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Intrusion detection Study of Nuclear Power Plant Electronic systems Based on Machine Learning Optimization Algorithms

Intrusion Detection Study of Nuclear Power Plant Electronic ...

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Electronic Information Engineering and Computer Communication (EIECC), international conference on

作者： Xiaoyan Zhang School of Management Shanghai University Shanghai China

In modern society, the rapid development of information technology makes a variety of electronic devices are widely used, and the electronic systems in these devices are often the key to the control system, and their stability and security are crucial to the operation of the whole system. This paper studies and compares the prediction ability of various machine learning algorithm models in the intrusion detection of electronic systems in nuclear power plants. The results show that the decision tree model and random forest model have the best prediction ability, which is far better than the other models. The linear regression model and KNN model have poor prediction ability, while the BP neural network model has poor prediction effect and is not suitable for intrusion detection of nuclear power plant electronic system. Intrusion detection in electronic systems of nuclear power plants is an important security problem, so it is important to study the prediction ability of different machine learning models for this problem. In this paper, through the experimental comparison of multiple machine learning models, we found that the decision tree model and random forest model have the best prediction ability, which indicates that these two models have high prediction ability and accuracy in nuclear power plant electronic system intrusion detection. The linear regression model and KNN model have poorer predictive ability, which may be due to the inability of these two models to capture the complex relationships in the intrusion detection of nuclear power plant electronic systems. And the BP neural network model has poor predictive effect, which may be due to the fact that the model is prone to overfitting phenomenon, resulting in inaccurate prediction results. The findings of this paper show that these conclusions provide valuable references for intrusion detection in electronic systems of nuclear power plants.

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Penetration of Artificial intelligence Techniques to Enhance the Agricultural Productivity and the Method of Farming;Opportunities and Challenges

Penetration of Artificial Intelligence Techniques to Enhance...

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Innovative Practices in technology and Management (ICIPTM), international conference on

作者： Ramakrishnan Raman Sai Mounika Muramulla Sreelekha Ponugoti Srinivas. O K. Dhinakaran Ramu Kuchipudi C.R.Rene Robin Symbiosis Institute of Business Management Pune India Symbiosis International Pune India Department of Zoology and Aquaculture Acharya Nagarjuna University Guntur India Department of Computer Science RMK College of Engineering and Technology Thiruvallur India Department of Computer Science (A.I & M.L) S. R. University Hasanparthy India Department of Computer Science Misrimal Navajee Munoth Jain Engineering College Chennai India Department of Information Technology Chaitanya Bharathi Institute of Technology Hyderabad India Department of Computer science and Engineering Sri Sai Ram Engineering College Chennai India

Each nation's economic sector depends heavily on agriculture. Every day, more people live on the planet, which raises the requirement of food. For the time being, farmers' conventional methods are unable to meet the demand. As a result, new automation techniques are launched to meet these needs and give many people in this industry excellent job prospects. In every industry, including banking, robotics, agriculture, and education, artificial intelligence(AI) has emerged as one of the most crucial technologies. It is playing a very important part in the agriculture sector and changing the agriculture industry. AI protects the agricultural sector from a variety of concerns, including food safety, population expansion, climate change, and employment problems in this industry. Due to AI, the agriculture system of today has advanced to a new level. Real-time crop monitoring, production, harvesting, processing, and marketing have all been enhanced by artificial intelligence. Numerous cutting-edge computer-based systems are created to identify several significant factors, including weed identification, yield identification, crop quality, and many others.

关键词： Service robots Sociology Crops Training data Agriculture Real-time systems Safety

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Using machine learning principles, the classification method for face spoof detection in artificial neural networks

Using machine learning principles, the classification method...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： P. Saravanan Aparna Pandey Kapil Joshi Ruchika Rondon Jonnadula Narasimharao Afsha Akkalkot Imran Computing Technologies SRM Institute of Science and Technology Chennai Tamilnadu Computer Science and Engineering Dr. C. V. Raman University Uttaranchal Institute of Technology Uttaranchal University Department of Petroleum Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science & Engineering CMR Technical Campus Hyderabad Telangana Department of Computer Engineering Savitribai Phule Pune University Pune Karnataka

The usage of biometric technologies for person authentication and verification has grown significantly, and these systems are now widely employed. This biometrics is essential to personal, governmental, and international security. This paper's major strategy is to explain the value of biometric systems. These days, various biometric system types are in use. These biometric technologies include facial recognition, voice recognition, palm vein biometrics, and iris recognition. Spoofing is the fundamental danger to all of these biometric systems. When compared to other biometric systems, the facial recognition biometric system is more commonly employed. Face-spoofing techniques include mask attacks, photographic and video attacks, and We examine various techniques for spotting Face spoofing. Face has recently received more attention in a wide range of fields because to its security and convenience. Biometric system-based face detection frequently employed for authentication of applications where human faces are the recognition of daily life as well as retaining of sensitive information. However, face spoofing assaults continue to pose a threat to face recognition systems. Although several face spoofing detection methods have been proposed by researchers and have demonstrated excellent performance, our goal is to create a system for face spoofing using machine learning algorithms.

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Health State Estimation of Lithium-ion Batteries Based on APDE-Bi-GRU-Attention Model

Health State Estimation of Lithium-ion Batteries Based on AP...

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Energy Engineering and Power systems (EEPS), international conference on

作者： Zheng Chang Jie Liu Bingyu Li Xuhao Du Ziwen Cai Le Zhu Electric Power Research Institute State Grid Hebei Electric Power Corporation Shijiazhuang China School of Artificial Intelligence Hebei University of Technology Tianjing China

To maintain safe and dependable battery operation and lower battery system maintenance costs, state of health (SOH) estimate, one of the primary BMS tasks for electric vehicles, is essential. In order to improve the SOH prediction accuracy of lithium battery, a SOH prediction method based on the improved differential evolution algorithm (APDE) and the bi-directional gated recurrent unit network (Bi-GRU-attention) combined with the attention mechanism is proposed. Firstly, the feature factors related to battery capacity are extracted from battery charging data and correlation analysis is performed, and the four with high correlation are selected as inputs of model. Then, the Attention mechanism is applied to the Bi-GRU network to form the Bi-GRU-attention network, and the key parameters of the network parameters are optimally searched by the improved differential evolution algorithm (APDE) to build the final prediction model. Experiments are conducted on NASA dataset, and the results show that the APDE-Bi-GRU-attention method has higher prediction accuracy and fit, which verifies the feasibility of the prediction method.

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3rd international conference on Ubiquitous Security, UbiSec 2023

3rd International Conference on Ubiquitous Security, UbiSec ...

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3rd international conference on Ubiquitous Security, UbiSec 2023

ISBN: (纸本)9789819712731

The proceedings contain 29 papers. The special focus in this conference is on Ubiquitous Security. The topics include: Impact of Library Code in Binary Similarity systems;how Does Post-quantum Cryptography Affect Central Bank Digital Currency?;FRAD: Front-Running Attacks detection on Ethereum Using Ternary Classification Model;a Comprehensive Survey of Attack Techniques, Implementation, and Mitigation Strategies in Large Language Models;process Mining with Programmable Logic Controller Memory States;honey-Gauge: Enabling User-Centric Honeypot Classification;Improving DNS Data Exfiltration detection Through Temporal Analysis;deploying Post-quantum Algorithms in Existing Applications and Embedded Devices;SCOrd: Shuffling Column-Oriented Relational Database to Enhance Security;A SLAHP in the Face of DLL Search Order Hijacking;poison Egg: Scrambling Federated Learning with Delayed Backdoor Attack;channel Spatio-Temporal Convolutional Network for Trajectory Prediction;multi-NetDroid: Multi-layer Perceptron Neural Network for Android Malware detection;SmartBuoy: A Machine Learning-Based detection Method for Interest Flooding Attacks in VNDN;loft: An Architecture for Lifetime Management of Privacy Data in Service Cooperation;privacy-Preserving Blockchain-Based Traceability system with Decentralized Ciphertext-Policy Attribute-Based Encryption;a Probability Mapping-Based Privacy Preservation Method for Social Networks;truFaaS - Trust Verification Framework for FaaS;detection of Cyberbullying in Social Media Texts Using Explainable Artificial intelligence;privacy Preserving Elder Fall detection Using Deep Learning;research on Authorization Model of Attribute Access Control Based on Knowledge Graph;simulation of Mixmining Reward Parameters for the Nym Mixnet;blockchain-Based Privacy-Preservation Platform for Data Storage and Query Processing;is It Really You Who Forgot the Password? When Account Recovery Meets Risk-Based Authentication;a Unified Knowledge Graph to Permit Inte

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Revolutionizing Weed detection in Agriculture through the Integration of IoT, Big Data, and Deep Learning with Robotic technology

Revolutionizing Weed Detection in Agriculture through the In...

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Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), international conference on

作者： Nafil Khalid Hajar Elkhiri Elyazyd Oumaima Nouhaila ElFahsi Zaglami Fatima Zahra Kobbane Abdellatif ENSIAS Mohammed V University Rabat Morocco

The objective of this study is to develop a weed detection and classification technique that can be utilized by robotic weed control systems. The approach leverages deep learning-based image processing algorithms to classify plants as either crops or weeds based on their features. A categorization strategy is applied to identify weeds, which can grow both inside and outside of crop rows. Data collected from crop image wavelet analysis is initially used to differentiate between crops and weeds, and the farmer receives a phone alert. Afterwards, the farmer provides the robot with instructions to spray the necessary areas. The entire model is executed by a cutting-edge real-time object detection system, YOLOv4 (You Only Look Once). During testing, this model has shown superiority over classifier-based systems in several aspects, such as achieving higher accuracy rates and being able to evaluate the full context of the image for all predictions. For instance, the deep learning-based image processing algorithms used in this technique can differentiate between crops and weeds with higher accuracy compared to traditional classifier-based systems. Additionally, the YOLOv4 object detection system used in this study is capable of analyzing the entire image to make predictions, taking into account the context of each object. This represents a significant improvement over traditional classifier-based systems that often rely on local features to make predictions, which can lead to misclassifications

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Research in Variable Period Flexible detection Scheduling Problem Based on Risk Assessment 23

Research in Variable Period Flexible Detection Scheduling Pr...

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Proceedings of the 2023 3rd international conference on Big Data, Artificial intelligence and Risk Management

作者： Yewei Feng Liang Ao Xinxin Li Xueli Deng Technology and Engineering Center for Space Utilization Chinese Academy of Sciences China

ISBN: (纸本)9798400716478

This article takes the component testing laboratory as the research object, and focuses on the problem of plan invalidation caused by order returns, delays, rework, and other situations during the component testing process. This article proposes a scheduling model and algorithm based on the scheduling problem of component inspection orders with changes in time, taking into account the factors of changes in inspection test time. The optimization objectives are the average inspection cycle and the risk of deviation from the scheduling plan. According to the verification of numerical examples, the improved algorithm proposed in this article can obtain a scheduling scheme that is more suitable for practical situations, with better computational speed, to support efficient order processing and resource allocation work under complex tasks

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Framework for the Deployment of Intelligent Smart Cities (ISC) using Artificial intelligence and Software Networking Technologies

Framework for the Deployment of Intelligent Smart Cities (IS...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Nachaat Mohamed Ramakant Upadhyay Geethamanikanta Jakka P V Rambabu Badria Sulaiman Alfurhood Devesh Pratap Singh Rabdan Academy UAE Homeland Security Universiti Sains Malaysia Abu Dhabi UAE Department of CSE Sanskriti University Mathura Uttar Pradesh India Data Analytics Information Technology University of the Cumberlands USA Civil Engineering Department of Computer Sciences College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Saudi Arabia

This article’s goals include providing a framework for interpersonal and communication to safeguard welfare services and assess how well the innovation ecosystem is now understood. An essay starts with an overview or description of "urban area" This viewpoint includes neighborhood services, modern amenities, or smart infrastructure. Information and technology, digital operations use, monitoring, and interconnection, amongst other things. The section that follows examines various urban planning concerns, either administration or expansion. Utilizing diverse technologies effectively is made possible by innovative information about towns or an answer to a question concerning high-quality public infrastructure. An innovative method of managing networks is the Software Defined Network (SDN) architecture. Unlike switches in a conventional network computing environment, changes in SDN do not process inbound sachets in the similar means. If no inbound packets are identified after searching the linking tables, they are passed to the control system, the SDN’s operating system, for processing. A Spread Denial of Service (DoS) outbreak poses the biggest risk to the cyber security of an SDN *** system or application layers of the disease-ridden devices connected to the system will be the target of the attack. The approach for determining whether incoming packets are contaminated or not is clever machine learning-based, and it is proposed in this paper. In the context of smart cities, the deployment of artificial intelligence (AI) has newly drawn more and more attention. The purpose of this study is to perform a works assessment to examine the role of AI in the various parts of smart towns given the relevance of the topic. This study’s technique combines a thorough literature evaluation to find the most pertinent papers with reference system investigation to reveal the subtleties of the subject. The findings of this study highlight the significance of this technology, whi

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Evaluating and Improving RoSELS for Road Surface Extraction from 3D Automotive LiDAR Point Cloud Sequences 1

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3rd international conference on Deep Learning Theory and Applications, DeLTA 2022

作者： Katkoria, Dhvani Sreevalsan-Nair, Jaya Graphics-Visualization-Computing Lab IIIT Bangalore 26/C Electronics City Karnataka 560100 India

ISBN: (数字)9783031373176

ISBN: (纸本)9783031373169

Navigable space determination is a difficult problem encountered in robotics and intelligent vehicle technology and it requires integrated solutions using advances in computer vision and computational geometry. The conventional data processing workflow uses semantic segmentation to identify road points from three-dimensional (3D) automotive LiDAR point clouds, which have to be extended to determine its boundary points. The boundary points are critical in getting an initial coarse extraction of its surface. Hence, to filter the curb or edge points from the road points, the semantic segmentation results are post-processed. The road surface is determined by the entire trajectory information, which is captured as a sequence of LiDAR point clouds. For the entire road surface extraction, we use an automated system, RoSELS (Road Surface Extraction for LiDAR point cloud Sequence) that works at different scales, i.e., at the scale of a point, a point cloud, and the sequence of point clouds. In this paper, we evaluate the algorithms used in RoSELS, namely, the Gaussian Mixture Model for curb detection and ResNet-50 for transfer learning in frame classification. We evaluate the quality of the mesh extracted using RoSELS, which is intended for straight-road geometry. Here, we also show how the initial extracted surface can be further post-processed to extend RoSELS for curved roads. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Vehicles

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A Comprehensive Analysis of the Use of Machine Learning Techniques to Optimize the Information Retrieval Process

A Comprehensive Analysis of the Use of Machine Learning Tech...

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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： S. Hemelatha Kalanandhini. G Prasad B. Chaudhari Rajeev Sharma Jambi Ratna Raja Kumar Dipannita Mondal Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department of Management Mangalayatan University Aligarh India Department of Computer Engineering Genba Sopanrao Moze College of Engineering Balewadi Pune Maharashtra India Artificial Intelligence And Data Science Department Dr. D.Y.Patil College Of Engineering And Innovation Talegaon

This explores using a system to gain knowledge of strategies that allow you to optimize the facts retrieval manner. Facts retrieval is a complicated technique; it is becoming even more complicated as advancements in generation have furnished tools to make the gathering, employing, and evaluating information less complicated and faster. Research techniques, including artificial intelligence, neural networks, and machine learning, can speed up records retrieval and improve outcomes' accuracy. This look examines the different machine learning techniques to optimize the facts retrieval process and the advantages and downsides of each technique. Furthermore, this study looks at how gadgetstudying algorithms improve a person's enjoyment of retrieval systems and how their use can be integrated into cutting-edge practices. Subsequently, methods for improving the performance of machine learning algorithms are discussed in addition to capacity destiny directions inside the use of gadget studying to optimize statistics retrieval.

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