26 research outputs found
Aerial Vehicles
This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space
Intelligent Circuits and Systems
ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.  This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering
Computational Intelligence in Healthcare
This book is a printed edition of the Special Issue Computational Intelligence in Healthcare that was published in Electronic
Computational Intelligence in Healthcare
The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications
Applied Metaheuristic Computing
For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC
Applied Methuerstic computing
For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC
Intrusion detection system for IoT networks for detection of DDoS attacks
PhD ThesisIn this thesis, a novel Intrusion Detection System (IDS) based on the hybridization of the
Deep Learning (DL) technique and the Multi-objective Optimization method for the detection
of Distributed Denial of Service (DDoS) attacks in Internet of Things (IoT) networks is
proposed. IoT networks consist of different devices with unique hardware and software
configurations communicating over different communication protocols, which produce huge
multidimensional data that make IoT networks susceptible to cyber-attacks. The network IDS
is a vital tool for protecting networks against threats and malicious attacks. Existing systems
face significant challenges due to the continuous emergence of new and more sophisticated
cyber threats that are not recognized by them, and therefore advanced IDS is required.
This thesis focusses especially on the DDoS attack that is one of the cyber-attacks that has
affected many IoT networks in recent times and had resulted in substantial devastating losses.
A thorough literature review is conducted on DDoS attacks in the context of IoT networks,
IDSs available especially for the IoT networks and the scope and applicability of DL
methodology for the detection of cyber-attacks. This thesis includes three main contributions
for 1) developing a feature selection algorithm for an IoT network fulfilling six important
objectives, 2) designing four DL models for the detection of DDoS attacks and 3) proposing a
novel IDS for IoT networks. In the proposed work, for developing advanced IDS, a Jumping
Gene adapted NSGA-II multi-objective optimization algorithm for reducing the dimensionality
of massive IoT data and Deep Learning model consisting of a Convolutional Neural Network
(CNN) combined with Long Short-Term Memory (LSTM) for classification are employed. The
experimentation is conducted using a High-Performance Computer (HPC) on the latest
CISIDS2017 datasets for DDoS attacks and achieved an accuracy of 99.03 % with a 5-fold
reduction in training time. The proposed method is compared with machine learning (ML)
algorithms and other state-of-the-art methods, which confirms that the proposed method
outperforms other approaches.Government of Indi
Advanced Modeling and Research in Hybrid Microgrid Control and Optimization
This book presents the latest solutions in fuel cell (FC) and renewable energy implementation in mobile and stationary applications. The implementation of advanced energy management and optimization strategies are detailed for fuel cell and renewable microgrids, and for the multi-FC stack architecture of FC/electric vehicles to enhance the reliability of these systems and to reduce the costs related to energy production and maintenance. Cyber-security methods based on blockchain technology to increase the resilience of FC renewable hybrid microgrids are also presented. Therefore, this book is for all readers interested in these challenging directions of research