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A lightweight identity based generalized signcryption scheme for secure communication in standard model

International Journal of Information Technology (Singapore) 2024年第2期16卷 1221-1229页

作者： Singh, Tej Kumar, Mukesh Gelmecha, Demissie Jabir Thakur, Ganesh Kumar Tyagi, Vikas Sori, Worku Jifara Department of Applied Sciences ABES Engineering College UP Ghaziabad India Institute of Applied Sciences & Humanities GLA University UP Mathura India Department of Electronics and Communication Engineering School of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia Department of CSE IMS Engineering College UP Ghaziabad India Department of Computer Science School of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia

The implementation of secure communication in the Industrial Internet of Things (IIoT) has made and it is a significant security concern. Identity-based generalized signcryption scheme is one among the cryptographic tools available that offers a secure way out to meet the security needs of IIoT. Our proposal is a lightweight scheme, an efficient identity-based generalized signcryption that works as an encryption signature, or signcryption scheme using only one algorithm in a standard model. This scheme is appropriate for storage-limited environments like wireless sensor networks and smart cards due to its lightweight nature. This means that it can be implemented in a range of IIoT applications where resources are limited, without compromising on security or efficiency. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Bilinear Pairing Generalized signcryption Identity-based cryptography Signcryption

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Quality of Service based Selfish Node Detection in Mobile Ad-Hoc Network 3

Quality of Service based Selfish Node Detection in Mobile Ad...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Kannan, B. Maruthu Vadivel, G. Viswanathan, C. Reddy, P. Kiran Kumar Sphoorthy Engineering College Department of Computer Science and Engineering Telangana Hyderabad India University of Technology and Applied Science Department of Information Technology Salalah Oman Alpha College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Secunderabad India

ISBN: (纸本)9781665462167

Mobile ad hoc networks (MANETs) are wireless networks containing a collection of wireless nodes interrelated with multi-hop communication paths. Mobile nodes can freely create and dynamically self-organize random and temporary wireless networks without pre-existing infrastructure. Hence, the transmission connection between two nodes breaks repeatedly due to node mobility. This causes an additional delay in the network's performance. All mobile nodes work together entirely regarding the distribution of their memory space. In actuality, though, several nodes may inconsiderately choose to cooperate partially;otherwise, not at all with other nodes. These selfish nodes minimize the whole data's accessibility. To solve these issues, quality-of-service-based self-node detection (QSND) in MANET is proposed. In this approach, the Quality of Service (QoS) parameters verifies the operation of a node. This approach uses the QoS parameters like packet loss ratio, packet delay ratio, and remaining energy ratio as the most significant components to evaluate the selfish nodes in the MANET. This approach minimized the delay and improved the network throughput. © 2023 IEEE.

关键词： Quality of service

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Noninvasive nanoimaging-nanomanipulation of CRISPR-Cas in cells 21

Noninvasive nanoimaging-nanomanipulation of CRISPR-Cas in ce...

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Plasmonics in Biology and Medicine XXI 2024

作者： Zu, Di Li, Borui Lee, Somin Eunice Department of Electrical & Computer Engineering Biomedical Engineering Applied Physics Biointerfaces Institute Macromolecular Science & Engineering University of Michigan Ann ArborMI United States

ISBN: (纸本)9781510669796

CRISPR-Cas9 is significantly potential and versatile gene-editing treatment for neurodegenerative disorders. The CRISPR-Cas9 system incorporates a single guide RNA (sgRNA) and Cas9 nuclease, which helps system to bind to the target sequence, and makes a double-strand break respectively. Viral vectors, as traditional delivery system of CRISPRCas9, cause safety issue regrading immunogenic complications. In this work we present alternative non-viral vectors with plasmonic properties as a delivery system. Our work provides a new perspective of nanoparticles for delivery and visualization applications of the CRISPR-Cas9 system. © 2024 SPIE.

关键词： Neurodegenerative diseases

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A Blockchain-Based Approach to Counterfeit Prevention 3

A Blockchain-Based Approach to Counterfeit Prevention

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Supriya, M.S. Narang, Mridula G Prajwal, G.K. Shetty, Sohan S Patil, Anurag M S Ramaiah University of Applied Sciences Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350317022

In recent years, the prevalence of counterfeit goods in the industrial sector has increased. The company's reputation, sales, and profitability are impacted by this. In recent times there has been a sudden rise in Blockchain technology all thanks to cryptocurrencies. This rise in the technology helped increase research about Blockchain and its uses in various domains, Blockchain technology being known as tamper proof helps in solving the problem where the integrity is in question. This paper discusses the working and implementation of product counterfeit prevention systems using Blockchain technology. This system uses a product-specific QR code generated by the manufacturer to store the product details and an Ethereum-based Blockchain network to store all the product and transaction details. Using this system the consumer can be completely sure about the product's credibility and the manufacturer can safeguard their business interest by reducing the circulation of fake products which can damage their reputation and financial aspects. © 2023 IEEE.

关键词： Anti-counterfeiting Blockchain Counterfeit products Decentralization Ganache Metamask Truffle

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Sub-10 nm nanoscopic imaging reveals sub-10nm cellular architectures 21

Sub-10 nm nanoscopic imaging reveals sub-10nm cellular archi...

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Plasmonics in Biology and Medicine XXI 2024

作者： Cui, Guangjie Kim, Do Young Zu, Di Chai, Guanbo Lee, Somin Eunice Department of Electrical & Computer Engineering Biomedical Engineering Applied Physics Biointerfaces Institute Macromolecular Science & Engineering University of Michigan Ann ArborMI United States

ISBN: (纸本)9781510669796

Phase intensity nanoscope (PINE) is a new super-resolution method to further improve the resolution of existing techniques. PINE utilizes an integrated phase-intensity device to modulate phase differences between electric field components to distinguish nanoprobes within a diffraction-limited region. This phase-intensity separation enables continuous imaging without photobleaching. PINE achieved sub-10nm resolution of cellular structures through precise localization of populations of randomly distributed nanorod probes. The distribution of localized nanorods forms patterns of underlying structures. By defining features from probe distribution patterns and minimizing the distances of each probe to its feature projection, PINE extracts sub-10nm structural information. PINE will pave the way for new sub-10nm long-term investigations of previously unexplored material, chemical and biological dynamics. © 2024 SPIE.

关键词： Probes

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BoT-FaceSORT: Bag-of-Tricks for Robust Multi-face Tracking in Unconstrained Videos 17th

BoT-FaceSORT: Bag-of-Tricks for Robust Multi-face Tracking ...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Kim, Jonghyeon Ju, Chan-Yang Kim, Gun-Woo Lee, D.-H. Department of Applied Artificial Intelligence Hanyang University Ansan Korea Republic of Department of Computer Science and Engineering Gyeongsang National University Jinju Korea Republic of

ISBN: (纸本)9789819609000

Multi-face tracking (MFT) is a subtask of multi-object tracking (MOT) that focuses on detecting and tracking multiple faces across video frames. Modern MOT trackers adopt the Kalman filter (KF), a linear model that estimates current motions based on previous observations. However, these KF-based trackers struggle to predict motions in unconstrained videos with frequent shot changes, occlusions, and appearance variations. To address these limitations, we propose BoT-FaceSORT, a novel MFT framework that integrates shot change detection, shared feature memory, and an adaptive cascade matching strategy for robust tracking. It detects shot changes by comparing the color histograms of adjacent frames and resets KF states to handle discontinuities. Additionally, we introduce MovieShot, a new benchmark of challenging movie clips to evaluate MFT performance in unconstrained scenarios. We also demonstrate the superior performance of our method compared to existing methods on three benchmarks, while an ablation study validates the effectiveness of each component in handling unconstrained videos. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Kalman filters

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Effective Return Rate Prediction of Blockchain Financial Products Using Machine Learning

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computers, Materials & Continua 2023年第1期74卷 2303-2316页

作者： K.Kalyani Velmurugan Subbiah Parvathy Hikmat A.M.Abdeljaber T.Satyanarayana Murthy Srijana Acharya Gyanendra Prasad Joshi Sung Won Kim PG and Research Department of Computer Science Marudupandiyar College(Affiliated to Bharathidasan UniversityTiruchirappalli)Thanjavur613403India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126TamilnaduIndia Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmmanJordan Department of Information Technology Chaitanya Bharathi Institute of TechnologyHyderabadTelangana500075India Department of Convergence Science Kongju National UniversityGongju32588Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

In recent times,financial globalization has drastically increased in different ways to improve the quality of services with advanced *** successful applications of bitcoin Blockchain(BC)techniques enable the stockholders to worry about the return and risk of financial *** stockholders focused on the prediction of return rate and risk rate of financial ***,an automatic return rate bitcoin prediction model becomes essential for BC financial *** newly designed machine learning(ML)and deep learning(DL)approaches pave the way for return rate predictive *** study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder(JSO-ELMAE)for return rate prediction of BC financial *** presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial ***,the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification *** application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return *** experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many *** experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.

关键词： Financial products blockchain return rate prediction model machine learning parameter optimization

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Approaches for Solving a Dynamic Stacking Problem in Uncertain Environments 17

Approaches for Solving a Dynamic Stacking Problem in Uncerta...

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17th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2023

作者： Boecke, Sarah Logofatu, Doina Frankfurt University of Applied Sciences Department of Computer Science and Engineering Frankfurt am Main Germany

ISBN: (纸本)9798350338904

Dynamic optimization is of significant practical relevance for production and delivery processes that involve storage systems because of the dynamic nature of their environments. The relocation of products has to be scheduled while adhering to different time constraints. This paper addresses the challenge of developing solution approaches for such dynamic stacking problems in uncertain environments, specifically the environment represented by the "Hotstorage"simulation of the DynStack Competition 2023 that is part of the Genetic and Evolutionary Computation Conference. Building on existing approaches, two new solution approached are introduced: a score-based and a genetic solver. Comparing their performance with each other and with existing approaches shows that the score-based solver outperforms other solvers in regard to specific evaluation criteria while the genetic solver in its current form does not perform as well. © 2023 IEEE.

关键词： Petroleum reservoir evaluation

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An Overview of Moving Object Detection Using YOLO Deep Learning Models 2

An Overview of Moving Object Detection Using YOLO Deep Learn...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Dwivedi, Upendra Joshi, Kireet Shukla, Surendra Kumar Rajawat, Anand Singh G L Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Greater Noida India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India SVKM S Nmims Mpstme Department of Computer Science and Engineering Shirpur Campus Shirpur India School of Computer Sciences and Engineering Sandip University Nashik India

ISBN: (纸本)9798350371055

computer vision is a promising domain that focuses on emerging approaches, algorithms and technologies to provide computing capability to machine to analysis visual data, such as image files, videos files and real time video streaming. In computer Vision and image processing detecting object from images and videos has been topic of extensive research. This is accomplished by applying various computer vision techniques to analyze the visual data and determine class of objects from image and videos files. One widespread approach to object detection is using the deep learning models. YOLO (You Only Look Once) is a convolutional neural network that offers a fast and efficient solution to object detection using deep learning. It enable computer to handle real-time detection of objects in video frames and accurately locate and classify moving objects. YOLO is able to simultaneously detect and classify objects in an efficient manner using convolutional neural networks. © 2024 IEEE.

关键词： Deep Learning Image Processing Motion Detection Moving Object Detection YOLO

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Degree reduction of ball Said-Ball surfaces using an enhanced coot optimization algorithm

Degree reduction of ball Said-Ball surfaces using an enhance...

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作者： Hu, Gang Dou, Wanting Du, Bo Department of Applied Mathematics Xi'an University of Technology Xi'an China School of Computer Science and Engineering Xi'an University of Technology Xi'an China

Degree reduction of ball Said-Ball (BSB) surface is a complex and unsolved problem in computer-aided design (CAD) and computer graphics (CG), which has potential application prospects in many engineering fields of geometric modeling. In this paper, the degree reduction of BSB surface is transformed into the optimization problems of center surface and radius function, and the improved coot optimization algorithm is used to solve it. An effective method to solve the degree reduction of BSB surface by intelligent optimization algorithm is proposed. Firstly, the degree reduction of BSB surface is decomposed into center surface reduction and radius function reduction, and the objective function and constraint conditions are determined according to the optimization idea, then the degree reduction optimization model of center surface and radius function is established. Secondly, an enhanced coot (ECOOT for short) algorithm is proposed, which combines chain strategy, dispersed foraging strategy, and quadratic interpolation strategy. Compared with the original algorithm, ECOOT algorithm has better convergence performance and higher calculation accuracy. Finally, the proposed ECOOT algorithm is used to solve the degree reduction optimization model, and the optimal center surface and minimum distance radius function of the BSB surface after degree reduction are obtained. The experimental results show that the ECOOT algorithm can effectively solve the problem of degree reduction for BSB surface, the degree reduction error is reduced by about 20% compared to the original COOT algorithm, and is superior to other intelligent optimization algorithms in accuracy, robustness, and convergence. © 2023 John Wiley & Sons, Ltd.

关键词： Optimization

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