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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Ashraf, Humaira Malik, Javeria Ihsan, Uswa Al-Quayed, Fatima Humayun, Mamoona Jhanjhi, N.Z. International Islamic University Department of Computer Science and Software Engineering Islamabad Pakistan Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Information Systems College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Taylor's University Malaysia Applied Science Research Center Applied Science Private University Amman11937 Jordan

ISBN: (纸本)9798350335644

In era of modern world and technology where everything is digitalized. Cloud computing emerged as one of the fastest growing parts of digital industry. With the Succor of cloud computing models, business accomplishes their objectives with least exertion when contrasted with traditional processing environment. In spite of these compensations, yet security of the information in cloud database is the significant fear in endorsement of cloud. Although several techniques have been proposed such as cryptographic and steganographic approaches, but all of these have some loopholes. Therefore, in this paper new data protection technique based on steganographic encryption is proposed which has less computational time and secure against known plain text and Man-In-Middle Attack. © 2023 IEEE.

关键词： Cryptography

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Distributed Service Discovery over Heterogeneous Robotic Systems-of-Systems 19

Distributed Service Discovery over Heterogeneous Robotic Sys...

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19th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023

作者： Montecchiari, Leonardo Trotta, Angelo Gigli, Lorenzo Bellone, Lorenzo Di Felice, Marco Natalizio, Enrico University of Bologna Department of Computer Science and Engineering Italy University of Bologna Advanced Research Center on Electronic Systems Ercole de Castro Italy United Arab Emirates

ISBN: (纸本)9798350346497

In robotic swarms, nodes must cooperate in order to accomplish complex tasks. Robot interactions may occur at run-time based on the dynamic network topology and on mission-specific goals. For this reason, service discovery mechanisms are fundamental in order to inform each robot about the functionalities offered by the other peers. Although several network discovery protocols have been proposed so far, none of them fits the unique characteristics of robotic swarms in terms of dynamic topology, communication heterogeneity, and latency requirements. In this paper, we fill such gap by designing, implementing and evaluating a novel service discovery mechanisms for generic Robotic Systems-of-Systems (RSoS). The study proposes algorithmic and practical contributions. Regarding the first, we describe a novel distributed service discovery algorithm for RSoS which adapts to highly mobile robotic environments while limiting the network overhead and latency. Regarding the latter, we implemented the proposed algorithm within the Uhura swarm robotics framework;as a result, our solution is able to support multi-radio scenarios where robots are provided with heterogeneous Machine-to-Machine (M2M) communication technologies. In addition, we validate our solution through large-scale simulations and a test-bed in which ground robots are able to discover a Federated Learning (FL) task and join it at run-time to improve the accuracy. © 2023 IEEE.

关键词： Topology

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A Comparative Study of Gaze Estimation Models 25

A Comparative Study of Gaze Estimation Models

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Moubayed, Abdallah Injadat, MohammadNoor Kanan, Mohammad King Fahd University of Petroleum & Minerals Interdisciplinary Research Center for Intelligent Secure Systems Computer Engineering Department Dhahran Saudi Arabia Zarqa University Data Science & Artificial Intelligence Department Zarqa Jordan University of Business and Technology Department of Industrial Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798331540012

The eye-mind hypothesis suggests that people tend to look at what they're actively thinking about, forming the basis of eye and gaze tracking. This concept is gaining attention in deep learning due to its broad applications. The use of AI alongside webcams to monitor eye movements is increasingly popular and expected to grow further. This is further emphasized by recent data showing a growing use of eye gaze estimation techniques, especially in marketing research, e-commerce, and educational tools. Accordingly, multiple previous research works have developed various eye and gaze estimation and tracking models. However, one main limitation is that many models use their own datasets for performance evaluation as well as having different underlying computing resources that are used during training. Consequently, it becomes harder to compare the effectiveness and efficiency of these models. To that end, this work aims at providing a comprehensive comparative study of three well-established eye gaze estimation models, namely OpenGaze, GazeRefineNet, ODABE, and FAZE models using a unified evaluation framework. Experimental results conducted using GazeCapture dataset illustrate that OpenGaze model achieves a mean error of 2.27 cm mean error, GazeRefineNet model achieves 1.91 cm, ODABE model achieves 3.46 cm, and FAZE model achieves 2.9 cm. This indicates that GazeRefineNet outperforms the other models in terms of accuracy while having comparable computational complexity. © 2024 IEEE.

关键词： Market research

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Real-Time Reconstruction of 3D Space using Holographic Video 15

Real-Time Reconstruction of 3D Space using Holographic Video

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Deshpande, Vivek Anandha Silambarasan, D. Kalra, Hitesh Reena, R. Gupta, Shivangi Manjunath, C. Vishwakarma Institute of Technology Department of Computer Engineering Maharashtra Pune India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai127 India Quantum University Research Center Quantum University India School of Engineering and Technology Karnataka Bangalore India

ISBN: (纸本)9798350370249

The real-time Reconstruction of 3-D space and the usage of holographic video entail the capture and processing of films that have a three-dimensional shape in them. The manner of capturing the 3-dimensional shape includes: using a camera with the functionality to seize and technique the holographic photograph in a composite format, taking multiple photos, merging them into one hologram. Then, the photographs are processed in real-time with the usage of laptop imaginative and prescient algorithms for function extraction, segmentation, and 3-D object popularity. This application of the laptop, imaginative and prescient, allows the person to navigate and engage with the three-D illustration that is reconstructed in actual time. The reconstructed 3D scene is then rendered in the shape of an interactive 360-degree holographic video. This technology can provide a couple of applications in augmented and virtual reality gaming and immersive 3-D visualization of the captured environment. © 2024 IEEE.

关键词： Holograms

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GPT4ESG: Streamlining Environment, Society, and Governance Analysis with Custom AI Models 4

GPT4ESG: Streamlining Environment, Society, and Governance A...

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Lin, Lydia Hsiao-Mei Ting, Fang-Kai Chang, Ting-Jui Wu, Jun-Wei Tsai, Richard Tzong-Han National Taipei University of Technology Department of Interaction Design Taipei City Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan City Taiwan Center for Gis Research Center for Humanities and Social Sciences Academia Sinica Taipei City Taiwan

ISBN: (纸本)9798350360721

Many companies now prioritize Environment, Society, and Governance (ESG) and financial performance due to concerns about climate change. We created GPT4ESG, a quick method to analyze a company's ESG investment and influence. GPT4ESG is a model system architecture based on BERT and GPT with five parts:(1) Data cleaning: The data is cleaned and preprocessed to ensure it is in a suitable format for the model. (2) Tokenizer adaptation: A customized GPT assistant is used to assist in scoring, and results are given after expert review. (3) Model adjustment: A classification layer is added to the output. (4) Fine-tuning: The pre-trained model is fine-tuned according to the ESG field know-how. (5) Evaluation and Testing: The model was tested on a validation set during training and then on a testing set. We collected ESG reports of well-known listed companies in the technology industries in the United States as a data set. The experimental results showed that the customized GPT4ESG model was more effective than ESG-BERT in classifying ESG data. This model simplifies ESG reporting for stakeholders to make responsible decisions. © 2024 IEEE.

关键词： Climate change

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Design Patterns for Multilevel Modeling and Simulation 27

Design Patterns for Multilevel Modeling and Simulation

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27th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2023

作者： Serena, Luca Marzolla, Moreno D'Angelo, Gabriele Ferretti, Stefano University of Bologna Department of Computer Science and Engineering Italy University of Bologna Center for Inter-Department Industrial Research Ict Italy University of Urbino Carlo Bo Department of Pure and Applied Sciences Italy

ISBN: (纸本)9798350337846

Multilevel modeling and simulation (M&S) is becoming increasingly relevant due to the benefits that this methodology offers. Multilevel models allow users to describe a system at multiple levels of detail. From one side, this can make better use of computational resources, since the more detailed and time-consuming models can be executed only when/where required. From the other side, multilevel models can be assembled from existing components, cutting down development and verification/validation time. A downside of multilevel M&S is that the development process becomes more complex due to some recurrent issues caused by the very nature of multilevel models: how to make sub-models interoperate, how to orchestrate execution, how state variables are to be updated when changing scale, and so on. In this paper, we address some of these issues by presenting a set of design patterns that provide a systematic approach for designing and implementing multilevel models. The proposed design patterns cover multiple aspects, including how to represent different levels of detail, how to combine incompatible models, how to exchange data across models, and so on. Some of the patterns are derived from the general software engineering literature, while others are specific to the multilevel M&S application area. © 2023 IEEE.

关键词： Autonomous agents

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Deep CNN based brain tumor detection in intelligent systems

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International Journal of Intelligent Networks 2024年第1期5卷 30-37页

作者： Gupta, Brij B. Gaurav, Akshat Arya, Varsha International Center for AI and Cyber Security Research and Innovations & Department of Computer Science and Information Engineering Asia University Taiwan Taichung413 Taiwan Ronin Institute MontclairNJ United States Department of Electrical and Computer Engineering Lebanese American University Beirut1102 Lebanon Symbiosis International University Pune India School of Computing Skyline University College P.O. Box 1797 Sharjah United Arab Emirates Department of Business Administration Asia University Taiwan Taiwan Dehradun India

The early detection of brain tumor is crucial for effective treatment and improved patient prognosis in Industrial Information Systems. This research introduces a novel computational model employing a three-layer Convolutional Neural Network (CNN) for the identification of brain tumors in Industrial Information Systems. Leveraging advanced computational techniques, this proposed model can autonomously detect intricate patterns and features from medical imaging data, resulting in more accurate and expedited diagnoses. With an impressive 90 % precision rate, our model demonstrates the potential to serve as a valuable tool for medical professionals working in the field of neuroimaging. By presenting a dependable and precise computational model, this study contributes to the advancement of brain tumor identification within the domain of medical imaging. We anticipate that our methodology will aid healthcare providers in making more accurate diagnoses, thereby leading to enhanced patient outcomes. Potential avenues for future research encompass refining the model's fundamental architecture and exploring real-time therapeutic applications. © 2024 The Authors

关键词： Deep learning

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Improving the Performance of SoC's with Ecosystem Based Artificial Intelligence and Enhanced Thermal and Power Management

Improving the Performance of SoC's with Ecosystem Based Arti...

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2022 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2022

作者： Srivardhan, R. Nishanth, S.M. Velu, Susiendiran Kaarthikeyan, S. Arumugam, Dhanasekaran Chennai Institute of Technology Center for Energy Research Department of Mechanical Engineering Chennai India Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350333848

Every year collective variants of mobile SOC (system of chipset) flatforms are launched their onboard functionality is to deliver the next level of throughput and response time with reliable and sophisticated working cycles, Most of them would update the previousversion and nearly many of the updates would be *** the thermal throttling and power consumption rate play a vital role in enhancing the value of upgrade, to pave a solution to this problem is arrived based on the analyzing and measuring the performance characteristics and leave a solution due to growing demand for performance in availing VR functionalities and high graphical renderings led by the system, Even atthe end of the year thus, making the mobile platform SOC a ginormous success. This paper signifies the importance of thermal and power consumption rate processors along with the mysterious and prolonged success behind the mobile platform and a custom way that can improve SOC. © 2022 IEEE.

关键词： Electric power utilization

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High-Value Target Escort and Defense Scenario with Differential Subgame Cost 63

High-Value Target Escort and Defense Scenario with Different...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Judy, Rachael L. Fuchs, Zachariah E. Casbeer, David W. University of Cincinnati Department of Electrical Engineering and Computer Science CincinnatiOH United States Control Science Center of Excellence Air Force Research Laboratory 2210 8th St. WPAFBOH45433 United States

ISBN: (纸本)9798350316339

An Escort and Defense Scenario is presented in which a high-value Target maneuvers through a high-risk region while being escorted by a mobile, defensive agent. Along this trajectory, an Attacker may be launched from one of several possible launch locations. If a launch occurs, the three agents play out a Target-Attacker-Defender (TAD) differential game in which the Defender attempts to intercept the Attacker at maximal distance from the Target while the Attacker strives to maneuver as close as possible to the Target. Prior to launch, the Target and Defender strive to preposition themselves in advantageous positions to effectively respond to a potential threat while simultaneously moving towards a safety region. An augmented collocation-based direct optimal control method is developed to solve the escort problem by solving and utilizing the value of the TAD differential subgame at each collocation point while simultaneously optimizing the primary optimal control problem. © 2024 IEEE.

关键词： Optimal control systems

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A Novel Recurrent Neural Net based Dimension Reduction Technique for Effectual Data Visualization 2

A Novel Recurrent Neural Net based Dimension Reduction Techn...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： John, Monoo Sharma, Y.K. Balaji, N.V. Suri, Sahil Nishanthi, R. Goyal, Samaksh Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Quantum University Research Center Quantum University Uttarakhand Roorkee247667 India

ISBN: (纸本)9798350388916

t-SNE, UMAP, & Tramp have shown excellent visualization performance on various real-world datasets. These approaches may preserve global structure but not local structure, a trade-off that has always plagued them. computer science RNN machine learning is evolving. RNN models can reliably forecast numeric values or nominal classifications, but a general-purpose mechanism for RNN architecture construction has yet to be devised. computer scientists must use trial-and-error to create accurate RNN models. Models often need multiple input parameters. When there are many input factors, it might be hard to determine which ones affect model prediction the most. science calls this the "curse of dimensionality."Problems may be reduced to two dimensions, however. Humans can plan and understand two-dimensional problems. Visualizing a multi-dimensional dataset may reveal which input factors most affect a nominal or numeric outcome. Identifying these factors may help train RNN models faster by utilizing only input variables that impact the result variable. This study explores RNN training methods and uses dimensional reduction to see and interpret complicated data. © 2024 IEEE.

关键词： Recurrent neural networks

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