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A New Generative Mathematical Model for Coverless Steganography System Based on Image Generation

computers, Materials & Continua 2023年第3期74卷 5087-5103页

作者： Al-Hussien Seddik Mohammed Salah Gamal Behery Ahmed El-harby Ahmed Ismail Ebada Sokea Teng Yunyoung Nam Mohamed Abouhawwash Department of Computer Science Faculty of ScienceMinia UniversityMiniaEgypt Department of Computer Science Faculty of Computers and Artificial IntelligenceDamietta UniversityNew DamiettaEgypt Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast Lansing48824MIUSA

The ability of any steganography system to correctly retrieve the secret message is the primary criterion for measuring its ***,researchers have tried to generate a new natural image driven from only the secret message bits rather than using a cover to embed the secret message within it;this is called the stego *** paper proposes a new secured coverless steganography system using a generative mathematical model based on semi Quick Response(QR)code and maze game image *** system consists of two *** first component contains two processes,encryption process,and hiding *** encryption process encrypts secret message bits in the form of a semi-QR code image whereas the hiding process conceals the pregenerated semi-QR code in the generated maze game *** the other hand,the second component contains two processes,extraction and decryption,which are responsible for extracting the semi-QR code from the maze game image and then retrieving the original secret message from the extracted semi-QR code image,*** results were obtained using the bit error rate(BER)*** results confirmed that the system achieved high hiding capacity,good performance,and a high level of robustness against attackers compared with other coverless steganography methods.

关键词： Coverless steganography data hiding information security QR code maze game

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A review on simulation models of cascading failures in power systems

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iEnergy 2023年第4期2卷 284-296页

作者： Zhenping Guo Kai Sun Xiaowen Su Srdjan Simunovic Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTNUSA Department of Electrical and Computer Engineering University of Texas at San AntonioSan AntonioTXUSA Computational Sciences&Engineering Division Oak Ridge National LaboratoryOak RidgeTNUSA

Among various power system disturbances,cascading failures are considered the most serious and extreme threats to grid operations,potentially leading to significant stability issues or even widespread power *** power systems’behaviors during cascading failures is of great importance to comprehend how failures originate and propagate,as well as to develop effective preventive and mitigative control *** intricate mechanism of cascading failures,characterized by multi-timescale dynamics,presents exceptional challenges for their *** paper provides a comprehensive review of simulation models for cascading failures,providing a systematic categorization and a comparison of these *** challenges and potential research directions for the future are also discussed.

关键词： Cascading failures power systems security and reliability simulation models

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Towards automatic generation of image recognition models for industrial robot arms 28

Towards automatic generation of image recognition models for...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Arnarson, Halldor Bremdal, Bernt Arild Fredheim Hanssen, Magnus UiT the Arctic University of Norway Department of Industrial Engineering Narvik Norway UiT the Arctic University of Norway and Smart Innovation Norway Department of Computer Science and Computational Engineering Narvik and Halden Norway UiT the Arctic University of Norway Department of Computer Science and Computational Engineering Narvik Norway

ISBN: (纸本)9798350339918

As the world moves towards mass customization, there is a need for a manufacturing system that can quickly adapt to market changes. Reconfigurable manufacturing systems (RMS) have been proposed as a solution. RMS is designed to be modular with a high degree of flexibility. However, such a structure creates a lot of complexity. For instance, if the modules are moved or changed, the robot arms in the system must be re-programmed. Adding 3D cameras and image recognition to the robot arms can solve some of these problems. Nevertheless, creating image recognition models is time-consuming work, requires human labor, and can increase the cost of manufacturing. To manufacture a large variety of products, there is a need to create image recognition models for each product. One method to automate the generation of image recognition models can be to use synthetic data. Synthetic data can be used to generate a large amount of labeled data, which can be used to train image recognition *** this paper, we propose a method for training image recognition models using synthetic data, which can further automate robots in RMS. Specifically, the system utilizes a 3D model of a part to generate images, which are then processed by a cycle generative adversarial network (GAN) to enhance their realism. These images are subsequently auto-labeled and employed to train an image recognition model compatible with an industrial robot arm. © 2023 IEEE.

关键词： Image recognition

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Optimizing Data Center Power Usage: Server Consolidation and Efficient Cooling Techniques 7th

Optimizing Data Center Power Usage: Server Consolidation and...

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7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： Bose, Rajesh Mondal, Haraprasad Sutradhar, Shrabani Roy, Sandip Department of Computer Science & Engineering JIS University West Bengal Kolkata India Institute of Engineering and Technology Dibrugarh University Assam786001 India Department of Computational Science Brainware University West Bengal Kolkata India

ISBN: (纸本)9789819753369

Data centers’ high energy consumption necessitates essential power-saving techniques for optimization. This paper proposes two effective strategies: server consolidation and precise air conditioning units. Server consolidation combines multiple servers into a single server or cluster, reducing energy usage, carbon footprint, and cutting hardware and maintenance costs. Additionally, a virtualized task scheduling algorithm enhances server utilization and data center performance. The second technique involves using computer room air conditioning (CRAC) units to regulate temperature, air distribution, and humidity, leading to reduced energy consumption and preventing overheating-related equipment failure. Studies confirm significant reductions in data center power consumption and carbon footprint through these techniques. For instance, a study showed savings of 41,000 kWh of electric power and 16 tons of carbon footprint. Implementing these methods allows data center operators to achieve substantial energy savings and environmental impact minimization. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Carbon footprint

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Discrete GWO Optimized Data Aggregation for Reducing Transmission Rate in IoT

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computer Systems science & engineering 2023年第3期44卷 1869-1880页

作者： S.Siamala Devi K.Venkatachalam Yunyoung Nam Mohamed Abouhawwash Department of Computer Science and Engineering Sri Krishna College of TechnologyCoimbatore641042India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

The conventional hospital environment is transformed into digital transformation that focuses on patient centric remote approach through advanced *** diagnosis of many diseases will improve the patient *** cost of health care systems is reduced due to the use of advanced technologies such as Internet of Things(IoT),Wireless Sensor Networks(WSN),Embedded systems,Deep learning approaches and Optimization and aggregation *** data generated through these technologies will demand the bandwidth,data rate,latency of the *** this proposed work,efficient discrete grey wolf optimization(DGWO)based data aggregation scheme using Elliptic curve Elgamal with Message Authentication code(ECEMAC)has been used to aggregate the parameters generated from the wearable sensor devices of the *** nodes that are far away from edge node will forward the data to its neighbor cluster head using *** scheme will reduce the number of transmissions over the *** aggregated data are preprocessed at edge node to remove the noise for better *** node will reduce the overhead of cloud *** aggregated data are forward to cloud server for central storage and *** proposed smart diagnosis will reduce the transmission cost through aggrega-tion scheme which will reduce the energy of the *** cost for proposed system for 300 nodes is 0.34μ*** energy cost of existing approaches such as secure privacy preserving data aggregation scheme(SPPDA),concealed data aggregation scheme for multiple application(CDAMA)and secure aggregation scheme(ASAS)are 1.3μJ,0.81μJ and 0.51μJ *** optimization approaches and encryption method will ensure the data privacy.

关键词： Discrete grey wolf optimization data aggregation cloud computing IoT WSN smart healthcare elliptic curve elgamal energy optimization

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Smoothed-LSTM: Advancing spatio-temporal NDVI prediction in semi-automated dataset for rice crop

Smoothed-LSTM: Advancing spatio-temporal NDVI prediction in ...

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2024 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2024

作者： Dey, Anamika Sarkar, Somrita Kumar, Debajyoti Mondal, Arijit Mitra, Pabitra Indian Institute of Technology Kharagpur Centre For Computational And Data Sciences Kharagpur India Indian Institute of Technology Department of Computer Science And Engineering Patna Bihta India Indian Institute of Technology Kharagpur Department of Computer Science And Engineering Kharagpur India

ISBN: (纸本)9798350389678

Accurate Normalized Difference Vegetation Index (NDVI) forecasting is crucial for effective agricultural planning. However, a good prediction of the same requires sufficient data, but structured data is not available in the public domain or open-source community. Also, most of the existing methods do not consider spatial information. This study presents a novel semi-automated dataset generation framework that utilizes Sentinel-2, POWER Data Access Viewer, and Google Earth Engine to create a comprehensive time-series dataset. We propose a smoothed long short-term memory (LSTM) model considering time series, historical meteorological, and spatial information. The proposed Smoothed-LSTM-based model outperforms Traditional-LSTM models, demonstrating its effectiveness in NDVI prediction for agricultural applications. © 2024 IEEE.

关键词： Long short-term memory

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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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Energy-Efficient Clustering Using Optimization with Locust Game Theory

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Intelligent Automation & Soft Computing 2023年第6期36卷 2591-2605页

作者： P.Kavitha Rani Hee-Kwon Chae Yunyoung Nam Mohamed Abouhawwash Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Wireless sensor networks(WSNs)are made up of several sensors located in a specific area and powered by a finite amount of energy to gather environmental *** use sensor nodes(SNs)to collect and transmit ***,the power supplied by the sensor network is ***,SNs must store energy as often as to extend the lifespan of the *** the proposed study,effective clustering and longer network lifetimes are achieved using mul-ti-swarm optimization(MSO)and game theory based on locust search(LS-II).In this research,MSO is used to improve the optimum routing,while the LS-II approach is employed to specify the number of cluster heads(CHs)and select the best *** the CHs are identified,the other sensor components are allo-cated to the closest CHs to them.A game theory-based energy-efficient clustering approach is applied to *** each SN is considered a player in the *** SN can implement beneficial methods for itself depending on the length of the idle listening time in the active phase and then determine to choose whether or not to *** proposed multi-swarm with energy-efficient game theory on locust search(MSGE-LS)efficiently selects CHs,minimizes energy consumption,and improves the lifetime of *** findings of this study indicate that the proposed MSGE-LS is an effective method because its result proves that it increases the number of clusters,average energy consumption,lifespan extension,reduction in average packet loss,and end-to-end delay.

关键词： Wireless sensor network clustering routing cluster head energy consumption network’s lifetime multi swarm optimization game theory

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Joint Wireless and Computing Resource Management with Optimal Slice Selection in in-network-edge Metaverse System 15

Joint Wireless and Computing Resource Management with Optima...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Rashid, Sulaiman Muhammad Aliyu, Ibrahim Isah, Abubakar Lee, Jihoon Oh, Sangwon Hahn, Minsoo Kim, Jinsul Chonnam National University Department of Intelligent Electronics and Computer Engineering Gwangju Korea Republic of Astana It University Department of Computational and Data Science Astana Kazakhstan

ISBN: (纸本)9798350364637

This paper presents an approach to joint wireless and computing resource management in slice-enabled metaverse networks, addressing the challenges of inter-slice and intra-slice resource allocation in the presence of in-network computing. We formulate the problem as a mixed-integer nonlinear programming (MINLP) problem and derive an optimal solution using standard optimization techniques. Through extensive simulations, we demonstrate that our proposed method significantly improves system performance by effectively balancing the allocation of radio and computing resources across multiple slices. Our approach outperforms existing benchmarks, particularly in scenarios with high user demand and varying computational tasks. © 2024 IEEE.

关键词： Mixed-integer linear programming

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Building a portable parallel asynchronous PDE solver using Kokkos 31

Building a portable parallel asynchronous PDE solver using K...

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31st IEEE International Conference on High Performance Computing, Data, and Analytics Workshops, HiPCW 2024

作者： Bhat, Ranjan Aditya, Konduri Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Computational and Data Sciences Indian Institute of Science Bengaluru India

ISBN: (纸本)9798331509118

In this study, we outline the design and implementation of a portable massively parallel asynchronous solver for time-dependent partial differential equations (PDEs). The solver is implemented using Kokkos library for portability across different node architectures and is coupled with the communication-avoiding algorithm that relaxes the communication across nodes, thus improving the scalability. Asynchrony-tolerant finite difference schemes for spatial derivatives along with the low storage Runge-Kutta scheme for time integration are used to achieve high-order accuracy. The efficacy of the implementation is demonstrated using a mini-app that solves the three-dimensional diffusion problem. © 2024 IEEE

关键词： Scalability

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