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Water is the basic requirement for the survival of life on earth. The water is mainly used for cooking, cleaning and drinking purpose. In which more solid ingredients along with water also reaches. The disposals of sewages are still a challenging task in this modern world. The main purpose of smart drainage system is collecting the solid waste which includes bottles, polythene and transport to the disposal area. Otherwise the solid waste will block the drainage system. To avoid such situation, the solid waste are need to be collected and disposed it into collecting bin. The proposed system will automatically cleans the water in the drainage system with the help of drive system controlled by Arduino and the mechanical setup which are driven by servo motor collect the solid waste and dispose it in waste bucket to avoid drainage block. It reduces the cost of manual labour as well as reduces the threat to human life.

The evolving automotive industry with the rapid growth of using fuel resources causes a shortfall of fuel availability. The engine maintenance has great importance and it is essential to develop a fault detection system and condition monitoring is done to reduce the damage-causing circumstances to improve engine safety. The engine tested at the rated speed of 1800 rpm by varying the loads. Captured vibration data is analyzed by using feature extraction and classification algorithms to obtain the best performance and minimum vibrations of blends. The best classification accuracy blend results are selected and used to develop a neural network model and to predict the vibration signatures. The neural network model is developed with a feedforward backpropagation algorithm and using Levenberg–Marquardt as training function. The developed ANN model is trained and vibration signatures are predicted for the selected blend B25 with the classification accuracy of 97%.

Machine learning involves artificial intelligence, and it is used in solving many problems in data science. One common application of machine learning is the prediction of an outcome based upon existing data. The machine learns patterns from the existing dataset, and then applies them to an unknown dataset in order to predict the outcome. Classification is a powerful machine learning technique that is commonly used for prediction. Some classification algorithms predict with satisfactory accuracy, whereas others exhibit a limited accuracy. This paper investigates a method termed ensemble classification, which is used for improving the accuracy of weak algorithms by combining multiple classifiers. Experiments with this tool were performed using a heart disease dataset. A comparative analytical approach was done to determine how the ensemble technique can be applied for improving prediction accuracy in heart disease. The focus of this paper is not only on increasing the accuracy of weak classification algorithms, but also on the implementation of the algorithm with a medical dataset, to show its utility to predict disease at an early stage. The results of the study indicate that ensemble techniques, such as bagging and boosting, are effective in improving the prediction accuracy of weak classifiers, and exhibit satisfactory performance in identifying risk of heart disease. A maximum increase of 7% accuracy for weak classifiers was achieved with the help of ensemble classification. The performance of the process was further enhanced with a feature selection implementation, and the results showed significant improvement in prediction accuracy. Keywords: Heart disease, Machine learning, Ensemble classifier, Prediction model

Mobile Ad hoc Networks (MANETs) is a continuously self-configuring, infrastructure-less network of mobile devices connected without wires and also it is an open network environment where nodes can join and leave the network freely. Hence, the dynamic natures of MANETs is not secure than the wired networks. To overcome this issue, the Cluster-based Certificate Revocation with Vindication Capability (CCRVC) scheme is proposed. In the meanwhile nodes forms a cluster consists of a Cluster Head with some Cluster Members (CMs) situated within the transmission range of their cluster Head. Each node should have to acquire valid certificates from the Certification Authority (CA) before it can join the network that is responsible for distribution and management of certificates to all nodes and also CA is responsible for updating two lists, Warning list and Black list. Both the lists are used to hold the accusing and accused nodes information, respectively. Experimental results show that the proposed CCRVC scheme is effective and efficient to provide efficient and secure communication.

Phishing is an online scandalous act that occurs when a malevolent web page impersonates as legitimate web page in the intension of exploiting the confidential information from the user. Phishing attack continues to pose serious risk for web users and annoying threat in the field of electronic commerce. Feature selection is the process of removing unrelated features and thus reduces the dimensionality of the feature. This paper focuses on identifying the foremost features that categorise legitimate websites from phishing websites based on feature selection. In real world identifying phishing URL with low computational time and accuracy is very important and thus feature selection is considered in this work. A comparative study is carried out on different data mining classifiers before and after feature selection and the performance are evaluated in terms of accuracy and computational rate. The results indicate that the proposed approach detects phishing websites with considerable accuracy.

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Phishing is an online criminal act that occurs when a malicious webpage impersonates as legitimate webpage so as to acquire sensitive information from the user. Phishing attack continues to pose a serious risk for web users and annoying threat within the field of electronic commerce. This paper focuses on discerning the significant features that discriminate between legitimate and phishing URLs. These features are then subjected to associative rule mining—apriori and predictive apriori. The rules obtained are interpreted to emphasize the features that are more prevalent in phishing URLs. Analyzing the knowledge accessible on phishing URL and considering confidence as an indicator, the features like transport layer security, unavailability of the top level domain in the URL and keyword within the path portion of the URL were found to be sensible indicators for phishing URL. In addition to this number of slashes in the URL, dot in the host portion of the URL and length of the URL are also the key factors for phishing URL.