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"An Optimized Bio-inspired Localization Routing Technique for Sustainab" by Premkumar Chithaluru, Fadi Al-Turjman et al.

The industrial Internet of Things (IIoTs) network life is shortened due to sensor node (SN) energy limitations and computational capability. As a result, optimum node location estimation and efficient energy usage are two critical IIoT requirements. This work reduces energy consumption by performing node localization and cluster-based routing using an improved evolutionary algorithm called Cat Swarm Optimization (CSO). First, the CSO method is used to optimize the bio-inspired node's location. Second, to conserve SN energy in the IIoT network, a cluster-based routing technique is used. The objective function is defined as minimizing the average distance between the cluster and its SNs while selecting the most energy-efficient Cluster Head (CH). In terms of fitness value, the Improved CSO (ICSO) algorithm outperforms the Particle Swarm Optimization (PSO) algorithm. In this paper, real-time test-bed analysis was used to investigate the performance of both node localization and energ ....

Cat Swarm Optimization , Cluster Head , Particle Swarm Optimization , Bio Inspired , Green Cities ,

New grid forming inverter concept based on bee colony algorithm

New grid forming inverter concept based on bee colony algorithm
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Monte Carlo , Inha University , Fractional Order , Particle Swarm Optimization , Gray Wolf Optimization , Monte Carlo Artificial Bee Colony Optimization ,

MyJournals.org - Science - 'Energies, Vol. 16, Pages 5070: A Bi-Level Optimization and Scheduling Strategy for Charging Stations Considering Battery Degradation' (Energies)

MyJournals.org - Science - Energies, Vol. 16, Pages 5070: A Bi-Level Optimization and Scheduling Strategy for Charging Stations Considering Battery Degradation (Energies) ....

Particle Swarm Optimization , Sequential Quadratic Programming , Bi Level ,

"Traffic flow modelling of long and short trucks using a hybrid artific" by Isaac Oyeyemi Olayode, Bo Du et al.

The significance of intelligent transportation systems and artificial intelligence in road transportation networks has made the prediction of traffic flow a subject of discussion among transportation engineers, urban planners, and researchers in the last decade. However, limited research has been done on traffic flow modelling of long and short trucks considering that they are among the major causes of traffic congestions and traffic-related accidents on freeways, especially freeway collisions between them and passengers’ vehicles. This study focused on long and short trucks traffic flow on the N1freeway in South Africa due to its high traffic volume and persistent traffic congestions caused by trucks. We obtained traffic data from this freeway using inductive loop detectors and video cameras. Traffic flow variables such as speed, time, traffic density, and traffic volume were identified, and the traffic datasets comprising 920 datasets were divided into 70% for training and 30% for ....

South Africa , Artificial Neural Network , Long Truck , Particle Swarm Optimization , Hort Truck , Traffic Flow ,

"A COVID-19 X-ray image classification model based on an enhanced convo" by Ashwini Kumar Pradhan, Debahuti Mishra et al.

The classification of medical images is significant among researchers and physicians for the early identification and clinical treatment of many disorders. Though, traditional classifiers require more time and effort for feature extraction and reduction from images. To overcome this problem, there is a need for a new deep learning method known as Convolution Neural Network (CNN), which shows the high performance and self-learning capabilities. In this paper,to classify whether a chest X-ray (CXR) image shows pneumonia (Normal) or COVID-19 illness, a test-bed analysis has been carried out between pre-trained CNN models like Visual Geometry Group (VGG-16), VGG-19, Inception version 3 (INV3), Caps Net, DenseNet121, Residual Neural Network with 50 deep layers (ResNet50), Mobile-Net and proposed CNN classifier. It has been observed that, in terms of accuracy, the proposed CNN model appears to be potentially superior to others. Additionally, in order to increase the performance of the CNN cl ....

Convolution Neural Network , Geometry Group , Neural Network , Visual Geometry Group , Caps Net , Residual Neural Network , Hill Climbing Algorithm , Particle Swarm Optimization , Receiver Operating Characteristic Curve , Area Under , Covid 19 , Ill Climbing Algorithms , Image Classification , Ailored Convolutional Neural Network , X Ray Images ,