Huong Thi Thanh Ngo¹, Nguyen Duc Dam¹, Quynh-Anh Thi Bui¹, Nadhir Al-Ansari^2,*, Romulus Costache^3,4,*, Hang Ha⁵, Quynh Duy Bui⁵, Sy Hung Mai⁶, Indra Prakash⁷, Binh Thai Pham¹

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2219-2241, 2023, DOI:10.32604/cmes.2023.022566

Abstract Flash floods are one of the most dangerous natural disasters, especially in hilly terrain, causing loss of life, property, and infrastructures and sudden disruption of traffic. These types of floods are mostly associated with landslides and erosion of roads within a short time. Most of Vietnam is hilly and mountainous; thus, the problem due to flash flood is severe and requires systematic studies to correctly identify flood susceptible areas for proper landuse planning and traffic management. In this study, three Machine Learning (ML) methods namely Deep Learning Neural Network (DL), Correlation-based Feature Weighted Naive Bayes (CFWNB), and Adaboost (AB-CFWNB) were… More >

Monika Saraswat^*, A. K. Wadhwani, Sulochana Wadhwani

Journal on Artificial Intelligence, Vol.4, No.2, pp. 61-76, 2022, DOI:10.32604/jai.2022.028140

Abstract Today, more families are affected by Diabetes Mellitus (DM) disease on account of its continually increasing occurrence. Most patients remain unknown about their health quality or the DM’s risk factors prior to diagnosis. The medical world has witnessed that individuals are affected by two different diabetes namely a) Type-1 diabetes (T1D), as well as b) Type-2 diabetes (T2D). As Type 2 Diabetes affects the other organs of the body, the proposed system concentrates specifically on Type 2 Diabetes. This work aims to ascertain the cardiac disorder in T2D patients. As of the ECG dataset, the requisite data is gathered it… More >

P. Arunachalam¹, N. Janakiraman^1,*, Arun Kumar Sivaraman², A. Balasundaram³, Rajiv Vincent², Sita Rani⁴, Barnali Dey⁵, A. Muralidhar², M. Rajesh²

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1241-1259, 2022, DOI:10.32604/iasc.2022.022573

Abstract Digital clinical histopathology technique is used for accurately diagnosing cancer cells and achieving optimal results using Internet of Things (IoT) and blockchain technology. The cell pattern of Synovial Sarcoma (SS) cancer images always appeared as spindle shaped cell (SSC) structures. Identifying the SSC and its prognostic indicator are very crucial problems for computer aided diagnosis, especially in healthcare industry applications. A constructive framework has been proposed for the classification of SSC feature components using Support Vector Machine (SVM) with the assistance of relevant Support Vectors (SVs). This framework used the SS images, and it has been transformed into frequency sub-bands… More >