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Driver Identification Using Optimized Deep Learning Model in Smart Transportation

ACM TRANSACTIONS ON INTERNET TECHNOLOGY 2022年第4期22卷 1–17页

作者： Ravi, Chandrasekar Tigga, Anmol Reddy, G. Thippa Hakak, Saqib Alazab, Mamoun Natl Inst Technol Puducherry Dept Comp Sci & Engn Karaikal 609609 India Natl Inst Technol Puducherry Comp Sci & Engn Karaikal 609609 India Vellore Inst Technol Sch Informat Technol & Engn Vellore 632014 Tamil Nadu India Univ New Brunswick Fac Comp Sci Canadian Inst Cybersecur Fredericton NB E3B 5A3 Canada Charles Darwin Univ Coll Engn IT & Environm Darwin NT 0909 Australia

The Intelligent Transportation System (ITS) is said to revolutionize the travel experience by making it safe, secure, and comfortable for the people. Although vehicles have been automated up to a certain extent, it still has critical security issues that require thorough study and advanced solutions. The security vulnerabilities of ITS allows the attacker to steal the vehicle. Therefore, the identification of drivers is required in order to develop a safe and secure system so that the vehicles can be protected from theft. There are two ways in which a driver can be identified: 1) face recognition of the driver, and 2) based on driving behavior. Face recognition includes image processing of 2-D images and learning of the features, which require high computational power. Drivers are known to have unique driving styles, whose data can be captured by the sensors. Therefore, the second method identifies drivers based on the analysis of the sensor data and it requires comparatively lesser computational power. In this paper, an optimized deep learning model is trained on the sensor data to correctly identify the drivers. The Long Short-Term Memory (LSTM) deep learning model is optimized for better performance. The novelty of the approach in this work is the inclusion of hyperparameter tuning using a nature-inspired optimization algorithm, which is an important and essential step in discovering the optimal hyperparameters for training the model which in turn increases the accuracy. The CAN-BUS dataset is used for experimentation and evaluation of the training model. Evaluation parameters such as accuracy, precision score, F1 score, and ROC AUC curve are considered to evaluate the performance of the model.

关键词： Intelligent Transportation Systems (ITS) security Auto -theft systems LSTM hyperparameter tuning crow search algorithm

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Coordinated flexible energy and self-healing management according to the multi-agent system-based restoration scheme in active distribution network

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IET RENEWABLE POWER GENERATION 2021年第8期15卷 1765-1777页

作者： Bagherzadeh, Leila Shayeghi, Hossein Pirouzi, Sasan Shafie-khah, Miadreza Catalao, Joao P. S. Univ Mohaghegh Ardabili Energy Management Res Ctr Ardebil Iran Islamic Azad Univ Semirom Branch Fac Engn Esfahan Iran Univ Vaasa Sch Technol & Innovat Flexible Energy Resources Vaasa 65200 Finland Univ Porto Fac Engn P-4200465 Porto Portugal INESC TEC P-4200465 Porto Portugal

This study presents the optimal model of the coordinated flexible energy and self-healing management (C-FE&SH-M) in the active distribution network (ADN) including renewable energy sources (RESs), electric vehicles (EVs) and demand response program (DRP).The flexible energy management (FEM) is extracted using coordination between the RESs, EVs and DRP. The self-healing method (SHM) is related to multi-agent system-based restoration process (MAS-based RP) that finds the optimal restoration pattern at the fault condition according to the different zone agents (ZAs) distributing along with the network. This method minimizes the difference between energy cost and flexibility benefit related to the FEM part and difference between the number of switching operation and priority loads restored based on the SHM part. Also, this problem subjects to power flow equations, RESs and active loads constraints, restoration process formulation and system operation limits. Stochastic programming is used to model the uncertainty of loads, energy prices, RESs and EVs. Hereupon, the suggested strategy is implemented on the 33-bus radial distribution network and it is solved by the crow search algorithm (CSA). Ultimately, the obtained results imply the high flexibility and security of the operation, incorporating the proposed strategy, and delineate the optimal restoration scheme for the ADN.

关键词： electric vehicles MAS RESs 33-bus radial distribution network power flow equations power engineering computing Optimisation techniques DRP distribution networks Distribution networks search problems switching operation coordinated flexible energy Combinatorial mathematics load uncertainty Power engineering computing active loads constraints load flow power system restoration energy prices CSA multi-agent systems fault condition self-healing management Transportation C-FE Power system management, operation and economics FEM crow search algorithm SHM flexible energy management stochastic programming ADN optimal restoration scheme distributed power generation active distribution network demand response program priority loads demand side management multiagent system-based restoration scheme SH-M renewable energy sources EVs Distributed power generation optimal restoration pattern

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Paddy Leaf Disease Detection Using an Optimized Deep Neural Network

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Computers, Materials & Continua 2021年第7期68卷 1117-1128页

作者： Shankaranarayanan Nalini Nagappan Krishnaraj Thangaiyan Jayasankar Kalimuthu Vinothkumar Antony Sagai Francis Britto Kamalraj Subramaniam Chokkaligam Bharatiraja Department of Computer Science&Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India School of Computing SRM Institute of Science and TechnologyKattankulathur603203India Department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India Department of Electronics&Communication Engineering SSM Institute of Engineering and TechnologyDindigul624002India Department of Mechanical Engineering Rohini College of Engineering and TechnologyPalkulam629401India Department of ECE Karpagam Academy of Higher EducationCoimbatore641021India Department of Electrical and Electronics Engineering SRM Institute of Science and TechnologyChennai603203India

Precision Agriculture is a concept of farm management which makes use of IoT and networking concepts to improve the *** diseases are one of the underlying causes in the decrease in the number of quantity and quality of the farming *** of diseases from the plant images is an active research topic which makes use of machine learning(ML)approaches.A novel deep neural network(DNN)classification model is proposed for the identification of paddy leaf disease using plant image *** errors were minimized by optimizing weights and biases in the DNN model using a crow search algorithm(CSA)during both the standard pre-training and fine-tuning *** DNN-CSA architecture enables the use of simplistic statistical learning techniques with a decreased computational workload,ensuring high classification *** leaf images were first preprocessed,and the areas indicative of disease were initially extracted using a k-means clustering *** was then applied to eliminate regions not indicative of ***,a set of features were extracted from the previously isolated diseased ***,the classification accuracy and efficiency of the proposed DNN-CSA model were verified experimentally and shown to be superior to a support vector machine with multiple cross-fold validations.

关键词： Leaf classification paddy leaf deep learning metaheuristics optimization crow search algorithm

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Optimal scheduling in demand-side management based grid-connected microgrid system by hybrid optimization approach considering diverse wind profiles

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ISA TRANSACTIONS 2023年 139卷 357-375页

作者： Basak, Sourav Bhattacharyya, Biplab IIT ISM Dept Elect Engn Dhanbad India

Demand side management (DSM) is one of the trending economic strategies which shifts the elastic demand to the off-peak hours from the peak hours so as to reduce the overall generation cost of the system. The work done in this paper can be categorized in three phases. In the first phase, various wind speed to power conversion mathematical models available in literature are analysed to find out the one with maximum level of wind penetration. For second phase, an economic DSM strategy is implemented to restructure the forecasted load demand model for various participation levels. In the final phase the cost-effective optimization of two microgrid distribution systems are percolated. As an optimization tool, novel hybrid CSAJAYA has been used to carry on the study. Different types of grid participating and pricing strategies along with valve point loading effect and wind energy uncertainty are considered to amplify the complexity and practicality of the study. The generation costs reduced from 3 to 5% when the forecasted demand was reformed with 20% DSM participation for both the test systems. A detailed comparison with the results from various optimization tools studied confirms the effectiveness of the proposed hybrid approach. The hybrid optimization tool presented in this paper performs better in terms of central tendencies, nonparametric statistical analysis, and algorithm execution time.& COPY;2023 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： crow search algorithm Demand Side Management Energy management Jaya algorithm Microgrid Wind Energy

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Capacitor placement in distribution systems for power loss reduction and voltage improvement: a new methodology

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IET GENERATION TRANSMISSION & DISTRIBUTION 2016年第14期10卷 3631-3638页

作者： Askarzadeh, Alireza Grad Univ Adv Technol Inst Sci & High Technol & Environm Sci Dept Energy Management & Optimizat Kerman Iran

For compensating reactive power, shunt capacitors are often installed in electrical distribution networks. Consequently, in such systems, power loss reduces, voltage profile improves and feeder capacity releases. However, finding optimal size and location of capacitors in distribution networks is a complex combinatorial optimisation problem. In such problem, an objective function which is usually defined based on power losses and capacitor installation costs should be minimised subject to operational limitations. In this study, a newly developed metaheuristic technique, named crow search algorithm (CSA), is proposed for finding the optimal placement of the capacitors in a distribution network. CSA is a population-based technique inspired by the greedy behaviour of crows in finding better food sources. The main reasons of using CSA are its easy implementation, few parameters to adjust, fast convergence speed and high efficiency. In the case studies, simulated results indicate that the proposed CSA produces more accurate results than the other studied search methods.

关键词： reactive power distribution networks search problems combinatorial mathematics power capacitors optimisation capacitor placement distribution systems power loss reduction voltage improvement reactive power compensation shunt capacitors electrical distribution networks voltage profile feeder capacity complex combinatorial optimisation problem objective function capacitor installation costs metaheuristic technique crow search algorithm CSA population-based technique food sources

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Smart IoT in Breast Cancer Detection Using Optimal Deep Learning

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JOURNAL OF DIGITAL IMAGING 2023年第4期36卷 1489-1506页

作者： Majji, Ramachandro Prakash, P. G. Om Rajeswari, R. Cristin, R. Vardhaman Coll Engn Dept Informat Technol Hyderabad Telangana India SRM Inst Sci & Technol Sch Comp Dept Computat Intelligence Chennai Tamil Nadu India Rajalakshmi Inst Technol Dept Elect & Commun Engn Chennai India GMR Inst Technol Dept Comp Sci & Engn Rajam Andhra Prades India

IoT in healthcare systems is currently a viable option for providing higher-quality medical care for contemporary e-healthcare. Using an Internet of Things (IoT)-based smart healthcare system, a trustworthy breast cancer classification method called Feedback Artificial crow search (FACS)-based Shepherd Convolutional Neural Network (ShCNN) is developed in this research. To choose the best routes, the secure routing operation is first carried out using the recommended FACS while taking fitness measures such as distance, energy, link quality, and latency into account. Then, by merging the crow search algorithm (CSA) and Feedback Artificial Tree, the produced FACS is put into practice (FAT). After the completion of routing phase, the breast cancer categorization process is started at the base station. The feature extraction step is then introduced to the pre-processed input mammography image. As a result, it is possible to successfully get features including area, mean, variance, energy, contrast, correlation, skewness, homogeneity, Gray Level Co-occurrence Matrix (GLCM), and Local Gabor Binary Pattern (LGBP). The quality of the image is next enhanced through data augmentation, and finally, the developed FACS algorithm's ShCNN is used to classify breast cancer. The performance of FACS-based ShCNN is examined using six metrics, including energy, delay, accuracy, sensitivity, specificity, and True Positive Rate (TPR), with the maximum energy of 0.562 J, the least delay of 0.452 s, the highest accuracy of 91.56%, the higher sensitivity of 96.10%, the highest specificity of 91.80%, and the maximum TPR of 99.45%.

关键词： Breast cancer Internet of Things crow search algorithm Feedback Artificial Tree And Shepherd Convolutional Neural Network

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Artificial intelligence-based super learner approach for prediction and optimization of biodiesel synthesis-A case of waste utilization

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INTERNATIONAL JOURNAL OF ENERGY REsearch 2022年第14期46卷 20519-20534页

作者： Hossain, S. M. Z. Sultana, Nahid Irfan, Muhammad Faisal Haque, S. Manirul Nasr, Nawaf Razzak, Shaikh Abdur Univ Bahrain Dept Chem Engn Coll Engn Zallaq Bahrain Imam Abdulrahman Bin Faisal Univ Dept Comp Sci Coll Comp Sci & Informat Technol Dammam Saudi Arabia Jubail Ind Coll Dept Chem & Proc Engn Technol Jubail Ind City Saudi Arabia King Fahd Univ Petr & Minerals Dept Chem Engn Dhahran Saudi Arabia King Fand Univ Petr & Minerals Ctr Membranes & Water Secur Dhahran Saudi Arabia

In this article, super learner approaches such as hybrid Bayesian Optimization algorithm-Support Vector Regression (BOA-SVR), Bayesian Optimization algorithm-Boosted Regression Tree (BOA-BRT), along with a statistical method (response surface methodology, RSM) were utilized as potential tools for predicting biodiesel synthesis using waste date seed oil as feedstock. Novelties of this investigation comprise (a) hybridization of BOA with each artificial intelligence (AI) approach resulting in the formation of BOA-SVR and BOA-BRT super learner models, (b) the model performance was compared using several performance indicators including coefficient of determination (R-2), mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), etc., (c) validation of the model was confirmed using extra simulated data, (d) the crow search algorithm (CSA) was integrated with the BOA-SVR resulting advanced super learner model (BOA-SVR-CSA) for finding the global optimal point. BOA-BRT model provided relatively low R-2 (0.81) and high errors (MAE of 8.5159, RSME of 12.4674, MAPE of 106.0391). RSM model was statistically significant (P-value <.05) with relatively high R-2 (0.95) and moderate errors (MAE of 4.8886, RSME of 5.5964, MAPE of 22.1574). The BOA-SVR model provided low errors (MAE of 3.8342, RSME of 3.8884, MAPE of 18.91) with a high R-2 of 0.98. The overall results suggested that the BOA-SVR model performs better with increased accuracy than other models. The extra simulated data further confirmed the prediction capability of the developed super learner model (BOA-SVR). The maximum biodiesel yield of 91.35% was achieved with a KOH dose of 0.6 wt%, M:O of 7:1 at a reaction time of 2 hours using the advanced super learner model (BOA-SVR-CSA). Overall, this novel platform could be of considerable promise in other process modeling and multiobjective optimization applications.

关键词： biodiesel boosted regression tree crow search algorithm response surface methodology support vector regression

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Optimal reactive power planning in power transmission system considering FACTS devices and implementing hybrid optimisation approach

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第25期14卷 6294-6305页

作者： Karmakar, Nihar Bhattacharyya, Biplab IIT ISM Dept Elect Engn Dhanbad Jharkhand India

This study introduces a techno-economic planning strategy of reactive power (VAr) in power transmission systems. This planning strategy primarily has been focused on reactive power planning (RPP) through operating cost minimisation ensuring cost-oriented system security. The objective function (operating cost) is blended with four different components viz. cost due to real power loss, VAr generation cost, additional VAr compensation devices cost and line charging cost. FACTS (flexible AC transmission system) devices are installed at weak positions through different echelons to improve the system voltage stability. The authors also implemented the probabilistic hybridisation of crow search algorithm and JAYA to find out the optimal set of controlling parameters related to VAr. Finally, a sharp and minute analysis of the results has been done to validate the proposed strategy under multi-loading conditions. To understand the efficiency and efficacy the code profiling and simulation has been rendered on IEEE 30 bus and UPSEB 75 bus test system.

关键词： static VAr compensators optimisation flexible AC transmission systems reactive power control power transmission control power transmission planning search problems probability optimal reactive power planning power transmission system FACTS devices hybrid optimisation approach techno-economic planning strategy operating cost minimisation cost-oriented system security objective function operating cost power loss VAr generation cost flexible AC transmission system system voltage stability UPSEB 75 bus test system VAr compensation devices cost multi-loading conditions crow search algorithm

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Meta-Heuristic algorithms-Tuned Elman vs. Jordan Recurrent Neural Networks for Modeling of Electron Beam Welding Process

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NEURAL PROCESSING LETTERS 2021年第2期53卷 1647-1663页

作者： Das, Debasish Das, Amit Kumar Pal, Abhishek Rudra Jaypuria, Sanjib Pratihar, Dilip Kumar Roy, Gour Gopal Indian Inst Technol Kharagpur Kharagpur W Bengal India Indian Inst Technol Kharagpur Dept Mech Engn Kharagpur W Bengal India Indian Inst Technol Kharagpur Dept Met & Mat Engn Kharagpur W Bengal India

A boost in the preference of high energy beam, such as electron beam, laser beam etc. has led to the requirement of its automation through accurate input-output modelling. Modeling of electron beam welding is conducted in the present study through Elman and Jordan recurrent neural networks (RNNs), both having a single feed-back loop, to meet the said requirement. The RNNs are trained using some nature-inspired optimization tools, namely cuckoo search, firefly, flower pollination, and crow search utilizing input-output welding data, obtained from a computational fluid dynamics-based heat transfer and fluid flow welding model. RNN predictions are validated through real experiments. Thus, the effect of change in the position of the feed-back loop on the accuracy of prediction of RNNs is investigated. In addition, a few popular statistical tests have been used to evaluate the performances of the RNNs tuned by various optimization algorithms, where flower pollination-tuned Jordan RNN is observed to yield the best results.

关键词： Recurrent Neural networks Cuckoo search algorithm Firefly algorithm Flower pollination algorithm crow search algorithm Statistical tests

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Incorporating Load Fluctuation in Feature Importance Profile Clustering for Day-Ahead Aggregated Residential Load Forecasting

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IEEE ACCESS 2020年 8卷 25198-25209页

作者： Huang, Nantian Wang, Wenting Wang, Sining Wang, Jun Cai, Guowei Zhang, Liang Northeast Elect Power Univ Minist Educ Key Lab Modern Power Syst Simulat & Control & Re Jilin 132012 Jilin Peoples R China Beijing Zhongdian Puhua Informat Technol Co Ltd Beijing 100192 Peoples R China

Residents clustering in different periods of load fluctuation and aggregated forecasting can increase the load prediction accuracy. But the strength of load fluctuation reflects the difference in the electricity consumption behavior of residents and affects the cluster results of residents. This paper presents a new day-ahead aggregated load-forecasting method for distribution networks based on the load fluctuation and feature importance (FI) profile clustering of residents. First, the input features are determined, the FI profile of residents is determined, and residents are clustered according to the FI profile. Then, the crow search algorithm is used to optimize the initial cluster centers for preventing the clustering results from falling into a local optimum. And the cluster verification index S_Dbw, the sum of the average scattering for the clusters and the inter-cluster density, is used to evaluate the cluster quality. The optimal clustering results of the aggregated load for different fluctuation periods are determined via statistical experiments. Finally, a random forest predictor based on ensemble learning is selected. According to the optimal clustering results in different fluctuation periods, a rolling forecasting model is constructed to realize day-ahead aggregated load forecasting in a residential distribution network.

关键词： Aggregated load forecasting feature importance load fluctuation crow search algorithm S-Dbw random forest

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