International Conference Artificial Intelligence, Smart Grid and Smart City Applications 

About: International Conference Artificial Intelligence, Smart Grid and Smart City Applications is an academic conference. The conference publishes majorly in the area(s): Feature selection & Photovoltaic system. Over the lifetime, 87 publications have been published by the conference receiving 118 citations.

Automatic Classification of Solid Waste Using Deep Learning

Abstract: Solid waste management is an essential task to be carried out in day-to-day life. So an automated recognition system using deep learning algorithm has been implemented to classify wastes as biodegradable and non-biodegradable. Efficient segregation of solid wastes helps to reduce the amount of waste buried in the ground, thereby improving the recycling rate, and safeguards the soil from pollution.

A Survey on Supervised and Unsupervised Learning Techniques

Abstract: Supervised learning is the popular version of machine learning. It trains the system in the training phase by labeling each of its input with its desired output value. Unsupervised learning is another popular version of machine learning which generates inferences without the concept of labels. The most common supervised learning methods are linear regression, support vector machine, random forest, naive Bayes, etc. The most common unsupervised learning methods are cluster analysis, K-means, Apriori algorithm, etc. This survey paper gives an overview of supervised algorithms, namely, support vector machine, decision tree, naive Bayes, KNN, and linear regression, and an overview of unsupervised algorithms, namely, K-means, agglomerative divisive, and neural networks.

Passive Fault-Tolerant Control Based on Interval Type-2 Fuzzy Controller for Coupled Tank System

Abstract: In this chapter, a robust controller for a coupled tank-level control is proposed in presence of system component fault. For this purpose, interval type-2 fuzzy logic control approach (IT2FLC) technique is used to design a controller, named passive Fault Tolerant Interval Type-2 Fuzzy Controller (PFTIT2FLC) based on the robust controller to fault tolerant of coupled tank level control system. The proposed control scheme allows avoiding modelling, reducing the rules number of the fuzzy controller. The simulation results show that the PFIT2FLC can provide good tracking performance, even in presence of system component faults.

Analysis on DGHV and NTRU Fully Homomorphic Encryption Schemes

Abstract: Homomorphic encryption (HE) is an emerging scheme that allows computation over encrypted data. The standard encryption algorithms like RSA, Elgamal, etc. help in protecting confidential data from attackers rather than performing computation over encrypted data. Fully homomorphic encryption (FHE) permits computation to perform upon encrypted data unlimitedly in server side than in computational node. In this paper, the basic DGHV FHE scheme and NTRU FHE scheme are analyzed to preserve the security and privacy of the data. DGHV performs computing over real integers, while NTRU in a truncated polynomial ring. A detailed investigation of both the schemes is based on the storage and noise reduction that best suits for a real-world application.

Android Malware Detection

Abstract: Smartphones and mobile tablets are rapidly becoming essential in daily life. Android has been the most popular mobile operating system since 2012. However, owing to the open nature of Android, countless malwares are intermingled with a large number of benign apps in Android markets that seriously threaten Android security. The botnet is an example of using good technologies for bad intentions. A botnet is a collection of Internet-connected devices, each of which is running one or more bots. The Bot devices include PCs, Internet of Things, mobile devices, etc. Botnets can be used to perform Distributed Denial of Service (DDoS attack), steal data, send spam and allow the attacker access to the device and its connection. To ensure the security of mobile devices, malwares have to be resolved. Malware analysis can be carried out using techniques like static, dynamic, behavioural, hybrid and code analysis. In this chapter, several machine learning techniques and classifiers are used to categorize mobile botnet detection.