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Symmetric and Asymmetric Algorithms for Susceptible Harmful in Wireless Sensor Network on FPGA Platform 9

Symmetric and Asymmetric Algorithms for Susceptible Harmful ...

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9th international conference on advanced computing and Communication systems, ICACCS 2023

作者： Saranya, L. Arvind, C. Zhu Tang, Seng Karpagam College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India University of Huaguoshan Department of Intelligent Robotics Huaguoshan Jileshijie Province China

ISBN: (纸本)9798350397376

WSNs are made up of a network of sensor nodes that are used to track environmental characteristics like pressure, temperature, etc. and communicate the data to the intended location. Networks must overcome obstacles including security, routing, problems with energy conservation, scalability, dependability, secrecy, integrity, authentication, and others when transmitting information. Different routing systems, including data-centric, location- and mobility-based, Quality of Service (QoS)-based, and multipath routing protocols, have been employed to address these issues. Even while these routing methods guarantee a successful transaction, the network still has to manage intermediary assaults that can completely alter the content of messages being carried. Therefore, a number of cryptographic approaches have been used to minimise the network. The newly developed symmetric and asymmetric algorithms are susceptible to problems with route latency, area overhead, and on-chip power consumption. Thus, to reduce these harmful actions in WSNs, an efficient reduced key size and high security based cryptographic method must be used. Utilizing the limited network resources, the main goal of the study is to increase network security while reducing network overhead and path delay-related problems. © 2023 ieee.

关键词： Network security

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An Exoskeleton Robot with More Active Degree of Freedoms and Larger Range of Motion for Hand Rehabilitation 9

An Exoskeleton Robot with More Active Degree of Freedoms and...

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9th ieee international conference on advanced Robotics and Mechatronics (ICARM)

作者： Sun, Xijin Liu, Mengqi Sun, Mingze Sun, Ning Li, Ke Shandong Univ Sch Control Sci & Engn Inst Intelligent Med Engn Jinan 250061 Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Peoples R China

ISBN: (纸本)9798350385731;9798350385724

Exoskeleton robots are a useful technique for hand rehabilitation. Most current exoskeleton robots have low active degrees of freedoms (DOFs) and confined range of motion (ROM), which may constrain the hand dexterity and motion area for rehabilitation. This study aimed to develop a novel exoskeleton robot with more active DOFs and larger ROM for hand rehabilitation. The novel exoskeleton robot was designed with 22 DOFs. The mechanical structure of the exoskeleton was optimized by kinematic modeling and optimization problem construction. The ROM was evaluated using extraction of the structure optimized robot motion space envelope. The effect of the novel exoskeleton was examined by an experiment using data of brain tissue oxy-hemoglobin (HbO). This study collected brain tissue HbO concentrations without training and with the assistance of the exoskeleton robot for comparison. Results showed that the ROM of the exoskeleton was improved from 169 cm(3) to 207 cm(3). The mean and maximum concentrations of brain tissue HbO during hand motion driven by the exoskeleton were 0.0429 (mmol/L) *mm and 0.0921 (mmol/L) *mm, respectively. By contrast, the mean and maximum without exoskeleton-assistant motion were -0.0541 (mmol/L) *mm and 0.0138 (mmol/L) *mm, respectively. These results suggested that the brain tissue HbO during exoskeleton-assisted training was notably elevated compared to non-training conditions. This is a preliminary study showing the effectiveness of the novel exoskeleton robot.

关键词： Exoskeleton (Robotics)

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A data Privacy Protection Scheme Integrating Federal Learning and Secret Sharing 5

A Data Privacy Protection Scheme Integrating Federal Learnin...

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5th ieee international conference on Power, intelligent computing and systems, ICPICS 2023

作者： Wang, Hao Zhang, Yiying Cheng, Yang Li, Qiang Zhao, Jia Li, Wenjing Tianjin University of Science and Technology Tianjin China State Grid Jiangsu Electric Power Company Taizhou Power Supply Company Taizhou China State Grid Information and Telecommunication Group Co. Ltd Beijing China Nanchang Institute of Technology Nanchang China

ISBN: (纸本)9798350333442

The explosive growth of power data in the process of new power system business operation has high utilization value, how to maximize the value of data mining at the same time, to ensure its privacy in the collection, transmission, aggregation and analysis is a critical issue that needs to be addressed. This paper proposes a data privacy protection scheme (FL-SS) based on federal learning and secret sharing. Firstly, based on the federal learning framework, the information of gateway local training model parameters is distributed and reconfigured to ensure the security of the model updating process while preventing the betrayal of internal gateways;meanwhile, a lightweight communication key negotiation mechanism is designed to protect the transmission process of model parameters and data. The comparison experiments show that FL-SS has better performance in terms of accuracy and energy consumption compared with other similar schemes. © 2023 ieee.

关键词： data mining

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3rd international Mobile, intelligent, and Ubiquitous computing conference, MIUCC 2023

3rd International Mobile, Intelligent, and Ubiquitous Comput...

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3rd international Mobile, intelligent, and Ubiquitous computing conference, MIUCC 2023

ISBN: (纸本)9798350306231

The proceedings contain 64 papers. The topics discussed include: enhancing counter synchronization in a secure communication scheme for can-based automotive embedded systems;implementation, fabrication and unit testing of phased antenna arrays for Wi-Fi indoor positioning;hybrid approach for COVID-19 severity identification;multimodal Arabic retrieval and recommendation system;key lessons from microservices for data mesh adoption;a comprehensive review on transformers models for text classification;and a run-length and discrete cosine transform based technique for image splicing detection.

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Monte Carlo-based Measurement Uncertainty Propagation on a Virtual Sensor for Real- Time Human Balance Exergame Analysis 3

Monte Carlo-based Measurement Uncertainty Propagation on a V...

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3rd ieee international conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2024

作者： Giulietti, Nicola Fabiocchi, Davide Giberti, Hermes Carnevale, Marco Università degli studi di Pavia Dip. Ingegneria Industriale e dell'Informazione Pavia Italy

ISBN: (纸本)9798350378009

Nowadays, increasingly advanced Artificial Intelligence techniques are progressively being integrated with design methodologies. The use of advanced inferential models trained on real-time data is making it possible to analyze the dynamic behaviors of very complex systems simply by observing them through vision-based measurement techniques, offering an innovative approach to indirect measurement of forces at play without the need for additional bulky and difficult-to-install sensors. However, it is not always possible to estimate the reliability of these systems because they depend on variables that are poorly controlled and difficult to assess. In addition, the assessment of measurement uncertainty is critical for the adoption of data-driven artificial intelligence models in commercial products and services. This paper explores the possibility of using cameras for the development of a virtual sensor for the dynamic analysis of human balance in a balance platform system. The developed virtual sensor is characterized by estimating the propagation of the measurement uncertainty through the use of the Monte Carlo method. The results obtained make it possible to evaluate the metrological performance of the proposed method. © 2024 ieee.

关键词： intelligent systems

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IoT-Driven Precision Farming: Enhancing Crop Yield and Sustainability Through Real-Time data Analytics

IoT-Driven Precision Farming: Enhancing Crop Yield and Susta...

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2024 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Jaya Krishnan, A.P. Laldinpuia Hema, R. Lotus, Rayappan Nair, Resmi R. Ragini, Y.P. Sona College of Technology Department of Ece Tamil Nadu Salem India Mizoram University Department of Mathematics and Computer Science Mizoram Aizawl India Saveetha Engineering College Department of Ece Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha University Department of Biosciences Tamil Nadu Chennai India

ISBN: (纸本)9798331543617

Precision farmers are enhancing crop yields and crop life through the implementation of IoT-based agricultural management. This investigation investigates the methods by which precision farmers are employing Internet of Things (IoT) devices for real-time crop management through data collection, analysis, and decision-making. The impact of IoT on soil moisture, nutrient levels, and ambient conditions is investigated in this study using a sensor network, drones, and smart irrigation systems. Yield, resource efficacy, and cost-effectiveness are significantly enhanced in comparison to conventional agricultural methods. The IoT is employed in precision farming to address current agricultural issues in novel ways. It accomplishes this by precisely regulating agricultural inputs and providing data analytics insights. According to the research, IoT technology has the potential to address the increasing global demand for food in a sustainable and environmentally favorable manner. The study scales and interoperate the IoT to advance precision farming. © 2024 ieee.

关键词： Smart agriculture

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Performance optimization of Learning Trees modules in the ECL repository 3

Performance optimization of Learning Trees modules in the EC...

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3rd international conference on Communication, Control, and intelligent systems, CCIS 2024

作者： Shashank, B. Sudershan, K.S. Shobha, G. Manonmani, S. Dev, Roger Rv College of Engineering Dept. of Computer Science Bangalore India LexisNexis Risk Solutions United States

ISBN: (纸本)9798331528201

Multi-Node computation, also known as distributed computing, is a paradigm that allows for the efficient utilization of multiple interconnected nodes or machines to perform complex computational tasks. By dividing the workload and distributing it across multiple nodes, multi-node computation enables parallel processing, scalability, and improved performance for a wide range of applications. HPCC systems® from LexisNexis Risk Solutions is a proven, open source solution for big data insights that can be implemented by businesses of all sizes. HPCC systems offers a consistent data-centric programming language, two processing platforms and a single, complete end-to-end architecture for efficient processing. ECL is the Enterprise Control Language designed specifically for huge data projects using the HPCC systems platform. It is extremely scalable and a powerful declarative data-processing language with native multi-processing capabilities and used for HPCC systems. Some core ECL functions, like the ones handling Learning Tree algorithms of Machine Learning Bundle are recursive in nature and hence high computational time is needed. This paper thus presents a solution to achieve optimized processing of ML learning tree algorithms in ECL through embedding python libraries. The proposed approach was tested on standard datasets and a significant decrease in computation time. For Real Estate Valuation Regression dataset the computation time was reduced from 11.371 seconds to 0.886 seconds using the proposed method showing 1324% improvement with no impact on the accuracy. Random Forests are also implemented using the proposed Decision Tree method allowing the users to specify the number of decision trees to be used in the forest. © 2024 ieee.

关键词： Parallel processing systems

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intelligent Eye Strain Relief Glasses using IoT and Naive Bayes Solutions for Digital Wellness 5

Intelligent Eye Strain Relief Glasses using IoT and Naive Ba...

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5th international conference on Emerging systems and intelligent computing, ESIC 2025

作者： Thanalakshmi, S. Geethamahalakshmi, G. Azath, H. Prabu, K. Kumanan, T. Srinivasan, C. Gojan School of Business and Technology Department of Management Studies Redhills Tamil Nadu Chennai India Easwari Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology School of Computing Tamil Nadu Chennai India Gojan School of Business and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Dr Mgr Educational and Research Institute Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331522100

Eye strain and its effects on general health have become more pressing issues due to the pervasive nature of digital devices in modern life. This work offers a new approach by combining the Internet of Things (IoT) with Naive Bayes classification for smart eyewear that reduces eye strain. The proposed system aims to reduce digital eye strain by adapting the lens's characteristics in real-time to user behavior and ambient factors. Using the IoT architecture, data like screen time, distance from the device, and ambient lighting conditions may be captured in real-time when users interact with digital displays. Using Naive Bayes classification, this data is processed to identify probable causes of eye strain and suggest suitable eyewear changes to reduce pain. The proposed system provides a preventative method of fostering digital well-being using software and hardware components, such as sensors, microcontrollers, and machine learning algorithms. These smart glasses provide hope for a future free of the negative impacts of digital device use on eye health by making personal recommendations based on each user's unique requirements. © 2025 ieee.

关键词： Ambient Sensing Eye Health Personalized Recommendations Smart Glasses User Interaction Analysis Wearable Technology

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Holistic Knowledge Framework for Improving PC Design using Big data Analytics 7

Holistic Knowledge Framework for Improving PC Design using B...

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7th international conference on intelligent computing and Control systems, ICICCS 2023

作者： Rajendran, T. Vignesh, O. Priyadharsini, V. Jagadeesh, K. Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Easwari Engineering College Department of Electronics and Communication Engineering Chennai India Akshaya College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India GRT Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Tiruttani India

ISBN: (纸本)9798350397253

Cyberspace is massively expanding every day, and the users of these digital devices are looking for more innovative applications to ease their day-to-day work. The main objective of any device is to use available system resources at a maximum optimum level and give high performance to the users. Especially in personal computers (PC), there is still a massive gap in the user experience and utilization of available system resources. There are several situations where error conditions and system responsiveness lag. Over several years the digital world evolved and provided high-performance computing. Furthermore, the PC design enhancements could be carryout through the participation of PC users. That could be accomplished by collecting the system performance indicators, system behavior and properties, software application usage, user working log, and other wide range of real-time data from the PC. These telemetry data needed to be retrieved from an extensive array of PCs then these datasets would be reposited on the centralized servers. By applying the big data analytical methods and other mining algorithms over these extensive datasets to identify the hidden patterns and discover more knowledge about the key indicators that would help improve PC performance and user experiences. Through this knowledge acquisition model, the organizations and research institutions could develop the most resilient and adoptable PC designs and software applications developments. © 2023 ieee.

关键词： Personal computers

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Enhancing Customer Personality Prediction using advanced Machine Learning Techniques and data Balancing Strategies: A Comprehensive Approach to Addressing Imbalanced datasets in Marketing Analytics 1

Enhancing Customer Personality Prediction using Advanced Mac...

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1st international conference on intelligent systems and Computational Networks, ICISCN 2025

作者： Selvalakshmi, V. Sree, T. Manju Usha Saranya, S. Devi, A. Uma Basha, Md Shaik Amzad Velammal College of Engineering and Technology Department of Master of Business Administration Madurai India Department of Master of Business Administration Hyderabad India Rajalakshmi Institute of Technology Department of Artificial Intelligence and Data Science Chennai India Alpha College of Engineering Department of Management Studies Chennai India Hyderabad India

ISBN: (纸本)9798331529246

This study tackles imbalanced customer response prediction by integrating advanced resampling methods with diverse machine learning and ensemble techniques. Novel resampling approaches (SMOTE, Borderline-SMOTE, ADASYN, and SMOTEENN) were systematically applied alongside Logistic Regression, Random Forest, Gradient Boosting, LightGBM, SVM, and Neural Networks. Stacking and voting ensembles further consolidated individual model strengths. Results show that coupling resampling with ensembles significantly boosts minority class metrics. For example, ADASYN combined with LightGBM achieved a 95% accuracy and substantially improved minority class recall and precision, while SMOTEENN with a voting ensemble reached a 99% recall, albeit with a slight precision trade-off. These findings underscore that employing sophisticated data rebalancing techniques in conjunction with hybrid ensembles can surpass traditional models, offering a balanced improvement in metrics such as accuracy, precision, recall, and F1 score. The practical significance lies in enabling more reliable predictions in highly imbalanced scenarios, ultimately enhancing decision-making processes. © 2025 ieee.

关键词： Adversarial machine learning

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