driving Under Challenging Lighting Conditions. In Proceedings of the 2025 6th International Conference on Computer Science and Management Technology (pp. 633-639).
  170. [170] Li, J., Zhang, F., & Li, M. (2026). Comparative Effectiveness of Blockchain Provenance Verification on Counterfeit Reduction in Art Transactions: A Multi-Scenario Empirical Assessment. Artificial Intelligence and Machine Learning Review, 7(2), 82-92.
  171. [171] Li, J. (2026). Evaluating BLIP-2, LLaVA, and GPT-4V for Accessible Artwork Description Generation in Virtual Museums: A Comparative Study Based on WCAG-Aligned Evaluation and User Satisfaction. Journal of Sustainability, Policy, and Practice, 2(3), 1-12.
  172. [172] Li, Y., Buchberger, B., Liu, Y., Capone, C., Adak, T., Ojha, S., ... & Baran, P. S. (2026). General Access to C-Glycosides via Redox-Neutral Radical Cross-Coupling of Glycohydrazides.
  173. [173] Li, Y., & Fu, H. (2026). Interpretable Gradient Boosting for Transmission Capital Expenditure Deviation: A Multi-Decade Benchmark on FERC-Regulated Utilities. Academia Nexus Journal, 5(2).
  174. [174] Li, Y., & Long, L. (2026). Lightweight AI-Driven Stress Testing for Small and Medium Financial Institutions: A Variational Autoencoder Approach with Extreme Value Theory for Macroeconomic Scenario Generation. Artificial Intelligence and Machine Learning Review, 7(1), 108-119.
  175. [175] Li, J. (2026). Style Genes: Leveraging Generative AI for Artwork Authentication through Artistic Style Consistency Analysis. Journal of Sustainability, Policy, and Practice, 2(1), 87-100.
  176. [176] Sheng, J. Y., Jia, X. Y., Guo, Z. H., Gao, Y., Cao, Y. P., & Feng, X. Q. (2025). Characterizing Layer-Specific Mechanical Properties of Soft Materials by Pipette Aspiration Using Transformer Model and SHapley Additive exPlanations. International Journal of Applied Mechanics, 17(06), 2550048.
  177. [177] Wang, J., & Jia, R. (2026). AI-Enhanced What-If Scenario Analysis in Supply Chain Digital Twins: A Multi-Objective Trade-Off Perspective on Cost, Resilience, and Carbon Efficiency. Journal of Computing Innovations and Applications, 4(1), 97-105.
  178. [178] Wang, M., & Zhu, L. (2024). Linguistic analysis of verb tense usage patterns in computer science paper abstracts. Academia Nexus Journal, 3(3).
  179. [179] Weng, H., Wang, H., & Wei, C. (2024). Adaptive Bidding Strategies for Hybrid Auction Mechanisms in Programmatic Advertising. Journal of Advanced Computing Systems, 4(4), 13-25.
  180. [180] Wu, X., Li, J., & Ren, W. (2024). Risk Assessment Framework for Data Leakage Prevention Using Machine Learning Techniques. Artificial Intelligence and Machine Learning Review, 5(3), 55-66.
  181. [181] Ye, H. (2026). Comparative Analysis of Machine Learning Approaches for Molecular Pathway Identification and Biomarker Discovery in Immune-Related Diseases. Journal of Science, Innovation & Social Impact, 2(1), 46-63.
  182. [182] Zhang, H. (2026). A Comparative Study of NER Methods for Ownership Structure Extraction from M&A Due Diligence Documents. Journal of Sustainability, Policy, and Practice, 2(1), 71-86.
  183. [183] Zhang, S., Jia, R., & Li, Z. (2024). Agentic AI Across Domains: A Comprehensive Review of Capabilities, Applications, and Future Directions. Journal of Computing Innovations and Applications, 2(1), 86-98.
  184. [184] Zhang, D., & Zhang, F. (2025). AI-Assisted Identification and Equity Assessment of Vulnerable Population Impacts in US Energy Transition. Journal of Advanced Computing Systems, 5(7), 1-17.
  185. [185] Zhang, R., Fan, C., Ming, Y., Fu, H., & Meng, X. (2020, October). An Effective Hierarchical Resolution Learning Method for Low-Resolution Targets Tracking. In 2020 IEEE International Conference on Image Processing (ICIP) (pp. 2076-2080). IEEE.
  186. [186] Zhang, F., Cheng, Z., & Holloway, V. (2024). Deep Learning in Cardiovascular CT Imaging: Evolution, Trends, and Clinical Translation from 2020 to 2025. Journal of Computing Innovations and Applications, 2(2), 88-99.
  187. [187] Zhang, Y. (2026, February). Performance Evaluation of Emotion Recognition Algorithms for Customer Service Virtual Assistants: A Comparative Analysis. In Proceedings of the 2026 6th International Conference on Computer Network Security and Software Engineering (pp. 145-151).
  188. [188] Zhang, D., & Feng, E. (2024). Quantitative Assessment of Regional Carbon Neutrality Policy Synergies Based on Deep Learning. Journal of Advanced Computing Systems, 4(10), 38-54.