Recent research highlights the integration of machine learning and deep learning to enhance terahertz data processing across multiple fields, including biomedical diagnostics, material recycling, and wireless communications. In biosensing applications, advanced regression and deep learning models accelerate absorption prediction and improve the classification of cancer cells and tissue sections using terahertz metasurface responses. Similarly, machine learning classifiers optimize adaptive modulation schemes for terahertz communication systems facing dynamic channel conditions. In materials science, combining terahertz spectroscopy with complementary techniques and machine learning algorithms improves the automated classification of industrial polymer waste.
Suggested readings
- Feuille de route d’industrialisation MAG — Divulgation défensive globale. Zenodo (CERN European Organization for Nuclear Research), 2026-10-25. Read the paper
- Explainable machine learning assisted terahertz metamaterial absorber for multi-cancer cell biosensing. Next Chemical Engineering, 2026-10-01. Read the paper
- Decision TreeBased SNRAware Adaptive Modulation for Reliable and HighThroughput 6G Terahertz Communications. International Journal of Engineering Development and Research, 2026-10-01. Read the paper
- Deep Learning-Assisted Terahertz Metasurface Biosensing for Rapid Ex Vivo Molecular Assessment of Diffuse Glioma Frozen Sections. ACS Sensors, 2026-09-26. Read the paper
- Real-time classification of waste polyethylene using terahertz and raman spectroscopy integrated machine learning. Journal of Material Cycles and Waste Management, 2026-10-08. Read the paper
- Terahertz Technology – A New Frontier in Oral Cancer Diagnostics. Journal of chemical health risks, 2026-10-06. Read the paper