Flight planning, as one of the challenging issues in the industrial world, is faced with many uncertain conditions. One such condition is delay occurrence, which stems from various factors and imposes considerable costs on airlines, operators, and travelers. With these considerations in mind, we implemented flight delay prediction through the proposed approaches that were based on machine learning algorithms. The parameters that enabled effective estimation of delay were identified and then, Bayesian modeling, decision tree, cluster classification, random forest, and hybrid method were applied to estimate the occurrences and magnitude of delay in a network. These methods were tested on a US flight dataset and then, refined for a large Iranian airline network. Results showed that the parameters affecting delay in US networks were visibility, wind, and departure time, whereas those affecting delay in the Iranian airline flights were fleet age and aircraft type. The proposed approaches exhibited an accuracy of more than 70% in calculating delay occurrence and magnitude for both the US and Iranian networks. It is hoped that the techniques put forward in this work will enable airline companies to accurately predict delays, improve flight planning, and prevent delay propagation.

Granular computing has emerged as a new computational method that is beneficial when dealing with large amounts of data. In recent years, several machine learning models based on the granular framework have been developed, outperforming traditional machine learning models. This article reviews some newly developed techniques in terms of granular framework settings. 

Modern studies in the field of environment science and engineering show that deterministic models struggle to capture the relationship between the concentration of atmospheric pollutants and their emission sources. The recent advances in statistical modeling based on machine learning approaches have emerged as solution to tackle these issues. It is a fact that, input variable type largely affect the performance of an algorithm, however, it is yet to be known why an algorithm is preferred over the other for a certain task. The work aims at highlighting the underlying principles of machine learning techniques and about their role in enhancing the prediction performance. The study adopts, 38 most relevant studies in the field of environmental science and engineering which have applied machine learning techniques during last 6 years. The review conducted explores several aspects of the studies such as: 1) the role of input predictors to improve the prediction accuracy; 2) geographically where these studies were conducted; 3) the major techniques applied for pollutant concentration estimation or forecasting; and 4) whether these techniques were based on Linear Regression, Neural Network, Support Vector Machine or Ensemble learning algorithms. The results obtained suggest that, machine learning techniques are mainly conducted in continent Europe and America. Furthermore a factorial analysis named multicomponent analysis performed show that pollution estimation is generally performed by using ensemble learning and linear regression based approaches, whereas, forecasting tasks tend to implement neural networks and support vector machines based algorithms.

Breast cancer has been the riskiest malignancy among ladies around the world. Nearly 2 million new cases were diagnosed in 2018. The main problem in the detection of breast cancer is to find how tumors turn into malignant or benign and we can do this with the help of machine learning techniques as they provide an appropriate result. According to research, an experienced physician can diagnose cancer with 79% accuracy while using machine learning techniques provides an accuracy of 91%. In this work, machine learning techniques have been applied which include K-Nearest Neighbors algorithm (KNN), Support Vector Machine (SVM), and Decision Tree Classifier (DT). To predict whether the cause is benign or malignant we have used the breast cancer dataset. The SVM classifier gives more accurate and precise results as compared to others, and this classifier is trained with the larger datasets.

The steadily growing dependency over network environment introduces risk over information flow. The continuous use of various applications makes it necessary to sustain a level of security to establish safe and secure communication amongst the organizations and other networks that is under the threat of intrusions. The detection of Intrusion is the major research problem faced in the area of information security, the objective is to scrutinize threats or intrusions to secure information in the network Intrusion detection system (IDS) is one of the key to conquer against unfamiliar intrusions where intruders continuously modify their pattern and methodologies. In this paper authors introduces Intrusion detection system (IDS) framework that is deployed over KDD Cup99 dataset by using machine learning algorithms as Support Vector Machine (SVM), Naï ve Bayes and Random Forest for the purpose of improving the precision, accuracy and recall value to compute the best suited algorithm.

Background and objective: Currently, diabetes is one of the leading causes of death in the world. According to several factors diagnosis of this disease is complex and prone to human error. This study aimed to analyze the risk of having diabetes based on laboratory information, life style and, family history with the help of machine learning algorithms. When the model is trained properly, people can examine their risk of having diabetes. Material and Methods: To classify patients, by using Python, eight different machine learning algorithms (Logistic Regression, Nearest Neighbor, Decision Tree, Random Forest, Support Vector Machine, Naive Bayesian, Neural Network and Gradient Boosting) were analysed. were evaluated by accuracy, sensitivity, specificity and ROC curve parameters. Results: The model based on the gradient boosting algorithm showed the best performance with a prediction accuracy of %95. 50. Conclusion: In the future, this model can be used for diagnosis diabete. The basis of this study is to do more research and develop models such as other learning machine algorithms.