Aissa, N.E.H.S.B., Korichi, A., Lakas, A., Kerrache, C.A., Calafate, C.T., Assessing robustness to adversarial attacks in attention-based networks: Case of EEG-based motor imagery classification. SLAS Technol, 29(4), 2024, 100142, 10.1016/j.slast.2024.100142.
Wu, M., Liu, W., Zheng, S., Probiotic fermentation environment control under intelligent data monitoring. SLAS Technol, 29(4), 2024, 100153, 10.1016/j.slast.2024.100153.
Byeon, H., Quraishi, A., Khalaf, M.I., Sunil, M.P., Raza Khan, I., Kumar Dutta, A., Dasari, R., Reddy Yellu, R., Ahmad Reegu, F., Bhatt, M.W., Bio-inspired EEG signal computing using machine learning and fuzzy theory for decision making in future-oriented brain-controlled vehicles. SLAS Technol, 29(5), 2024, 100187, 10.1016/j.slast.2024.100187.
Zhang, K., Zhong, J., Bio inspired technological performance in color Doppler ultrasonography and echocardiography for enhanced diagnostic precision in fetal congenital heart disease. SLAS Technol, 29(6), 2024, 100207, 10.1016/j.slast.2024.100207.
Chen, Y., Li, M., Guo, K., Exploring the mechanisms and current status of acupuncture in alleviating tumor metabolism and associated diseases: Insights from the central nervous system and immune microenvironment. SLAS Technol, 29(6), 2024, 100208, 10.1016/j.slast.2024.100208.
Zhao, X., Wang, Q., Wang, S., Wang, W., Chen, X., Lu, S., A novel multi-omics approach for identifying key genes in intervertebral disc degeneration. SLAS Technol, 29(6), 2024, 100223, 10.1016/j.slast.2024.100223.
Song, X., Zhang, J., Hua, W., Zheng, Y., Liu, X., Zhu, Y., Bin, S., Ding, J., Sun, S., Bio inspired microfluidic-based analysis of Klebsiella pneumoniae virulence factors and antimicrobial resistance. SLAS Technol, 29(6), 2024, 100209, 10.1016/j.slast.2024.100209.
Wang, W., Li, X., Yu, H., Li, F., Chen, G., Machine learning model for early prediction of survival in gallbladder adenocarcinoma: A comparison study. SLAS Technol, 29(6), 2024, 100220, 10.1016/j.slast.2024.100220.
Chen, S., Wang, L., Zhu, R., Yu, J., Th1/Th2 cytokines in early peripheral blood of patients with multiple injuries and its predictive value for SIRS: A bioinformatic analysis. SLAS Technol, 29(4), 2024, 100150, 10.1016/j.slast.2024.100150.
Bao, L., Chen, M., Dai, B., Lei, Y., Qin, D., Cheng, M., Song, W., He, W., Chen, B., Shen, H., Nanoengineered therapeutic strategies targeting SNHG1 for mitigating microglial ischemia-reperfusion injury implications for hypoxic-ischemic encephalopathy. SLAS Technol, 29(4), 2024, 100167, 10.1016/j.slast.2024.100167.
Fu, D.-S., Adili, A., Chen, X., Li, J.-Z., Muheremu, A., Abnormal genes and pathways that drive muscle contracture from brachial plexus injuries: Towards machine learning approach. SLAS Technol, 29(4), 2024, 100166, 10.1016/j.slast.2024.100166.
Xu, F., Zhu, W., Evalution of neurodiagnostic insights for enhanced evaluation and optimization of badminton players' physical function via data mining technique. SLAS Technol, 29(4), 2024, 100138, 10.1016/j.slast.2024.100138.
Tang, D., Systematic training of table tennis players' physical performance based on artificial intelligence technology and data fusion of sensing devices. SLAS Technol, 29(4), 2024, 100151, 10.1016/j.slast.2024.100151.
Chen, X., Jin, J., Ke, W., Mao, Y., Hao, F., Xu, D., Exploring cognitive behavioral aspects in educational psychology: A rigorous analysis of reliability and validity measures. SLAS Technol, 29(4), 2024, 100144, 10.1016/j.slast.2024.100144.
Kiran, A., Alsaadi, M., Dutta, A.K., Raparthi, M., Soni, M., Alsubai, S., Byeon, H., Kulkarni, M.H., Asenso, E., Bio-inspired deep learning-personalized ensemble Alzheimer's diagnosis model for mental well-being. SLAS Technol, 29(4), 2024, 100161, 10.1016/j.slast.2024.100161.
