Coastal Flood risk assessment using ensemble multi-criteria decision-making with machine learning approaches.

Coastal areas are at a higher risk of flooding, and novel changes in the climate are induced to raise the sea level. Flood acceleration and frequency have increased recently because of unplanned infrastructural conveniences and anthropogenic activities. Therefore, the assessment of flood susceptibility mapping is considered the most significant flood management model. In this paper, flood susceptibility identification is performed by applying the innovative Multi-criteria decision-making model (MCDM) called Analytical Hierarchy Process (AHP) by ensembles with Support vector machine (AHP-SVM) and Decision Tree (AHP-DT). This model combines two Representation concentration pathway (RCP) scenarios such as RCP 2.6 & RCP 8.5. The factors influencing the coastal flooding in Bandar Abbas, Iran, identified through Flood susceptibility mapping. Multi-criteria decision-making (MCDM) has been applied to evaluate the Coastal flood conditioning factors, and ensemble machine learning (ML) approaches are employed for Coastal risk factor (CRF) prediction and classification. The statistical variances are measured through Friedman and Wilcoxon signed rank tests and statistical metrics such as Accuracy, sensitivity, and specificity. Among the models, AHP-DT obtained an improved AUC value of ROC as 0.95. After applying the ML models, the northern and western park of Raidak Basin River recognises very low and low flood susceptibility because of their topographic characteristics. The eastern part of the middle section fell very high and high CFSM. Observed from this result analysis, the people living nearer to the coastline are distributed by the low to medium exposure in the region of the west and middle of the considered study area. The results of this study can help decision-makers take necessary risk reduction approaches in the high-risk flooding zones of the coastal system.

Computer-aided diagnosis for lung cancer using waterwheel plant algorithm with deep learning.

Lung cancer (LC) is a life-threatening and dangerous disease all over the world. However, earlier diagnoses and treatment can save lives. Earlier diagnoses of malevolent cells in the lungs responsible for oxygenating the human body and expelling carbon dioxide due to significant procedures are critical. Even though a computed tomography (CT) scan is the best imaging approach in the healthcare sector, it is challenging for physicians to identify and interpret the tumour from CT scans. LC diagnosis in CT scan using artificial intelligence (AI) can help radiologists in earlier diagnoses, enhance performance, and decrease false negatives. Deep learning (DL) for detecting lymph node contribution on histopathological slides has become popular due to its great significance in patient diagnoses and treatment. This study introduces a computer-aided diagnosis for LC by utilizing the Waterwheel Plant Algorithm with DL (CADLC-WWPADL) approach. The primary aim of the CADLC-WWPADL approach is to classify and identify the existence of LC on CT scans. The CADLC-WWPADL method uses a lightweight MobileNet model for feature extraction. Besides, the CADLC-WWPADL method employs WWPA for the hyperparameter tuning process. Furthermore, the symmetrical autoencoder (SAE) model is utilized for classification. An investigational evaluation is performed to demonstrate the significant detection outputs of the CADLC-WWPADL technique. An extensive comparative study reported that the CADLC-WWPADL technique effectively performs with other models with a maximum accuracy of 99.05% under the benchmark CT image dataset.

Exploiting histopathological imaging for early detection of lung and colon cancer via ensemble deep learning model.

Cancer seems to have a vast number of deaths due to its heterogeneity, aggressiveness, and significant propensity for metastasis. The predominant categories of cancer that may affect males and females and occur worldwide are colon and lung cancer. A precise and on-time analysis of this cancer can increase the survival rate and improve the appropriate treatment characteristics. An efficient and effective method for the speedy and accurate recognition of tumours in the colon and lung areas is provided as an alternative to cancer recognition methods. Earlier diagnosis of the disease on the front drastically reduces the chance of death. Machine learning (ML) and deep learning (DL) approaches can accelerate this cancer diagnosis, facilitating researcher workers to study a vast majority of patients in a limited period and at a low cost. This research presents Histopathological Imaging for the Early Detection of Lung and Colon Cancer via Ensemble DL (HIELCC-EDL) model. The HIELCC-EDL technique utilizes histopathological images to identify lung and colon cancer (LCC). To achieve this, the HIELCC-EDL technique uses the Wiener filtering (WF) method for noise elimination. In addition, the HIELCC-EDL model uses the channel attention Residual Network (CA-ResNet50) model for learning complex feature patterns. Moreover, the hyperparameter selection of the CA-ResNet50 model is performed using the tuna swarm optimization (TSO) technique. Finally, the detection of LCC is achieved by using the ensemble of three classifiers such as extreme learning machine (ELM), competitive neural networks (CNNs), and long short-term memory (LSTM). To illustrate the promising performance of the HIELCC-EDL model, a complete set of experimentations was performed on a benchmark dataset. The experimental validation of the HIELCC-EDL model portrayed a superior accuracy value of 99.60% over recent approaches.