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Demystifying AI: A Robust and Comprehensive Approach to Explainable AI

Demystifying AI: A Robust and Comprehensive Approach to Expl...

引用

2024 ieee international conference on intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Vasanth, S. Keerthana, S. Saravanan, G. Rmk College of Engineering and Technology Department of Computer Science and Engineering Thiruvallur India St.Joesph's College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India Sri Sai Ram Institute of Technology Department of Ece West Tambaram Chennai India

ISBN: (数字)9798331508432

ISBN: (纸本)9798331508432

The adoption of Artificial Intelligence (AI) and Machine Learning (ML) in various computing platforms and areas, necessitates the development of strong Explainable AI (XAI) techniques. Most current AI models are opaque about their decision-making process thereby impeding trust, debugging, and improvement. The goal of this research is to develop comprehensive robust XAI methods capable of explaining the reasoning and decision-making processes in Autonomic, Edge, Server-less, Quantum computing platforms and IoT, Business automation, Service Innovation domains where these AI models are *** study comprehensively addresses the opacity in AI models through solutions for balanced test-train splits, model evaluation, feature importance, metric imbalances, ROC curve and precision-recall curve analysis, accuracy and statistical metrics, benefits of manual review. This research aims at increasing transparency and trustworthiness within AI systems through developing as well as applying such XAI methods that can detect and mitigate biases while enhancing ethical debugging;responsible development for AI enabled computing purposes. © 2024 ieee.

关键词： Adversarial machine learning

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IoT-Enabled Advanced Health Monitoring System using ESP32 and UBI DOTS 5

IoT-Enabled Advanced Health Monitoring System using ESP32 an...

引用

5th international conference on IoT Based control Networks and intelligent systems, ICICNIS 2024

作者： Balamanikandan, A. Neeraja, Siddavatam Lakshmi Kishore, Thathireddy Venkata Deepika, Udatha Prathyusha, Settipalli Venkatachalam, K. Tirupati India Electronics and Communication Engineering Audisankara College of Engineering & Technology Gudur India

ISBN: (纸本)9798331518097

This research presents a real-time vital sign monitoring system utilizing IoT and wearable technology aiming to provide continuous health monitoring. The system is developed using an ESP32 microcontroller unit (MCU) integrated with various biomedical and environmental sensors including an ECG sensor, temperature sensor, humidity sensor, gyroscope sensor, SPO2 sensor, and heartbeat sensor. A 16x2 LCD provides local feedback, while a buzzer alerts users to abnormal conditions. To facilitate remote monitoring, NODEMCU (ESP8266) serves as a communication gateway, connecting the system to the IoT platform UBIDOTS. Through UBIDOTS, the system transmits vital data to cloud storage in real-time, enabling healthcare professionals and caretakers to monitor patients remotely. This solution is designed to support the early detection of critical health issues, improve patient outcomes, and enhance accessibility to health monitoring through scalable IoT solutions. Experimental results indicate that the proposed system effectively monitors vital signs, sending alerts and updating data in real-time demonstrating its potential in wearable health monitoring applications. © 2024 ieee.

关键词： Remote patient monitoring

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Design of centralised remote intelligent inspection system for substations 2

Design of centralised remote intelligent inspection system f...

引用

2nd international conference on Mechatronics, IoT and Industrial Informatics, ICMIII 2024

作者： Zhao, Xinjie Guo, Yibo Zhuhai Unitech Power Technology Co. Ltd Guangdong Zhuhai China Beijing University of Posts and Telecommunications School of Computer Science Beijing China

ISBN: (纸本)9798350386639

At present, China's economy is continuing to develop rapidly, accompanied by a substantial increase in productivity;all walks of life, the demand for electricity is expanding, the volume scale of the power system around the world is also expanding. At the same time the power system's operation and maintenance needs are also growing, the traditional substation manual inspection substation, has not been able to meet the high-quality development of the enterprise requirements. Combined with the characteristics of substation production and operation and maintenance, the article designs and implements a set of substation centralized remote intelligent inspection systems, using the centralized station's powerful network communication capability, highly integrated, openness, scalability, security, and other characteristics to combine the "Remote intelligent Patrol Centralized Monitoring System"and "Regional intelligent Patrol System"with the "Regional intelligent Patrol System. "By combining the system functions of "Remote intelligent Patrol Centralized Monitoring System"and "Regional Remote intelligent Patrol System,"and deploying server clusters through virtualization and cluster design, the system improves and optimizes the operation and maintenance mode of substations, and enhances the ability of substation operation and maintenance in terms of equipment state awareness, active warning, and risk management and control. The practical application results of the substation centralized remote intelligent inspection system in the power system show that the system provides full-coverage remote inspection for substations under centralized control stations, effectively manages the health status of the equipment in the stations, and reduces the operational risks of the substations. © 2024 ieee.

关键词： Risk management

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An Improved PID Method for Trajectory Tracking of Underwater Robots

An Improved PID Method for Trajectory Tracking of Underwater...

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2024 international Annual conference on Complex systems and intelligent Science, CSIS-IAC 2024

作者： Shen, Yanglin Zhang, Zhonghao Zhang, Xiyuan Cang, Naimeng Guo, Dongsheng Zheng, Jinrong Hainan University School of Information and Communication Engineering Haikou China Chinese Academyof Science Institute of Deep-sea Science and Engineering Sanya China

ISBN: (纸本)9798331504755

Aiming at the problem that the traditional PID controller regulation process of underwater robots is complicated in manual tuning, has a lot of repetitive workload, and is difficult to get the optimal parameter combinations, an Improved PID Method for Trajectory Tracking of Underwater Robots is proposed. Firstly, the underwater robot is modeled and simulated to determine the parameters of its dynamic process;the recorded parameters are substituted into the parameter tuning model, and the values of the proportional gain and the desired closed-loop bandwidth parameters are calculated;the phase overrun com- pensation parameters in the PID tuning formula are initialized, and the parameters are tuned in combination with the simulation results;and finally, the optimal parameter configurations are derived. The simulation results show that the improved PID algorithm can make the position vectors of the snorkel converge more quickly, and have smaller overshoot and smaller steady- state error than the traditional PID algorithm. © 2024 ieee.

关键词： Three term control systems

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Hybrid Deep Learning Cloud Intrusion Detection 3

Hybrid Deep Learning Cloud Intrusion Detection

引用

3rd ieee international conference on intelligent Techniques in control, Optimization and Signal Processing, INCOS 2024

作者： Karnan, Lokanayaki Mahalakshmi, S.B. Thirunavukkarasu, V. Christ University Department of Computer Science Karnataka Bangalore560029 India Coimbatore Institute of Technology Department of Artificial Intelligence & Machine Learning Tamil Nadu Coimbatore641014 India

ISBN: (纸本)9798350361186

The scalability and flexibility that cloud computing provides, organisations can readily adapt their resources to meet demand without having to make significant upfront expenditures in hardware infrastructure. Three main types of computing services are provided to people worldwide via the Internet. Increased performance and resource access are two benefits that come with using cloud computing, but there is also an increased chance of attack. As a result of this research, intrusion detection systems that can process massive amounts of data packets, analyse them, and produce reports using knowledge and behaviour analysis were created. Convolution Neural Network Algorithm encrypts data as it's being transmitted end-to-end and is stored in the cloud, providing an extra degree of security. Data protection in the cloud is improved by intrusion detection. This study uses a model to show how data is encrypted and decrypted, of an algorithm and describes the defences against attacks. When assessing the performance of the suggested system, it's critical to consider the time and memory needed to encrypt and decrypt big text files. Additionally, the security of the cloud has been investigated and contrasted with various encoding techniques now in use. © 2024 ieee.

关键词： Intrusion detection

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Automatic Decision-Making for Unmanned Surface Vehicle Operation Based on CNN-RF Hybrid Model 6

Automatic Decision-Making for Unmanned Surface Vehicle Opera...

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6th international conference on Robotics, intelligent control and Artificial Intelligence, RICAI 2024

作者： Bai, Yuting Xue, Xinyi Su, Tingli Jin, Xuebo Beijing Technology and Business University College of Computer and Artificial Intelligence Beijing China

ISBN: (纸本)9798331541699

Automatic decision-making is crucial for the autonomous operation of unmanned surface vehicles, especially in effectively utilizing multi-source information for dynamic strategy selection. In this paper, we propose a CNN-RF hybrid model for unmanned surface vehicle operations. This model integrates CNN’s feature learning with RF’s nonlinear classification for comprehensive multi-source data modeling. Our approach includes meteorological data alongside state and obstacle information, enhancing decision-making reliability. Comparative simulations with traditional methods demonstrate that our model accurately predicts collision risks and performs effective avoidance, ensuring efficient navigation in complex environments. © 2024 ieee.

关键词： Unmanned surface vehicles

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Mobile Ad Hoc Networks Supporting Adaptive Threat Detection through Intrusion Detection Effective Use of Machine Learning for cyber Defense

Mobile Ad Hoc Networks Supporting Adaptive Threat Detection ...

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2023 international conference on Innovative Computing, intelligent Communication and Smart Electrical systems, ICSES 2023

作者： Bommy, M. Vivekanandan, T. Sreeraman, Y. Jagadeesan, D. Sunil Kumar, C. Asha, G. Madanapalle Institute of Technology & Science Department of CSE Andhra Pradesh Madanapalle India School of Technology the Apollo University Department of CSE Andhra Pradesh Chittoor India Adhiparasakthi College of Engineering Department of ECE Tamilnadu Kalavai India

ISBN: (纸本)9798350319200

Due to the ever-changing nature of MANETs, novel methods of adaptive threat identification are required to ensure user safety. In this study, we investigate how machine learning may be used to improve MANET intrusion detection. To this end, we use a wide variety of machine learning models, such as Multilayer Perceptron (MLP) Neural Networks, Support Vector Machines (SVM), and Random Forests, and compare how well they can spot harmful activity in network data. The findings illustrate the advantages of the MLP Neural Network, giving heightened accuracy and flexibility. In addition, we develop a reinforcement learning-inspired adaptive learning technique to improve real-time intrusion detection by allowing the model to quickly adjust to changing network circumstances. Our results pave the way for future cross-domain applications and have far-reaching consequences for MANET network security. This study contributes to a more secure cyberspace by outlining the next steps for more adaptive and resilient protection of vital infrastructure, military networks, and emergency response networks. © 2023 ieee.

关键词： Adaptive Learning Adaptive Threat Detection cyber defense cybersecurity Dynamic Threat Analysis Intrusion Detection Mobile Ad Hoc Networks (MANETs) Multilayer Perceptron (MLP) Neural Network Support Vector Machines (SVM)

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Strategies for Data Security and Privacy Protection in Cloud Infrastructure 3

Strategies for Data Security and Privacy Protection in Cloud...

引用

3rd ieee international conference on Blockchain and Distributed systems Security, ICBDS 2024

作者： Naikwade, Namrata Pathan, Shafi Mit Art Design and Technology University Dept. of Computer Science & Engineering Pune India

ISBN: (纸本)9798350354348

In today's rapidly growing digital era, cloud computing has emerged as an important technology, providing the ability to scale resources efficiently, accessibility, and cost-effectiveness for storing and processing huge amounts of data. However, alongside its numerous benefits, the adoption of cloud computing has also introduced deep concerns regarding data protection and privacy. The current scenario of cloud computing is characterized by increase in usage of cloud services and an exponential growth of data. While organizations leverage the cloud to enhance agility and innovation, the data sharing ability of cloud and the outsourcing of data management raise significant challenges for ensuring the privacy, integrity, and availability of sensitive data. Moreover, the rising cyber threats, regulatory compliance requirements, and the possibility for unauthorized access bring out the critical importance of robust data protection mechanisms in the cloud *** paper presents a comprehensive survey of strategies for data protection and identity protection in the context of cloud based systems. It examines encryption techniques, access control mechanisms, identity management solutions, among other approaches, highlighting their strengths, limitations, and applicability in different cloud deployment models and use cases. Furthermore, the paper discusses emerging trends such as homomorphic encryption, Attributes Based Encryption (ABE) Schemes and Identity Based Encryption (IBE), which hold promise for enhancing data protection in the developing landscape of cloud technology. © 2024 ieee.

关键词： Cloud security

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STE-PFL: Spatial-Temporal Enhanced Personalized Federated Learning for IoT Cross-Domain Access Decision-Making 27

STE-PFL: Spatial-Temporal Enhanced Personalized Federated Le...

引用

27th international conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Liu, Chunwen Yu, Hongchen Xiao, Juxin Dai, Feng Yin, Jie Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Heilongjiang Univ Sch Data Sci & Technol Harbin Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9798350349184;9798350349191

Insufficient access control in the domain of cross-domain IoT (CD-IoT) can result in unauthorized access events and impede the seamless functioning of collaborative IoT applications. The introduction of federated learning facilitates the development of dynamic intelligent access decision-making models for CD-IoT systems. Nonetheless, existing research overlooks the necessity of protecting the privacy of devices and inadequately tackles local model performance and data heterogeneity issues on CD-IoT devices. In this paper, we present a privacy-preserving access decision-making model for CD-IoT based on Spatial-Temporal Enhanced Personalized Federated Learning (STE-PFL) to address the aforementioned issues. By leveraging the Dirichlet distribution, we construct two Non-IID data scenarios using the CICIDS2018 and UNSW-NB15 datasets, respectively, to thoroughly evaluate the effectiveness of the proposed access decision-making model. The results demonstrate that our model delivers commendable accuracy in CD-IoT access decision support, and surpasses other baseline models in terms of accuracy, recall, and F1-score.

关键词： IoT cross-domain access decision-making models personalized federated learning spatial-temporal enhanced network

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Boolean matrix compressed sensing

Boolean matrix compressed sensing

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2025 ieee international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Qiang Soleymani, Mahdi Mahdavifar, Hessam National Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China Halıcıoğlu Data Science Institute University of California La Jolla San DiegoCA92093 United States Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States

ISBN: (纸本)9798350368741

In real-world datasets, leveraging the low-rank and sparsity properties enables developing efficient algorithms across a diverse array of data-related tasks, including compression, compressed sensing, matrix completion, etc. Notably, these two properties often coexist in certain real-world datasets, especially in Boolean datasets and quantized real-valued datasets. To harness the advantages of low-rank and sparsity simultaneously, we adopt a technique inspired by compressed sensing and Boolean matrix completion. Our approach entails compressing a low-rank sparse Boolean matrix by performing inner product operations with a randomly generated Boolean matrix. We then propose a decoding algorithms based on message-passing techniques to recover the original matrix. Our experiments demonstrate superior recovery performance of our proposed algorithms compared to Boolean matrix completion, with equal measurement requirements. © 2025 ieee.

关键词： Belief Propagation Boolean Matrix Compressed Sensing Matrix Completion

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