Botnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet

Ensuring security, integrity, and reliability of the election process consider as the main challenges in the electronic voting system. This paper describes the e-voting system by integrating the biometric authentication, advanced encryption, and watermarking techniques towards meeting such challenges. The system employs the fingerprint authentication by utilizing the Scale-Invariant Feature Transform (SIFT) for verifying the identity of the voter to ensure genuineness and non-repudiation of the service. The vote will be encrypted with the AES-GCM technique to be employed in securing the voting process, thus ensuring both data privacy and integrity. Hybrid Blind Watermarking employs the technique of Discrete Wavelet Transform (DWT) a

Smart cities have recently undergone a fundamental evolution that has greatly increased their potentials. In reality, recent advances in the Internet of Things (IoT) have created new opportunities by solving a number of critical issues that are allowing innovations for smart cities as well as the creation and computerization of cutting-edge services and applications for the many city partners. In order to further the development of smart cities toward compelling sharing and connection, this study will explore the information innovation in smart cities in light of the Internet of Things (IoT) and cloud computing (CC). IoT data is first collected in the context of smart cities. The data that is gathered is uniform. The Internet of Things,

Becloud stupefy computing is solid buzzword in the trade. It is timeless in which the advantage seat be leveraged on sound out miserable take into consideration reducing the indict and complication of grant providers. Cloud computing promises to curtail truly and opinionated retrench and approximately specifically concede IT departments focusing on moral projects as contrasted with of misery datacenters contention, It is unconditionally with than on the up internet. Give are sundry consequences of this put together. For the actuality remodeling in turn flock cause get revenge buyer be attractive to. This implies ramble they chaperone custody of servers, they carry out software updates and assistant on the condense user pay

In the last years, a new technology called Cloud computing has been developed. Empirical and previous studies, commonly examined in business field and other domains. In this study, the significant factors that affecting the adoption of cloud computing have been examined using a frequency analysis that have been explored by the previous studies. The results showed that the most effected factors were relative advantage which followed by security and privacy, complexity, innovativeness, and external support. In this study the model of technology organization-environment was used to examine the significant factors that affecting the adoption of cloud computing.

Stereolithography (SLA) has become an essential photocuring 3D printing process for producing parts of complex shapes from photosensitive resin exposed to UV light. The selection of the best printing parameters for good accuracy and surface quality can be further complicated by the geometric complexity of the models. This work introduces multiobjective optimization of SLA printing of 3D dental bridges based on simple CAD objects. The effect of the best combination of a low-cost resin 3D printer’s machine parameter settings, namely normal exposure time, bottom exposure time and bottom layers for less dimensional deviation and surface roughness, was studied. A multiobjective optimization method was utilized, combining the Taguchi me

In this work, a new development of predictive voltage-tracking control algorithm for Proton Exchange Membrane Fuel Cell (PEMFCs) model, using a neural network technique based on-line auto-tuning intelligent algorithm was proposed. The aim of proposed robust feedback nonlinear neural predictive voltage controller is to find precisely and quickly the optimal hydrogen partial pressure action to control the stack terminal voltage of the (PEMFC) model for N-step ahead prediction. The Chaotic Particle Swarm Optimization (CPSO) implemented as a stable and robust on-line auto-tune algorithm to find the optimal weights for the proposed predictive neural network controller to improve system performance in terms of fast-tracking de

In current article an easy and selective method is proposed for spectrophotometric estimation of metoclopramide (MCP) in pharmaceutical preparations using cloud point extraction (CPE) procedure. The method involved reaction between MCP with 1-Naphthol in alkali conditions using Triton X-114 to form a stable dark purple dye. The Beer’s law limit in the range 0.34-9 μg mL^-1 of MCP with r =0.9959 (n=3) after optimization. The relative standard deviation (RSD) and percentage recoveries were 0.89 %, and (96.99–104.11%) respectively. As well, using surfactant cloud point extraction as a method to extract MCP was reinforced the extinction coefficient(ε) to 1.7333×105L/mol.cm in surfactant-rich phase. The small volume of organi