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• Develop an Adaptive Parallel Seeker Optimization based Routing Technique • Employ weight based clustering scheme for proper cluster head selection • Derives a fitness function for APSO algorithm to optimally choose routes • Validate the performance of APSO-EARPT technique under different measures Smart city is a phenonmenon that integrates physical and social infrastructures with Information Technology to keep a city's cooperative intelligence under control. Smart cities primarily rely on Wireless Sensor Networks (WSN) to manage and maintain its service offerings. In literature, clustering and multihop routing techniques have been proposed, validated and implemented to reduce the consumption of energy in the network. With this motivation, the current study develops Adaptive Parallel Seeker Optimization-based Energy Aware Route Planning Technique (APSO-EARPT) for clustered WSN in smart cities. The presented APSO-EARPT technique concentrates on appropriate selection of Cluster Heads (CHs) and optimal routes in WSN. To accomplish this, APSO-EARPT model encompasses Weight-Based Clustering Scheme (WBCS) for effective selection of CHs. Then, routing process is performed with the help of APSO algorithm. The proposed APSO-EARPT technique computes a Fitness Function (FF) that comprises of three variables such as Residual Energy (RE), distance to Base Station (BS), and node degree. This fitness function helps in optimal selection of routes in WSN. In order to validate the supremacy of the proposed APSO-EARPT model in terms of network lifespan and energy efficiency, simulations were conducted and the results confirmed the excellent performance of the proposed model. [Display omitted] [ABSTRACT FROM AUTHOR]

Recently, distributed generators (DG) and electric vehicles (EV) are commonly employed in recent days in spite of the power flow of the distributed network (DN) being influenced by the intermittency and arbitrariness of DGs and electric vehicles (EVs). The massive increase in the distinct varieties of controllable devices has complicated controlling needs and is combined in the DN. Reactive power optimization (RPO) of the DNs helps to minimize the power loss, enhance the voltage quality, and inexpensive functioning of the DNs. ROR can be considered as a complicated high dimension non-linearity problem. The recent advances of data science approach containing machine learning (ML) and deep learning (DL) aid to make effective decisions. With this motivation, this paper presents a parameter tuned deep learning model for reactive power optimization (PTDL-RPO) in distributed systems. The proposed model involves a bi-directional long short term memory (Bi-LSTM) technique for learning the non-linear complex relationships among the system characteristics and reactive power control solution. The past DG data are used for training the Bi-LSTM model for learning the relativity among the system characteristics and reactive power control solution. Besides, for improving the performance of Bi-LSTM method, search and rescue optimization algorithm (SROA) was employed as a hyperparameter tuning technique. The design of optimal BiLSTM model for RPO of the DNs show the novelty of the study. For examining the enhanced outcomes of the PTDL-RPO technique, a wide range of simulations take place and the experimental outcome highlighted the enhanced performance of the PTDL-RPO technique. • Present a deep learning model for reactive power optimization in distributed system. • Employ Bidirectional long short term memory to learn nonlinear complex relationship. • Design a hyperparameter optimizer using search and rescue optimization algorithm. • Validate the performance of proposed model on IEEE-37 node distribution network. [ABSTRACT FROM AUTHOR]

Automatic fruit classification is an interesting problem in retail supply chain processes and in fruit industry, since the process can help the supermarkets as well as the fruit growers find various fruits and their statuses from the containers or stock. In this background, the current research article designs an Automated Fruit Classification system using Enhanced Tunicate Swarm Algorithm with Fusion-based Deep Learning (AFC-ETSAFDL) technique. The goal of the proposed AFC-ETSAFDL technique is to recognize different types of fruits from the captured images. In the presented technique, a fusion-based feature extraction method is applied using three DL models namely, DenseNet, ResNet, and Inception v3 with ETSA as a hyperparameter optimizer. For fruit classification, the Extreme Gradient Boosting (XGBoost) model is used in this study. The proposed model was experimentally evaluated using an open-access fruit dataset and the results showcase the enhanced performance of the proposed technique over other existing models. [ABSTRACT FROM AUTHOR]