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IoDseC++: authenticated key exchange protocol for cloud-enable internet of drone communication

Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9529-9542页

作者： Amin, Ruhul Jayaswal, Sakshita Sureshkumar, Venkatasamy Rathore, Balram Jha, Ananya Abdussami, Mohammad Department of Computer Science and Engineering IIIT Naya raipur Sector 24 Chhattisgarh Naya Raipur493661 India Department of Applied Mathematics and Computational Sciences PSG College of Technology Tamilnadu Coimbatore641004 India

In recent times, sensor node or smart camera can be embedded in the drones for the collection of important data with the help of Internet Technology. Several security threats/vulnerabilities may hamper the system while collecting data. Hence, strong protection against security attacks is essential in drone based data collection system. In this work, we have considered a drone based cloud enable architecture for collecting and sharing information. The aim of this paper is to design a lightweight security protocol which will provide secure communication and mutual authentication among the entities. The proposed protocol establishes temporary session key for each session in order to make the complete system security attacks free. We have simulated the proposed protocol for measuring security strength using Scyther simulation whose results show all the claimed parameters are private during protocol run. It also ensures the correctness of mutual authentication. The informal analysis confirms strong protection on all security threats related to proposed architecture. The performance analysis has also made which shows that the protocol is lightweight and achieves better efficiency in comparison with related published works. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Drones

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Traffic Sign Recognition System

Traffic Sign Recognition System

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Ahmed, Maqsood Ali, Raja Hashim Ali, Nisar Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14469 Germany Faculty of Engineering and Applied Science University of Regina ReginaS4S0A2 Canada

ISBN: (纸本)9798331504465

Traffic Sign Recognition Systems (TSRS) are instrumental in improving road safety by assisting drivers and supporting autonomous vehicles in real-time identification of road regulations. These systems have advanced significantly in recent years, with deep learning approaches achieving promising results. However, despite these developments, existing models face challenges in adapting to the diverse conditions of real-world environments, including variations in lighting, weather, and sign appearance. Addressing this limitation, our study focuses on enhancing the robustness and accuracy of TSRS for practical deployment. In response to this gap, we introduce a novel deep learning model optimized for traffic sign recognition across various conditions. Our approach utilizes a convolutional neural network (CNN) architecture, which was trained on the German Traffic Sign Recognition (GTSR) dataset. We improved the model’s adaptability to new and unseen data while achieving a high accuracy rate of 97.99% by applying techniques like data augmentation and transfer learning. Methodologically, the model workflow includes extensive preprocessing, hyperparameter tuning, and real-time inference evaluations, ensuring suitability for deployment in autonomous driving systems. Our study contributes by providing a scalable, high-accuracy TSRS model that can reliably identify traffic signs in complex environments, supporting both autonomous and assisted driving technologies. The model’s performance paves the way for safer and more efficient road transport, while our results highlight the potential for further interdisciplinary research to expand TSRS capabilities. © 2024 IEEE.

关键词： Convolutional neural networks

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Tube-based Model Reference Adaptive Control for Vibration Suppression of Active Suspension Systems

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 728-731页

作者： Yashar Mousavi Alireza Alfi Ibrahim Beklan Kucukdemiral Afef Fekih IEEE the Department of Applied Science School of ComputingEngineering and Built EnvironmentGlasgow Caledonian University the Faculty of Electrical Engineering Shahrood University of Technology the Electrical and Computer Engineering Department University of Louisiana at Lafayette

Dear editor,With the developments of industrial automation in recent years,vehicle suspension systems have received a great deal of attention in industry and academia due to their critical role in the chassis performance of vehicles[1].The suspension system is expected to guarantee the vehicle’s maneuverability and provide satisfactory ride comfort by absorbing the vibrations arising from the road surface excitations and ensuring road-holding capability and suspension *** by the desirable performance of the model reference adaptive control(MRAC)approach,various literature studies have investigated its performance in diverse linear and nonlinear practical systems[2].

关键词： editor Active guarantee

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Back propagation artificial neural network for diagnose of the heart disease

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Journal of Reliable Intelligent Environments 2023年第1期9卷 57-85页

作者： Kaur, Jagmohan Khehra, Baljit S. Singh, Amarinder IKG Punjab Technical University Punjab Jalandhar144103 India Computer Science and Engineering BBSB Engineering College Punjab Fatehgarh Sahib140407 India Department of Applied Sciences BBSB Engineering College Punjab Fatehgarh Sahib140407 India

Nowadays, coronary heart disease is one of the most fatal disease globally. Many researchers and medical technicians have developed and designed various computer-aided diagnosis systems using various machine learning models such as random forest, linear regression, rough set, naive bayes, artificial neural network, support vector machine, multivariate adaptive regression splines, K-nearest neighbor and decision tree to name a few. This era of digital health domain demands early prediction of heart disease which is the crucial need to control the global mortality rate of this particular disease. Commonly used methods for heart disease detection are clinical and expert dependant which makes them costly and inaccessible to the masses. In this paper, an efficient-cum-automated coronary heart disease diagnosis model is being proposed using multi-layered artificial neural network with back propagation algorithm. The proposed model compares the variation caused by different number of neurons used in the hidden layers for different transfer functions. The model has been implemented on Kaggle and Statlog heart disease dataset with thirteen clinical parameters. The experimental results attained an accuracy of 99.92% using six hidden layers with tan-hyperbolic transfer function. The results have been substantiated through statistical parameters and k-fold cross validation. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Nearest neighbor search

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An Ensemble Learning Based Approach for Detecting and Tracking COVID19 Rumors

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computers, Materials & Continua 2022年第1期70卷 1721-1747页

作者： Sultan Noman Qasem Mohammed Al-Sarem Faisal Saeed Computer Science Department College of Computer and Information SciencesAl Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Computer Science Department Faculty of Applied ScienceTaiz UniversityTaiz6803Yemen College of Computer Science and Engineering Taibah UniversityMedinaSaudi Arabia Information System Department Saba’a Region UniversityMareebYemen

Rumors regarding epidemic diseases such as COVID 19,medicines and treatments,diagnostic methods and public emergencies can have harmful impacts on health and political,social and other aspects of people’s lives,especially during emergency situations and health *** huge amounts of content being posted to social media every second during these situations,it becomes very difficult to detect fake news(rumors)that poses threats to the stability and sustainability of the healthcare sector.A rumor is defined as a statement for which truthfulness has not been *** COVID 19,people found difficulty in obtaining the most truthful news easily because of the huge amount of unverified information on social *** methods have been applied for detecting rumors and tracking their sources for COVID 19-related ***,very few studies have been conducted for this purpose for the Arabic language,which has unique ***,this paper proposes a comprehensive approach which includes two phases:detection and *** the detection phase of the study carried out,several standalone and ensemble machine learning methods were applied on the Arcov-19 dataset.A new detection model was used which combined two models:The Genetic Algorithm Based Support Vector Machine(that works on users’and tweets’features)and the stacking ensemble method(that works on tweets’texts).In the tracking phase,several similarity-based techniques were used to obtain the top 1%of similar tweets to a target tweet/post,which helped to find the source of the *** experiments showed interesting results in terms of accuracy,precision,recall and F1-Score for rumor detection(the accuracy reached 92.63%),and showed interesting findings in the tracking phase,in terms of ROUGE L precision,recall and F1-Score for similarity techniques.

关键词： Rumor detection rumor tracking similarity techniques COVID-19 social media analytics

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Integrating Artificial Intelligence and Multiple Intelligences for Advanced Educational Models

Integrating Artificial Intelligence and Multiple Intelligenc...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Palarimath, Suresh Maran, Pyingkodi Venkateswaran, R. Wilfred Blessing, N.R. Shiny, K.V. Renuga, S. University of Technology and Applied Sciences College of Computing and Information Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India University of Technology and Applied Sciences College of Computing and Information Sciences Ibri Oman Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350378092

The aim of educational innovation is to foster students' creative and problem-solving skills via the integration of several disciplines, including science, technology, engineering, art, and mathematics. Efficiently identifying and fostering the various abilities of pupils continues to be a crucial obstacle. This research presents a sophisticated educational approach that combines the notion of multiple intelligences with artificial intelligence (AI) technology to tackle this problem. This concept improves the teaching environment by including intelligent auxiliary services for instructors and students via the use of smart speech and picture interaction. Artificial intelligence (AI) integrated into the multiple intelligence's framework enables the ability to observe, analyze, and implement personalized teaching tactics in real-time using machine learning. The suggested AI-assisted education paradigm aims to enhance teacher-student interactions and facilitate personalized instruction. The study provides evidence that this technique successfully facilitates personalized and captivating learning experiences, promoting students' diverse talents and augmenting their creativity and problem-solving capabilities. This approach seeks to fundamentally transform conventional educational techniques by addressing the disparity between existing educational practices and the future requirements of the workforce. © 2024 IEEE.

关键词： Adversarial machine learning

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Leveraging Information-Centric Edge Computing in Private 5G Cellular Networks via the W3C Web of Things 22

Leveraging Information-Centric Edge Computing in Private 5G ...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Romann, Jan Damer, Hugo Hakim Gias, T. M. Rayhan Bergmann, Olaf University of Applied Sciences Emden/Leer Faculty of Technology Department of Electrical Engineering and Computer Science Constantiaplatz 4 Emden26723 Germany

ISBN: (纸本)9798331508050

In this paper, we present an architecture for the deployment of an edge computing system based on Named Data Networking (NDN) as part of a private 5G network. Our proposed architecture integrates IP-based and non-IP-based components using the W3C Web of Things (WoT) standards, including a Thing Description Directory (TDD). Our results highlight the potential of both edge computing and WoT concepts for private 5G deployments but also reveal several standardization gaps that need to be closed to make effective use of the new technologies. © 2025 IEEE.

关键词： Internet of things

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Optimal Number of Cardiac Cycles for Continuous Blood Pressure Estimation

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IEIE Transactions on Smart Processing and Computing 2022年第6期11卷 421-425页

作者： Sunarya, Unang Park, Cheolsoo Department of Computer Engineering Kwangwoon University Seoul Korea Republic of School of Applied Science Telkom University Bandung Indonesia

Continuous blood pressure monitoring is essential for patients with hypertension. Most studies have suggested cuffless blood pressure monitoring techniques using a single cardiac cycle based on the pulse transit time. This paper investigates feature extraction from multiple cardiac cycles to estimate blood pressure. This implementation uses electrocardiogram, photoplethysmogram, and ballistrocardiagram signals. Random forest was applied to estimate blood pressure using the leave-one-subject-out cross-validation technique. The results show that the model could achieve better performance when using three cardiac cycles with an average MAE of 3.364 ± 3.059 mmHg for the diastolic blood pressure and 4.201 ±2.355 mmHg for the systolic blood pressure. Copyrights © 2022 The Institute of Electronics and Information Engineers.

关键词： Blood pressure

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Cluster-preserving sampling algorithm for large-scale graphs

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science China(Information sciences) 2023年第1期66卷 60-76页

作者： Jianpeng ZHANG Hongchang CHEN Dingjiu YU Yulong PEI Yingjun DENG National Digital Switching System E&T Research Center Information Engineering University Network Systems Department of the Strategic Support Force School of Computer Science and Technology Eindhoven University of Technology Center for Applied Mathematics Tianjin University

Graph sampling is a very effective method to deal with scalability issues when analyzing largescale graphs. Lots of sampling algorithms have been proposed, and sampling qualities have been quantified using explicit properties(e.g., degree distribution) of the sample. However, the existing sampling techniques are inadequate for the current sampling task: sampling the clustering structure, which is a crucial property of the current networks. In this paper, using different expansion strategies, two novel top-leader sampling methods(i.e., TLS-e and TLS-i) are proposed to obtain representative samples, and they are capable of effectively preserving the clustering structure. The rationale behind them is to select top-leader nodes of most clusters into the sample and then heuristically incorporate peripheral nodes into the sample using specific expansion strategies. Extensive experiments are conducted to investigate how well sampling techniques preserve the clustering structure of graphs. Our empirical results show that the proposed sampling algorithms can preserve the population's clustering structure well and provide feasible solutions to sample the clustering structure from large-scale graphs.

关键词： graph sampling clustering structure top-leader nodes expansion strategies large-scale graphs

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CNN Based Features Extraction and Selection Using EPO Optimizer for Cotton Leaf Diseases Classification

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computers, Materials & Continua 2023年第9期76卷 2779-2793页

作者： Mehwish Zafar JaveriaAmin Muhammad Sharif Muhammad Almas Anjum Seifedine Kadry Jungeun Kim Department of Computer Science COMSATS University IslamabadWah CampusWah Cantt47040Pakistan Department of Computer Science University ofWahWah Cantt47040Pakistan National University of Technology(NUTECH) Islamabad44000Pakistan Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Worldwide cotton is the most profitable cash *** year the production of this crop suffers because of several *** an early stage,computerized methods are used for disease detection that may reduce the loss in the production of *** several methods are proposed for the detection of cotton diseases,however,still there are limitations because of low-quality images,size,shape,variations in orientation,and complex *** to these factors,there is a need for novel methods for features extraction/selection for the accurate cotton disease *** in this research,an optimized features fusion-based model is proposed,in which two pre-trained architectures called EfficientNet-b0 and Inception-v3 are utilized to extract features,each model extracts the feature vector of length N×*** that,the extracted features are serially concatenated having a feature vector lengthN×*** prominent features are selected usingEmperor PenguinOptimizer(EPO)*** method is evaluated on two publically available datasets,such as Kaggle cotton disease dataset-I,and Kaggle *** EPO method returns the feature vector of length 1×755,and 1×824 using dataset-I,and dataset-II,*** classification is performed using 5,7,and 10 folds *** Quadratic Discriminant Analysis(QDA)classifier provides an accuracy of 98.9%on 5 fold,98.96%on 7 fold,and 99.07%on 10 fold using Kaggle cotton disease dataset-I while the Ensemble Subspace K Nearest Neighbor(KNN)provides 99.16%on 5 fold,98.99%on 7 fold,and 99.27%on 10 fold using Kaggle cotton-leaf-infection dataset-II.

关键词： Deep learning cotton disease detection features selection classification efficientnet-b0 inception-v3 quadratic discriminant analysis subspace KNN

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