Landslide susceptibility analysis is beneficial information for a wide range of applications. We aimed to explore and compare three machine learning (ML) techniques, namely the random forests (RF), support vector machine (SVM) and multiple layer neural networks (MLP) for landslide susceptibility assessment in the Ahar county of Iran. To achieve this goal, 10 landslide occurrence-related influencing factors were pondered. A sum of 266 locations with landslide potentiality was recognized in the context of the study, and the Pearson correlation technique utilized in order to select the influencing factors in landslide models. The association between landslides and conditioning factors was also evaluated using a probability certainty factor (PCF) model. Three landslide models (SVM, RF, and MLP) were structured by the training dataset. Lastly, the receiver operating characteristic (ROC) and statistical procedures were employed to validate and contrast the predictive capability of the obtained three models. The findings of the study in terms of the Pearson correlation technique method for the importance ranking of conditioning factors in the context area uncovered that slope, aspect, normalized difference vegetation index (NDVI), and elevation have the highest impact on the occurrence of the landslide. All in all, the MLP model had the utmost rate of prediction capability (85.22 %), after which, the SVM model (78.26 %) and the RF model (75.22 %) demonstrated the second and third rates. Besides, the study revealed that benefiting the optimal machine with the proper selection of the techniques could facilitate landslide susceptibility modeling. Manuscript profile

Hybrid connections (bolts, glue) and perforated plates are one of the most important topics in various industries, including aerospace. This type of process occurs due to the growth of small cracks in the metal structure as a result of cyclic or intermittent loading. Since failures occur suddenly, terrible accidents such as plane crashes, shipwrecks, bridge collapses, and toxic radioactive fallout can occur. To prevent these incidents, fatigue tests are performed on a sample of parts that is similar to the real part, so that the fatigue life can be obtained through this method. However, because fatigue tests are time-consuming and expensive, artificial intelligence methods have been used in this research to estimate the fatigue life of hybrid joints and perforated plates. In the experimental part of this research, plates made of aluminum alloy 2024-T3, which is one of the widely used materials in aerospace, the used materials are screws made of Hex head M5 and a special adhesive made of Loctite 3421 (Henkel ltd). Fatigue tests are extracted as input and output data from the related article. Out of a total of 71 fatigue tests, 35 tests were performed for perforated plates, 18 tests for hybrid joints, and 18 tests for bolted joints. Also, according to the number of data, the best result was when 80% of the data was considered for training the network and 20% was used as test data to evaluate the performance of the network. Finally, the predicted output was compared with the actual output and it was seen that the best performance of the neural network was after normalizing the data, that the error value was close to zero. Manuscript profile