iosr-JCE  Volume-28 ~ Issue-3 ~ May - Jun. 2026

Abstract: Organ transplantation is one of the most critical medical procedures, where timely identification of a suitable donor-recipient match can significantly improve survival rates. However, traditional organ matching systems are often slow, complex, and dependent on manual evaluation of multiple medical factors. To overcome these challenges, It is an intelligent AI-based organ matching system that improves the speed, accuracy, and reliability of donor-recipient selection. The system uses machine learning techniques to analyze important medical parameters such.....

Key Word: Organ Transplantation, Artificial Intelligence, Machine Learning, Predictive Analysis, Donor-Recipient Matching, Healthcare Analytics, Medical Decision Support System, Organ Compatibility Prediction, Smart Healthcare

[1]. S. Patwardhan Et Al., “Durable Mixed Chimerism May Permit Subsequent Immunosuppression-Free Intestinal Transplantation — A Proof-Of-Principle Study,” Am. J. Transplant., Vol. 25, No. 4, Pp. 825–835, Apr. 2025.
[2]. C. Smullin Et Al., “Two-Stage Approach To Intestinal And Multivisceral Transplantation: Patient And Graft Survival Outcomes Of Two-Staged It/Mvtx In High-Acuity Recipients Are Excellent And Comparable To One-Stage It/Mvtx For Low-Risk Patients,” Am. J. Transplant., 2025.
[3]. S. Merl Et Al., “Development Of A Large Animal Orthotopic Intestinal Transplantation Model With Long-Term Survival For Study Of Immunologic Outcomes,” Front. Transplant., Vol. 3, Art. No. 1367486, 2024.
[4]. F. Lacaille Et Al., “Indications And Successes Of Intestinal Transplantation In Children In The 21st Century: A Retrospective Cohort Study,” Clin. Nutr. Espen, Vol. 62, Pp. 247–252, Aug. 2024.
[5]. M. L. Samoylova Et Al., “Evolution Of Intestinal And Multivisceral Transplantation: A Thirty-Year Report Of Trends In Utilization And Outcomes In The United States,” Intest. Failure J., 2024.

Abstract: Cryptocurrencies have become a popular and volatile investment asset class, where accurate price prediction is crucial for informed decision-making by investors and traders. Cryptocurrency price fluctuations are influenced by various factors such as news, tax policy changes, technical changes, and external factors. To predict the price trend of cryptocurrencies, individuals and analytical organizations use various tools such as technical analysis,fundamental analysis, and machine learning models. This study introduces an efficient model using the GRU- ALR recurrent neural....

Key Word: Cryptocurrency price prediction, GRU-ALR, Bitcoin

[1]. Akyildirim, E., Cepni, O., Corbet, S., & Uddin, G. S. (2023). Forecasting Mid-Price Movement Of Bitcoin Futures Using Machine Learning. Annals Of Operations Research, 330(1), 553-584.
[2]. Almeida, J., & Gonçalves, T. C. (2022). A Systematic Literature Review Of Volatility And Risk Management On Cryptocurrency Investment: A Methodological Point Of View. Risks, 10(5), 107.
[3]. Almeida, J., & Gonçalves, T. C. (2023). A Decade Of Cryptocurrency Investment Literature: A Cluster-Based Systematic Analysis. International Journal Of Financial Studies, 11(2), 71.
[4]. Almeida, J., & Gonçalves, T. C. (2023). A Systematic Literature Review Of Investor Behavior In The Cryptocurrency Markets. Journal Of Behavioral And Experimental Finance, 37, 100785.
[5]. Almeida, J., & Gonçalves, T. C. (2024). Cryptocurrency Market Microstructure: A Systematic Literature Review. Annals Of Operations Research, 332(1), 1035-1068.