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A technique for optimum distribution of watermark in a document image 4

A technique for optimum distribution of watermark in a docum...

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4th IEEE International Conference on Advances in Electronics, computers and Communications, ICAECC 2022

作者： Das, Nandini Pal, Tamal Indian Institute of Engineering Science and Technology Dept. of Computer Science and Technology Shibpur Howrah India

ISBN: (纸本)9781665402392

Watermarking is a very useful technique to ensure the integrity of a document image. But when too many bits are embedded into image, it reduces quality of the document. When limited number of watermark bits are used, these should not be concentrated in background or few parts of the image. As very few existing techniques address this issue, in this paper, we propose a watermark distribution scheme, which divides image into optimum number of segments and distributes watermark based on information content such that watermark is well spread across the document. After embedding, on average the proposed scheme achieves 31.79% more area with ensuring 38.59% better quality in terms of PSNR compared to state-of-the-art schemes. © 2022 IEEE.

关键词： Signal to noise ratio

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On the Hardness of Decentralized Multi-Agent Policy Evaluation Under Byzantine Attacks

On the Hardness of Decentralized Multi-Agent Policy Evaluati...

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International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

作者： Hairi Minghong Fang Zifan Zhang Alvaro Velasquez Jia Liu Dept. of Computer Science University of Wisconsin-Whitewater Dept. of Computer Science and Engineering University of Louisville Dept. of Computer Science North Carolina State University Dept. of Computer Science University of Colorado Boulder Dept. of Electrical and Computer Engineering The Ohio State University

ISBN: (数字)9783903176652

ISBN: (纸本)9798331508722

In this paper, we study a fully-decentralized multi-agent policy evaluation problem, which is an important sub-problem in cooperative multi-agent reinforcement learning, in the presence of up to $f$ faulty agents. In particular, we focus on the so-called Byzantine faulty model with model poisoning setting. In general, policy evaluation is to evaluate the value function of any given policy. In cooperative multi-agent system, the system-wide rewards are usually modeled as the uniform average of rewards from all agents. We investigate the multi-agent policy evaluation problem in the presence of Byzantine agents, particularly in the setting of heterogeneous local rewards. Ideally, the goal of the agents is to evaluate the accumulated system-wide rewards, which are uniform average of rewards of the normal agents for a given policy. It means that all agents agree upon common values (the consensus part) and furthermore, the consensus values are the value functions (the convergence part). However, we prove that this goal is not achievable. Instead, we consider a relaxed version of the problem, where the goal of the agents is to evaluate accumulated system-wide reward, which is an appropriately weighted average reward of the normal agents. We further prove that there is no correct algorithm that can guarantee that the total number of positive weights exceeds $\vert \mathcal{N}\vert -f$ , where $\vert \mathcal{N}\vert$ is the number of normal agents. Towards the end, we propose a Byzantine-tolerant decentralized temporal difference algorithm that can guarantee asymptotic consensus under scalar function approximation. We then empirically test the effective of the proposed algorithm.

关键词： Wireless networks Reinforcement learning Approximation algorithms Function approximation Optimization Multi-agent systems Convergence

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Privacy and Data Control on Social Networks using Deep Learning 4

Privacy and Data Control on Social Networks using Deep Learn...

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4th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2022

作者： Parimala, A. Vijayalata, Y. Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Telangana Hyderabad India

ISBN: (数字)9781665462464

ISBN: (纸本)9781665462464

Online social networks are huge data exchange platforms that help to promote and share a lot of good information about products, news, education, tourism, health care, etc., also there is a great risk involved to individual's privacy and security. Online posted photos can be shared, tagged, and reposted again without having any consent from the people present in the images. In this project, we are proposing a facial recognition-based system that detects every single individual and checks their online account relationship between the individual. The post or image that is reposted, tagged, or copied by known or unknown individuals will be checked. If it is an unknown individual the images convolved through a low-pass filter kernel which removes the high-frequency content like noise and edges to smooth the images that make the image blur. For this mechanism, we proposed to use deep learning based VGG19 architecture for image classification and Haar cascade viola jones algorithm for detection, and computer vision-based blurring techniques used. algorithm performance as the area under the ROC curve (AUC) accuracy of 88% obtained. This proposed model achieved 91.53% classification accuracy and further we evaluated the model using Accuracy, Precision, Recall and F1-Score metrices and achieved 70.83%, 82%, 71% and 0.68%. © 2022 IEEE.

关键词： Deep learning

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Network-based Approach to Identify Pathways and Macromolecule Interactions that Mediate Influences of COVID-19 on the Progression of Respiratory System Diseases

Network-based Approach to Identify Pathways and Macromolecul...

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2022 International Conference on Advancement in electrical and Electronic engineering, ICAEEE 2022

作者： Podder, Nitun Kumar Shill, Pintu Chandra Rana, Humayan Kabir Saha, Subir Mimi, Afsana Corniya, Nahnun Nahar Paul, Nandita Saha, Tarun Kumar Khulna University of Engineering & Technology Dept. of Computer Science and Engineering Khulna Bangladesh Green University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Pabna University of Science and Technology Dept. of Computer Science and Engineering Pabna Bangladesh Z. H. Sikder University of Science and Technology Dept. of Computer Science and Engineering Shariatpur Bangladesh Dhaka Bangladesh Mohammadpur Fertility Services and Training Center Dhaka Bangladesh

ISBN: (数字)9781665469449

ISBN: (纸本)9781665469449

COVID-19 is an infectious illness concerning coronavirus that is transmitted through droplets propagated by an infected person exhales, coughs, or sneezes. People affected by coronavirus have a risk to occur respiratory diseases (RDs). The longevity of COVID-19 may appear a vital risk of manifesting RDs. To address these issues, we explored transcriptomic data to identify the genetic effects of COVID-19 on the development of RDs such as Bronchitis (BC), Asthma (AT), Lung cancer (LC), and Pulmonary Edema (PE). We explored GEO datasets from NCBI for COVID-19, BC, AT, LC, PE case, and control subjects. We identified COVID-19 is associated with RDs by sharing 16, 19, 27, and 59 commonly DEGs accordingly. By using these genes we performed some bioinformatics analysis and constructed diseasome networks, identified functional and ontological pathways. We formed PPIs networks and PDIs network. On the basis of PPIs and PDIs, we have identified hub proteins and constructed hub proteins network. We have successfully developed a quantitative model to identify the genetic effects of COVID-19 on the progression of RDs. We also validated our investigations through gold-benchmark datasets. Our results are an effective resource to mark out the most important influences on the development of RDs for COVID-19. © 2022 IEEE.

关键词： Genes

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A Hybrid Nonlinear Control Strategy for Dc-Dc Boost Converter in a DC Motor Driven System

A Hybrid Nonlinear Control Strategy for Dc-Dc Boost Converte...

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International Conference on electrical, computer and Communication engineering (ECCE)

作者： Atanu Baidya Arka Shaibal Palit Dept. of Electrical & Electronic Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

This paper inaugurates a hybrid non-linear control strategy for DC-DC boost converter in a DC motor driven system. Conventional control methods struggle with the non-linearities and uncertainties of the real world system that results into downgrading both performance and efficiency. The hybrid controller integrates the backstepping approach with double-integral sliding mode technique to efficiently manage the changes, disturbances and uncertainties of the DC motor driven system. This paper also incorporates a comparative simulation study with a PID controller to show the robustness and stability of the proposed hybrid controller.

关键词： Uncertainty Backstepping Computational modeling Noise DC motors Control systems Stability analysis Robustness Steady-state Oscillators

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Evaluation of Reflection Loss and SAR of ~2.4 GHz Rectangular Microstrip Patch Antenna for Wearable Applications

Evaluation of Reflection Loss and SAR of ~2.4 GHz Rectangula...

引用

International Conference on electrical, computer and Communication engineering (ECCE)

作者： Sabuj Sarkar Md Rejvi Kaysir Md. Jahirul Islam Dept. of Electrical and Electronic Engineering Khulna University of Engineering & Technology Khulna Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

This study evaluates a narrow rectangular microstrip patch antenna designed for 2.4 GHz operation, with potential applications as a wearable antenna. Two antennae were fabricated to investigate the performance (e.g. S 11 & SAR parameter) and compare them with the theoretical design that was done in CST Microwave Studio software. A 64 Portable Vector Network Analyzer (VNA) known as LitaVNA™ is utilized for conducting measurements. The performances of fabricated antennae are compared, which offer a reflection loss below -10 dB for each antenna. The measured performance indicating parameters showed excellent agreement with the simulated results ~ 2.4 GHz. In addition, a list of important parameters (both structural and electrical) of the fabricated antennas is outlined in this analysis, which must be considered carefully during the fabrication process. Finally, the antenna's SAR performance was evaluated with a human body phantom model in CST Microwave Studio for 1 gm and 10 gm tissue, showing that the SAR values are well below the international limits of 1.6 W/Kg and 4 W/Kg for 1 gm and 10 gm tissues, respectively. This analysis will aid in the effective implementation and thorough examination of the designed antenna for wearable applications.

关键词： Antenna measurements Microwave antennas Wireless communication Antenna theory Patch antennas Microstrip antennas Reflector antennas Loss measurement Frequency measurement Microstrip

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A Blockchain-based Technique to Prevent Grade Tampering: A University Perspective 3

A Blockchain-based Technique to Prevent Grade Tampering: A U...

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3rd International Conference on electrical, computer and Communication engineering, ECCE 2023

作者： Habib, Md. Ahsan Hossen Manik, Md. Motaleb Zaman, Saklain Khulna University of Engineering & Technology Dept. of Computer Science and Engineering Khulna Bangladesh School of Information Studies Syracuse University New York United States

ISBN: (纸本)9798350345360

Education plays a significant role in the advancement of a country. The quality of education that a university provides is crucial to this progress. Almost every university stores the assessed grades on the local server, making them vulnerable to different security attacks e.g. DDoS, SQL injections, cross-site scripting, etc. Any alteration to the assessed grades highly affects the students' personal, academic, and professional lives. This study proposes a blockchain-based secure approach for storing, managing and recovering students' grades by employing both a local server and a blockchain server. The immutability feature of blockchain ensures grade integrity and facilitates the implementation of a grade recovery system. Grades are encrypted before storing on the local and blockchain server to protect privacy. The blockchain server returns the index number of the corresponding block to the user for further use. Finally, the experimental analyses demonstrate the system's effectiveness over state-of-the-art works. © 2023 IEEE.

关键词： Cryptography

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Stroke Risk Prediction Using Deep Neural Networks: Empowering Healthcare Services for Early Identification and Prevention

Stroke Risk Prediction Using Deep Neural Networks: Empowerin...

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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Kurlekar, Rajvardhan Sharma, Arjun Dalvi, Sujit Shetty, Rohit Bharne, Smita Ramrao Adik Institute of Technology D.Y. Patil Deemed to Be University Dept. of Computer Engineering Navi Mumbai India

ISBN: (纸本)9798350300826

The objective of this study is to develop a predictive model for identifying individuals at risk of stroke in order to support healthcare services. The model incorporates several key characteristics that play a vital role in determining an individual's susceptibility to stroke. By considering inputs such as blood sugar level, glucose level, weight, age, and other relevant parameters, the goal is to accurately predict whether a person is prone to experiencing a stroke. Early detection and prevention can be initiated promptly for individuals identified as high-risk. To achieve this, the model employs Deep Neural Networks (DNN) as the underlying framework for creating the classifier. Prior to feeding the data into the neural network, a preprocessing step is performed to appropriately format and prepare the dataset. The neural network utilizes ReLU and sigmoid activation functions to make predictions based on the input data. Through the research efforts, A predictor has been successfully developed, achieving an impressive accuracy rate of 90% when predicting stroke risk based on user information. By leveraging the power of deep learning techniques and utilizing a comprehensive set of user inputs, the model holds great potential for aiding in the early identification and prevention of stroke cases. This research contributes to the ongoing efforts in healthcare to enhance stroke risk assessment and improve patient outcomes. © 2023 IEEE.

关键词： Deep neural networks

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Integrating IoT and Cloud for Enhanced Autonomous Vehicle Safety

Integrating IoT and Cloud for Enhanced Autonomous Vehicle Sa...

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2024 IEEE Middle East Conference on Communications and Networking, MECOM 2024

作者： Murtaza, Mohsin Iftikhar, Mohsin Raza, Mansoor Syed Hameed Mir, Zeeshan Abdulaal, Mohammed J. Nasser, Nidal RMIT University STEM College Melbourne Australia Fujairah United Arab Emirates King Abdulaziz University Dept. of Electrical and Computer Engineering Jeddah Saudi Arabia Alfaisal University College of Engineering Riyadh Saudi Arabia

ISBN: (纸本)9798350376715

Autonomous Vehicles (AVs) require constant sensing and accurate identification of dynamic environmental conditions for prompt decision-making. Limited onboard processing capabilities can impede these vehicles' ability to distinguish between road obstacles and harmless objects such as plastic bags, posing potential safety risks. This study examines a reallife fatal crash to highlight the dangers of relying solely on invehicle sensory data. It underscores the importance of employing multiple communication and sensing systems to ensure safe AV operation. The paper advocates for the integration of both onboard sensors and extensive communication networks that connect vehicles with each other and cloud-based resources. This approach facilitates enhanced data processing and the exchange of information critical for improving obstacle detection and managing blind spots. Furthermore, the study proposes an intelligent cloud-connected ecosystem that incorporates advanced network connectivity solutions to match the high speeds of autonomous vehicles, enabling real-time decision-making. This ecosystem aims to ensure seamless information and sensory data flow, which is essential for effective decision-making in autonomous vehicle navigation. © 2024 IEEE.

关键词： Plastic containers

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Optimizing Photovoltaic System Efficiency: A Comparative Study of RNN-based Controller and Incremental Conductance in MPPT

Optimizing Photovoltaic System Efficiency: A Comparative Stu...

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IEEE Conference Photovoltaic Specialists (PVSC)

作者： Babatunde D. Soyoye Indranil Bhattacharya Mary Vinolisha Trapa Banik Electrical and Computer Engineering dept. of Tennessee Tech University Cookeville TN USA Antony Dhason Electrical and Computer Engineering dept. of Tennessee Tech University Cookeville TN USA

ISBN: (数字)9781665464260

ISBN: (纸本)9781665475822

In this study, a Recurrent Neural Network (RNN)- based PV controller is thoroughly examined and compared with conventional Incremental Conductance techniques for Maximum Power Point Tracking (MPPT). The study emphasizes the improved responsiveness and efficiency of the RNN controller and focuses on the dynamic adaptation of PV systems to changing environmental conditions. In this study, the better performance of the RNN controller in monitoring the maximum power point, its fast convergence time, and its stability under various environmental conditions are demonstrated by comprehensive simulations and comparative studies. According to the results, using cutting-edge machine learning methods, such as RNNs, may greatly increase PV systems' operational effectiveness, highlighting their potential to optimize renewable energy systems. This study offers insights that advance the realm of renewable energy technologies.

关键词： Maximum power point trackers Photovoltaic systems Renewable energy sources Recurrent neural networks Machine learning algorithms Heuristic algorithms Machine learning Stability analysis Real-time systems Monitoring

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