A PSO Enable Multi-Hop Clustering Algorithm for VANET

VANET (Vehicle Ad-hoc Network) is an emerging technology in today’s intelligent transport system. In VANET, there are many moving nodes which are called the vehicle running on the road. They communicate with each other to provide the information to driver regarding the road condition, traffic, weather and parking. VANET is a kind of network where moving nodes talk with each other with the help of equipment. There are various other things which also make complete to VANET like OBU (onboard unit), RSU (Road Aside Unit) and CA (Certificate authority). In this paper, a new PSO enable multi-hop technique is proposed which helps in VANET to Select the best route and find the stable cluster head and remove the malicious node from the network to avoid the false messaging. The false can be occurred when there is the malicious node in a network. Clustering is a technique for making a group of the same type node. This proposed work is based on PSO enable clustering and its importance in VANET. While using this approach in VANET, it has increased the 20% packet delivery ratio.

A Secure Energy Aware Game Theory (SEGaT) Mechanism for Coordination in WSANs

A novel secure energy aware game theory (SEGaT) method has proposed to have better coordination in wireless sensor actor networks. An actor has a cluster of sensor nodes which is required to perform different action based on the need that emerge in the network individually or sometime with coordination from other actors. The method has different stages for the fulfilment of these actions. Based on energy aware actor selection (EAAS), selection of number of actors and their approach is the initial step followed by the selection of best team of sensors with each actor to carry out the action and lastly the selection of reliable node within that team to finally nail the action into place in the network for its smooth working and minimum compromise in the energy The simulations are done in MATLAB and result of the energy and the packet delivery ratio are compared with game theory (GaT) and real time energy constraint (RTEC) method. The proposed protocol performs better in terms of energy consumption, packet delivery ratio as compared to its competitive protocols.

Hybridizing Convolutional Neural Network for Classification of Lung diseases

Pulmonary disease is widespread worldwide. There is persistent blockage of the lungs, pneumonia, asthma, TB, etc. It is essential to diagnose the lungs promptly. For this reason, machine learning models were developed. For lung disease prediction, many deep learning technologies, including the CNN, and the capsule network, are used. The fundamental CNN has low rotating, inclined, or other irregular image orientation efficiency. Therefore by integrating the space transformer network (STN) with CNN, we propose a new hybrid deep learning architecture named STNCNN. The new model is implemented on the dataset from the Kaggle repository for an NIH chest X-ray image. STNCNN has an accuracy of 69% in respect of the entire dataset, while the accuracy values of vanilla grey, vanilla RGB, hybrid CNN are 67.8%, 69.5%, and 63.8%, respectively. When the sample data set is applied, STNCNN takes much less time to train at the cost of a slightly less reliable validation. Therefore both specialists and physicians are simplified by the proposed STNCNN System for the diagnosis of lung disease.

Towards Design of an Efficient Sensing Data Acquisition scheme for UAVs-assisted Wireless Sensor Networks

This paper investigates sensing data acquisition issues from large-scale hazardous environments using UAVs-assisted WSNs. Most of the existing schemes suffer from low scalability, high latency, low throughput, and low service time of the deployed network. To overcome these issues, we considered a clustered WSN architecture in which multiple UAVs are dispatched with assigned path knowledge for sensing data acquisition from each cluster heads (CHs) of the network. This paper first presents a non-cooperative Game Theory (GT)-based CHs selection algorithm and load balanced cluster formation scheme. Next, to provide timely delivery of sensing information using UAVs, hybrid meta-heuristic based optimal path planning algorithm is proposed by combing the best features of Dolphin Echolocation and Crow Search meta-heuristic techniques. In this research work, a novel objective function is formulated for both load-balanced CHs selection and for optimal the path planning problem. Results analyses demonstrate that the proposed scheme significantly performs better than the state-of-art schemes.

Defense and Detection of DDOS Attack using Secured Geographic Routing

Due to the absence of routing initiation, the routing protocol requires a secure message transition. The key downside is that there are many current routing protocols. The big downside is the inability of the node to give a message when the attackers are routing. The key attack in the proposed routing model is Distributed Denial of Service (DDOS). The Protected Geographic Routing Protocol (SGRP) is the assured routing carried out in the proposed work. The Protected Geographic Routing Protocol (SGRP) will improve the efficiency of the transmission method by choosing a specific source node. The paper suggested that the Protected Spatial Routing Protocol (PSRP) would recognize and isolate such threats. Several modeling time estimation studies have been carried out to analyze the simulation time and the efficiency of the proposed routing technique. The proposed routing technique demonstrates the performance by calculating the Packets Delivery Ratio(PDR) and Energy consumption. The Routing protocol is used in many applications such as the Industrial Internet of Things (IoT)

Predicting the death rate around the world due to Covid-19 using regression analysis

Nowadays, COVID-19 is considered to be the biggest disaster that the world is facing. It has created a lot of destruction in the whole world. Due to this COVID-19, analysis has been done to predict the death rate and infected rate from the total population. To perform the analysis on COVID-19, regression analysis has been implemented by applying the differential equation and ordinary differential equation (ODE) on the parameters. The parameters taken for analysis are the number of susceptible individuals, the number of Infected Individuals, and the number of Recovered Individuals. This work will predict the total cases, death cases, and infected cases in the near future based on different reproductive rate values. This work has shown the comparison based on 4 different productive rates i.e. 2.45, 2.55, 2.65, and 2.75. The analysis is done on two different datasets; the first dataset is related to China, and the second dataset is associated with the world's data. The work has predicted that by 2020-08-12: 59,450,123 new cases and 432,499,003 total cases and 10,928,383 deaths.

A Voronoi- Ant Colony based Routing (VoR-Ant-R) Algorithm for WMSNs

Wireless Multimedia Sensor Networks (WMSNs) have been used in many applications and powerful distributed systems. But the performance of WMSNs is suffering from the occurrence of energy holes. To improve the performance of the network and packet delivery ratio, a Voronoi-Ant colony based Routing (VoR-Ant-R) algorithm is proposed for WMSNs to discover the energy holes and finds the shortest path from the source to destination in the WMSNs even though faces some obstacles. The WMSNs are constructed using the Voronoi structure to bypass energy holes. After bypassing the energy hole in the path; an ACO is introduced to select a neighborhood node for data forwarding. This ACO constructs the shortest optimized path to enhance the performance of the WMSNs. The proposed work is experimentally compared with other algorithms such as IEEABR, EEABR, SC, and BEES. The simulation results show that VoR-Ant-R can increase energy efficiency, success rate, reduces energy consumption, and latency.

NEW DISCRETE CROW SEARCH ALGORITHM FOR CLASS ASSOCIATION RULE MINING

Associative Classification (AC) or Class Association Rule (CAR) mining is a very efficient method for the classification problem. It can build comprehensible classification models in the form of a list of simple IF-THEN classification rules from the available data. In this paper, we present a new, and improved discrete version of the Crow Search Algorithm (CSA) called NDCSA-CAR to mine the Class Association Rules. The goal of this article is to improve the data classification accuracy and the simplicity of classifiers. The authors applied the proposed NDCSA-CAR algorithm on eleven benchmark dataset and compared its result with traditional algorithms and recent well known rule-based classification algorithms. The experimental results show that the proposed algorithm outperformed other rule-based approaches in all evaluated criteria.

A Novel Discrete Binary Gaining-Sharing knowledge-based Optimization Algorithm for the Travelling Counselling Problem for Utilization of Solar Energy

This article proposes a novel binary version of recently developed Gaining-Sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems is proposed. GSK algorithm is based on the concept of how humans acquire and share knowledge during their life span. Discrete Binary version of GSK named novel binary Gaining Sharing knowledge-based optimization algorithm (DBGSK) depends on mainly two binary stages: binary junior gaining sharing stage and binary senior gaining sharing stage with knowledge factor 1. These two stages enable DBGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space.An improved scheduling of the technical counselling process for utilization of the electricity from solar energy power stations is introduced. The scheduling aims at achieving the best utilization of the available day time for the counselling group,n this regard,a new application problem is presented, which is called a Travelling Counselling Problem (TCP).A Nonlinear Binary Model is introduced with a real application

Logistic Map and Exponential Scaling Factor based Differential Evolution

Differential evolution (DE), an important evolutionary technique, enhances its parameters such as, initialization of population, mutation, crossover etc. to resolve realistic optimization issues. This work represents a modified differential evolution algorithm by using the idea of exponential scale factor and logistic map in order to address the slow convergence rate, and to keep a very good equilibrium linking exploration and exploitation. Modification is done in two ways: (i) Initialization of population and (ii) Scaling factor.The proposed algorithm is validated with the aid of a 13 different benchmark functions taking from the literature, also the outcomes are compared along with 7 different popular state of art algorithms. Further, performance of the modified algorithm is simulated on 3 realistic engineering problems. Also compared with 8 recent optimizer techniques. Again from number of function evaluations it is clear that the proposed algorithm converses more quickly than the other existing algorithms.