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International Conference on ent Computing and Control Systems (ICICCS)

作者： Vineela Madireddy Bhagya Lakshmi Medam Sai Vardhan Reddy Manne Navaneeth Yadav Madhugani Dept. Computer Science and Engineering -AI&ML Vignan Institute of Technology and Science Nalgonda India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Bone fractures are among the most common physical injuries, affecting individuals across all age groups and requiring prompt and precise diagnosis. However, radiologists often face challenges in accurate fracture identification due to variations in image quality and complexity. The present work investigates a robust system for the automatic detection and classification of fractures in X-radiation (X-ray) images. The proposed model integrates Convolutional Neural Networks (CNN) and MobileNet with a hybrid approach that combines MobileNet and Random Forest to improve classification accuracy. The system is designed to be user-friendly, incorporating modules for registration, data processing, and real-time classification of fractures into 11 distinct types, including avulsion fracture, impacted fracture, longitudinal fracture, comminuted fracture, greenstick fracture, oblique fracture, pathological fracture, spiral fracture, and others. Experimental results demonstrate that the hybrid model significantly enhance diagnostic accuracy, underscoring its potential application in medical settings.

关键词： Pathology Accuracy Spirals Computational modeling Bones Real-time systems Convolutional neural networks X-ray imaging Random forests Injuries

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Basic Study on Transmission Power Enhancement in Dynamic Wireless Power Transfer Systems using Circular–Solenoid Multi-coil

Basic Study on Transmission Power Enhancement in Dynamic Wir...

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Wireless Power Week (WPW)

作者： Harutaka Suzuki Ryosuke Ota Dept. of Electrical Engineering and Computer Science Tokyo Metropolitan University Tokyo Japan

ISBN: (数字)9798350349139

ISBN: (纸本)9798350349146

This paper proposes a method for stable power transmission in dynamic wireless power transfer (DWPT) systems with a coil array installed on the ground. The proposed method adopts a Circular-Solenoid type multi-coil on the electric vehicle (EV) side to utilize both vertical and horizontal magnetic fields, achieving stable power transmission all over the DWPT lane. In the experiments, a prototype was constructed, and the magnetic coupling characteristics of the ground and EV side coils were clarified. The power transmission characteristics were then derived, and a guideline was provided for efficiently and stably transmitting 3 kW of power to EVs all over the DWPT lane.

关键词： Coils Couplings Wireless communication Magnetic field measurement Power transmission Prototypes Wireless power transfer

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Enhancing Hyperledger Fabric Security with Lightweight Post-Quantum Cryptography and National Cryptographic Algorithms

Enhancing Hyperledger Fabric Security with Lightweight Post-...

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Open Innovations Association (FRUCT), Conference of

作者： Vinay Kumar Kasula Sarath Babu Rakki Rajkumar Banoth Dept. Information Technology University of the Cumberlands Williamsburg KY USA Dept. of Computer Science and Engineering JNTUH University College of Engineering Hyderabad TG India Dept. of Computer Science University of Texas at San Antonio San Antonio TX USA

ISBN: (数字)9789526524634

ISBN: (纸本)9798331595425

Hyperledger Fabric is a scalable and modular consortium blockchain platform designed for enterprise applications, where cryptographic algorithms play a fundamental role in ensuring data security and integrity. However, the native Fabric framework lacks support for national cryptographic standards, necessitating the integration of secure cryptographic mechanisms. This study proposes an enhanced approach to embedding national cryptographic algorithms into the Fabric platform. First, Graph-based Dependency Analysis is employed to investigate the interaction logic among Fabric components and the invocation of cryptographic functions, facilitating an efficient integration strategy. Next, the Lightweight Post-Quantum Cryptography (L-PQC) framework, which enhances resistance to quantum threats while maintaining computational efficiency, is utilized to integrate SM2, SM3, and SM4 cryptographic algorithms into Fabric’s Blockchain Cryptographic Service Provider (BCCSP) module. Subsequently, a Microservices-Based Cryptography Integration mechanism is designed to establish seamless mapping between Fabric’s cryptographic function calls and the national cryptographic algorithm interfaces, ensuring compatibility and interoperability. Finally, the implementation is evaluated using Blockchain Simulation Environments, where a fabric-gm consortium blockchain instance is deployed to validate the correctness and efficiency of the embedded cryptographic modules. Comparative analysis with the original Fabric platform reveals that the enhanced system introduces a 2.5% increase in network startup time, a 1.8× rise in transaction latency, and a 7.5% increase in dynamic certificate generation time, while maintaining operational performance within acceptable limits. The proposed integration strategy ensures secure and efficient cryptographic support within Hyperledger Fabric, making it resilient to emerging cryptographic threats.

关键词： Resistance Technological innovation Quantum computing Distributed ledger Heuristic algorithms System performance Fabrics Blockchains Cryptography Standards

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CloudSec Innovation: Enhanced Data Security with Multi-Tier Encryption Systems 8

CloudSec Innovation: Enhanced Data Security with Multi-Tier ...

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Issac Immanuel, S.R. Jenefa, A. Edward Naveen, V. Santhiya, P. Sangeetha, R. Lincy, A. Karunya Institute of Tech. and Sci Dept. of Cse Coimbatore India Sri Shakthi Institute of Engg. and Tech. Dept. of Cse Coimbatore India Karunya Institute of Tech. and Sci School of Computer Science and Tech Coimbatore India National Engineering College Dept. of Cse Kovilpatti India

ISBN: (纸本)9798350386578

In the evolving landscape of cloud computing, data security remains a paramount concern, necessitated by the increasing sophistication of cyber threats. Traditional encryption methodologies, primarily Single-Tier systems, are proving insufficient against the advanced tactics employed by modern cyber attackers. The CloudSec Innovation introduces a novel Multi-Tier encryption framework designed to fortify cloud data security by integrating symmetric (AES-256), asymmetric (RSA-OAEP), and post-quantum (Kyber-512) encryption techniques. The system is rigorously evaluated using a dataset simulated to mirror diverse cyber-attack scenarios and data sensitivity levels. The experimental analysis demonstrates a significant enhancement in security efficacy;the Multi-Tier system drastically reduces the success rate of cyber attacks to 5% from the 25% seen with Single-Tier systems, while also prolonging the average breach time from 2 to 10 hours. These results, tested across scenarios like SQL Injection, Brute Force, and Phishing attacks, attest to the robustness and comprehensive protection offered by the MultiTier approach. Conclusively, 'CloudSec Innovation' emerges as a superior solution, providing a sophisticated and layered defense mechanism that is adept.at securing sensitive data in cloud environments against an array of cyber threats, thus establishing a new benchmark in cloud data encryption technology. © 2024 IEEE.

关键词： Cloud security

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A Comparative Analysis of Early Departure Buttons in Coordinated Control of EV Charging

A Comparative Analysis of Early Departure Buttons in Coordin...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Baver Ozceylan Gerwin Hoogsteen Johann L. Hurink Dept. of Electrical Engineering Mathematics and Computer Science University of Twente Enschede The Netherlands

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

The increasing adoption of electric vehicles (EVs) necessitates the efficient management of large EV parking facilities to prevent them from exceeding grid capacity and to improve the overall user experience. This paper introduces a data-driven approach for coordinated control of EV charging in an office parking facility, integrating Early Departure Buttons (EDB) into the system. These buttons provide a binary option for users to indicate an earlier departure than a predefined time. We employ the EDB data to improve departure time estimations and to address issues where EVs receive no or only very low energy. We utilize three datasets originating from different geographical locations. One dataset displays a user pattern where users leave shortly after completing their charging, unlike the other datasets which follow typical working hours. Our simulations show that the unique user pattern significantly increases fairness among users, and integrating EDBs improves fairness for the other datasets to levels similar to those of quick station turnover.

关键词： Measurement Schedules Europe Estimation Charging stations Electric vehicle charging User experience Smart grids

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Comprehensive Forecasting-Based Analysis of Hybrid and Stacked Stateful/Stateless Models

arXiv

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arXiv 2024年

作者： Saha, Swayamjit Dept. of Computer Science and Engineering Mississippi State University Starkville United States

Wind speed is a powerful source of renewable energy, which can be used as an alternative to the nonrenewable resources for production of electricity. Renewable sources are clean, infinite and do not impact the environment negatively during production of electrical energy. However, while eliciting electrical energy from renewable resources viz. solar irradiance, wind speed, hydro should require special planning failing which may result in huge loss of labour and money for setting up the system. In this paper, we discuss four deep recurrent neural networks viz. Stacked Stateless LSTM, Stacked Stateless GRU, Stacked Stateful LSTM and Statcked Stateful GRU which will be used to predict wind speed on a short-term basis for the airport sites beside two campuses of Mississippi State University. The paper does a comprehensive analysis of the performance of the models used describing their architectures and how efficiently they elicit the results with the help of RMSE values. A detailed description of the time and space complexities of the above models has also been discussed. © 2024, CC BY.

关键词： Forecasting

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Modular Deep Reinforcement Learning: Enhancing Flexibility through Agent Splitting

Modular Deep Reinforcement Learning: Enhancing Flexibility t...

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IEEE International Conference on Big Data

作者： Chong Tian Danda B. Rawat Dept. of Electrical Engineering & Computer Science Howard University Washington DC USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Deep reinforcement learning (DRL) has shown remarkable success in tackling complex tasks by learning representations directly from raw data. However, as DRL agents become increasingly sophisticated, they often face challenges in adapting to new tasks or environments due to their monolithic structure. In this paper, we prove that by decomposing the agent into modular components, we enable more efficient adaptation as well as more efficient than that of the singular DRL agent on tackling complex tasks. Overall, our work contributes to advancing the field of DRL by introducing a flexible and scalable framework for agent decomposition. By enabling the creation of modular agents, we empower DRL systems to efficiently adapt to changing environments, learn new tasks with minimal intervention, and achieve higher levels of performance and versatility.

关键词： Big Data Deep reinforcement learning Faces

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XAI for Understanding the Success in Curbing COVID-19 6

XAI for Understanding the Success in Curbing COVID-19

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6th International Conference on Intelligent Systems and computer Vision, ISCV 2024

作者： Bouktif, Salah Kunnath, Nimmi Ouni, Ali CIT United Arab Emirates University Dept. of Computer Science and Software Engineering Al Ain United Arab Emirates University of Quebec École de Technologie Supérieure MontrealQC Canada

ISBN: (纸本)9798350350180

The Covid-19 pandemic has prompted governments worldwide to implement various non-pharmaceutical interventions (NPIs) in an effort to curb the pandemic to attenuate the harmness of the pandemic. However, there is a debate about how to assess the effectiveness of these interventions. A set of indicators has been used to monitor the outbreak such as case counts, infection rates, death rate, hospitalization. In this study, we contribute to the debate by firstly proposing a definition of the success in managing the pandemic in terms of mortality rate, socio-demographic and epidemiological factors. Secondly, we propose a modeling framework based on (1) analysing the time series pandemic data and (2) employing eXplainable Artificial Intelligence (XAI) to provide interpretability of the successfulness of countries in responding to the pandemic. By using a rich dataset collected by Oxford COVID-19 Government Response Tracker (OxCGRT), we built a success model based on Random Forest Regressor for time series analysis and we identified the major factors/practices/measures/protocols. Our findings indicate a significant relationship between policy compliance levels and death counts, highlighting the importance of considering mortality outcomes in evaluating the efficacy of interventions. Additionally, we discovered that socio-demographic and epidemiological factors such as elderly populations aged (65+), prevalent cardiovascular disease, high diabetes prevalence, increased smoking rates, and reduced life expectancy based on the person correlation coefficient are key determinants of success in managing the pandemic. © 2024 IEEE.

关键词： COVID-19

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SynthBA: Reliable Brain Age Estimation Across Multiple MRI Sequences and Resolutions 3

SynthBA: Reliable Brain Age Estimation Across Multiple MRI S...

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3rd IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2024

作者： Puglisi, Lemuel Rondinella, Alessia De Meo, Linda Guarnera, Francesco Battiato, Sebastiano Ravi, Daniele University of Catania Dept. of Math and Computer Science Catania Italy University College London Department of Neuroinflammation London United Kingdom School of Physics Engineering and Cs University of Hertfordshire Hatfield United Kingdom

ISBN: (纸本)9798350378009

Brain age is a critical measure that reflects the biological ageing process of the brain. The gap between brain age and chronological age, referred to as brain PAD (Predicted Age Difference), has been utilized to investigate neurodegenerative conditions. Brain age can be predicted using MRIs and machine learning techniques. However, existing methods are often sensitive to acquisition-related variabilities, such as differences in acquisition protocols, scanners, MRI sequences, and resolutions, significantly limiting their application in highly heterogeneous clinical settings. In this study, we introduce Synthetic Brain Age (SynthBA), a robust deep-learning model designed for predicting brain age. SynthBA utilizes an advanced domain randomization technique, ensuring effective operation across a wide array of acquisition-related variabilities. To assess the effectiveness and robustness of SynthBA, we evaluate its predictive capabilities on internal and external datasets, encompassing various MRI sequences and resolutions, and compare it with state-of-the-art techniques. Additionally, we calculate the brain PAD in a large cohort of subjects with Alzheimer's Disease (AD), demonstrating a significant correlation with AD-related measures of cognitive dysfunction. SynthBA holds the potential to facilitate the broader adoption of brain age prediction in clinical settings, where re-training or fine-tuning is often unfeasible. The SynthBA source code and pre-trained models are publicly available at https://***/LemuelPuglisi/SynthBA. © 2024 IEEE.

关键词： Deep learning

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Color correction by QAB (Quantum particle swarm optimization-ant colony optimization- back propagation) method 16

Color correction by QAB (Quantum particle swarm optimization...

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16th IEEE International Conference on computer science and Education, ICCSE 2021

作者： Sumaiya Armanuzzaman, M.D. KUET Dept. of Computer Science and Engineering Khulna Bangladesh Univarsity at Buffalo Dept. of Computer Science and Engineering NY United States

ISBN: (纸本)9781665414685

Now-a-days color correction has been so well accepted method for recovering color information. We proposed a color correction technique based on QPSO-ACO-BP algorithm. Firstly, the QPSO and ACO algorithms are merged. Then the optimized values are retrieved through the merged QPSO-ACO algorithm. BP model is utilized for accomplishing a model regarding this. This model performed with Macbeth Color Checker on the images for color correction. The experimental results show that our proposed technique goes well for color correction. We checked some quality metrics like PSNR (Peak Signal to Noise Ratio) and MSE (Mean Square Error) for the evaluation of results and found the desired improvement. © 2021 IEEE.

关键词： Color

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