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Multi-source Analysis of AI Vulnerabilities: Methodology and Algorithms of data Collection

Multi-source Analysis of AI Vulnerabilities: Methodology and...

引用

ieee international Workshop on intelligent data acquisition and advanced computing systems: Technology and Applications

作者： Oleksii Neretin Vyacheslav Kharchenko Herman Fesenko National aerospace university “KhAI” Kharkiv Ukraine

The initial stage of assessing the cybersecurity of artificial intelligence (AI) systems and tools for cyber-physical systems is the collecting, processing and integrating of vulnerability data from relevant and reliable information sources using Big data tools. The article provides a classification of sources of information about vulnerabilities of AI systems. The possibilities of automatic scanning of these sources using software scanners are analyzed. The analysis methodology and data structures are proposed, and the necessary set of data for further storing and analyzing is selected. Algorithms for direct collection of vulnerability data from various sources are developed. The directions of further research on the development of algorithms and software tools for aggregation, filtering, processing and integration of multisource data on vulnerabilities of AI systems are substantiated.

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Interactive Genetic Algorithms in intelligent Art and Graphic Design System 3

Interactive Genetic Algorithms in Intelligent Art and Graphi...

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3rd international conference on data Analytics, computing and Artificial Intelligence, ICDACAI 2024

作者： Zhao, Qingfen Department of Art Education Jinan Early Childhood Normal College Shandong Jinan250002 China

ISBN: (纸本)9798331519032

Computer art design is a new way of artistic creation that relies on computer science and technology and closely combines computers with art graphic design. On this basis, this article utilizes a new intelligent art graphic design system with high design efficiency. The system management function module in the intelligent art graphic design system includes creating new design works and managing system configurations. The size and materials of the work in the modeling function module can also be adjusted appropriately according to specific environments. By comparing interactive genetic algorithms (GA) with traditional GA, the advantages of interactive GA in improving design efficiency were studied. In the comparison of completion time between interactive GA and traditional method design, the first image has a completion time of 3.1 hours for interactive GA design and 4.9 hours for traditional method design. The interactive GA in this article can effectively improve the efficiency of art graphic design in intelligent art graphic design systems. © 2024 ieee.

关键词： Efficiency

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intelligent Information Technology for Identification of Remaining Defects in Software Products

Intelligent Information Technology for Identification of Rem...

引用

ieee international Workshop on intelligent data acquisition and advanced computing systems: Technology and Applications

作者： Iryna Zasornova Tetiana Hovorushchenko Mykola Fedula Viktoriia Buzyl Khmelnytskyi National University Khmelnytskyi Ukraine Elite Business Centre Ruby Play Company Malta

An actual task nowadays is to identify remaining defects in software products after their testing. The purpose of this study is to develop an intelligent information technology for identification of remaining defects in software products. This paper's scientific novelty is the developed structure of formation and a detailed scheme of information technology for identification of remaining defects in software products. The intelligent information technology for identification of remaining defects in software products provides the conclusion about the remaining defects' absence or presence (with different degrees of criticality of their consequences on the basis of a natural language report on defects in a software product identified during its main testing).

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Deep Neural Quality of Service Prediction for Unmanned Aircraft System Communications 20

Deep Neural Quality of Service Prediction for Unmanned Aircr...

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20th ieee international Wireless Communications and Mobile computing (ieee IWCMC) - Green and intelligent Communications

作者： Purucker, Patrick Reil, Christian Hoess, Alfred Schuller, Bjoern W. Univ Appl Sci Amberg Weiden Dept Elect Engn Media Technol & Comp Sci D-92224 Amberg Germany Tech Univ Munich MRI CHI Munich Germany Munich Data Sci Inst MDSI Munich Germany Munich Ctr Machine Learning MCML Munich Germany Imperial Coll London GLAM London England

ISBN: (纸本)9798350361261;9798350361278

Commercial Unmanned Aircraft systems (UAS) have a wide range of applications, including package delivery, inspection and search and rescue missions. For the operation of Unmanned Aircraft Vehicles (UAV) Beyond Visual Line of Sight (BVLOS), reliable long-range communication is essential. The cellular network is one possible solution, but there are issues such as signal loss and frequent handovers at higher altitudes. To mitigate these issues, our work proposes the use of two cellular links from different providers prioritised according to Quality of Service (QoS) prediction. We evaluate multiple AI-based model architectures for the prediction, and find that the model consisting of Gated Recurrent Units (GRU) and convolutional layers outperforms the others. The models are trained and tested on real-world data and show a reduction in latency peaks, thereby increasing connection resilience. Moreover, the prediction pipeline is designed to be executable on the UAV side and is not limited to a specific geographical area, making it applicable to real-world scenarios. Finally, we present a pre-flight path planning algorithm that takes QoS into account when calculating the flight path in order to further improve communication. To support the research community, we publicly share the dataset used to obtain our results.

关键词： Deep Learning UAS Communication Quality of Service LTE 5G

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Edge computing and Distributed Ledger Technology for the Industrial IoT 11

Edge Computing and Distributed Ledger Technology for the Ind...

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11th ieee international conference on intelligent computing and Information systems, ICICIS 2023

作者： Abo-Soliman, Mohamed Ahmed Shabaan, Eman Al-Qutt, Mirvat Emara, Kareem Ain Shams University Computer Systems Department Cairo Egypt

ISBN: (纸本)9798350322101

The tremendous increase in implementing IoT devices for critical infrastructures yells for higher performance and robust protection. Traditional cloud based IoT architecture is unsuitable for delay-sensitive or real-time applications and lacks the robust security needed by industrial systems. Thus, new technologies are introduced to endorse the industry's digital transformation. Edge computing greatly satisfies required performance while Distributed Ledger Technology (DLT) mitigates potential data compromises. This work proposes an integrated four layers architecture based on Edge computing and DLT that provides efficient computing, security, and scalability. Edge computing handles transmission delay and scalability issues, while DLT handles data validation and trust. A lightweight DLT model is introduced to enable faster data commitment and better networking. The model incorporates two main features: a new grouping technique that enables network segmentation reducing network complexity. The second technique dynamically optimizes resource-utilization by adjusting DLT requirements according to the hardware capabilities of each participating node. © 2023 ieee.

关键词： Internet of things

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Research on intelligent recognition method of tunnel lining cracks based on small objective recognition algorithm 9

Research on intelligent recognition method of tunnel lining ...

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9th international conference on Image, Vision and computing, ICIVC 2024

作者： Meng, Ying Wu, Hongtao Niu, Bingqing Shanxi Intelligent Transportation Institute Co. Ltd. Shanxi Transportation Technology Research & Development Co. Ltd Taiyuan China Shanxi Intelligent Transportation Institute Co. Ltd. Taiyuan China

ISBN: (纸本)9798350385991

With the rapid development of today's highways, the number of tunnels is also increasing. Cracks, as one of the most common and serious diseases detected in tunnels, are an important parameter indicator to evaluate the apparent structural quality of tunnel linings. When the crack length or width exceeds the allowable range, it can affect the bearing capacity of the reinforced concrete structure and even lead to instability and sudden collapse of the lining, posing a great threat to human life and property. For this reason, image acquisition of tunnels is needed regularly to analyze the disease. At present, the rapid detection means of highway tunnels is still in the exploration stage, and the technical bottlenecks common to only a few products at home and abroad limit the promotion and application of the system. For the existing tunnel lining acquisition images exist in poor picture quality, disease is not prominent and manual labeling disease inefficiency and other problems, innovation based on image processing, deep learning and other methods to develop a small target recognition algorithm for tunnel lining cracks, and build a tunnel crack intelligent identification system to achieve intelligent identification detection of crack disease on highway tunnel scenes. The use of tunnel crack intelligent identification system can significantly reduce the detection time of crack disease and human detection cost investment, at the same time, can also effectively avoid the manual detection of fatigue situation prone to misidentification, omission identification and other problems, play an irreplaceable role in the protection of highway tunnel safe operation, timely detection of highway initial disease, reducion of maintenance costs and other aspects, has significant economic and social benefits. © 2024 ieee.

关键词： Tunnel linings

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Research on Artificial Intelligence-Based Computer Vision and Speech Recognition in intelligent Automation systems 2

Research on Artificial Intelligence-Based Computer Vision an...

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2nd ieee international conference on Image Processing and Computer Applications, ICIPCA 2024

作者： Zhang, Jianqin Nanjing Engineering Branch Jiangsu Union Technical Institute Nanjing China

ISBN: (纸本)9798350360240

This paper focuses on the use of SIFT algorithm for visual feature extraction, and innovatively deeply integrates visual information with voice commands to enable accurate control of intelligent automation systems. Firstly, the robustness and efficiency of SIFT algorithm in complex environment is deeply discussed, and it is applied to target object recognition, scene understanding and other visual tasks to effectively extract key visual features. Further, the audio-visual fusion model is researched and constructed, and the visual features extracted by SIFT algorithm are cleverly integrated with the voice commands processed by advanced speech recognition technology. The synchronous analysis and collaborative decision-making of dual-mode information are realized through artificial intelligence means such as deep learning. The purpose of this model is to improve the comprehensive understanding and response ability of the automated system to multiple and heterogeneous input data, so as to ensure that it can accurately identify user intentions and perform accurate operations in complex environments. Experimental results show that this strategy can significantly improve the environment adaptability, interaction naturalness and task completion accuracy of the intelligent automation system. © 2024 ieee.

关键词： intelligent systems

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Air Pollution Prediction systems in City Highways using Artificial Intelligence

Air Pollution Prediction Systems in City Highways using Arti...

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2024 international conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Gupta, Rohan Manna, Ambarish Sharma, Himanshu Pandit, Priyansh Pan, Sandip Singh, Yuvraj Chandigarh University UIE Dept. of ECE Punjab Mohali India Chandigarh University UIE Dept. of CSE Punjab Mohali India

ISBN: (纸本)9798350378092

The danger to the human's health in metropolitan environments usually city highways happen due to air pollution. By the use of Artificial intelligence (AI) system has been designed in this paper so as to reduce air pollution on city roads. Our examination merges data analysis and modelling for reduction of air pollution. data acquisition, data pre-processing, data analysis and model development are various steps which are included in the methodology. Training of different machine learning models is done in model development. The capabilities of AI for air quality index (AQI) in a particular region is showcased through user-interactive model. Perceptive data on air pollution patterns on city roads along with framework which rely on AI for handling and forecasting air quality is illustrated in result section. Environmental health and traffic management in city areas are few of the implications. © 2024 ieee.

关键词： data acquisition

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Real-Time Weapon Detection System for Enhanced Security Monitoring 4

Real-Time Weapon Detection System for Enhanced Security Moni...

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4th international conference on Ubiquitous computing and intelligent Information systems, ICUIS 2024

作者： Kuppili, Rohan Agarwal, Khusboo Bhosale, Tanmay Gouthaman, P. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India School of Computing Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Kattankulathur603203 India

ISBN: (纸本)9798331529635

Innovative technology for weapon detection during security monitoring is necessary today due to the rising threats of violence and terror. The paper discusses a detailed design of a weapon detection system intended for application in enhancing security surveillance specifically using a deep learning approach. The system employs YOLOv3 (You Only Look Once, Version 3) mood and adaption aimed at live footage weapon detection. To do this on broader scale, this system incorporates big data tools Hadoop, Docker, Spark and Kafka for reasons of real time processing and effectiveness in high volume data situations. This model is applied on the live footage surveillance of public and private places to determine if the model can detect weapons, to warn relevant authorities in the aim of decreasing response time and averting security breach occurrences. The experiments conducted confirm that the model is capable of performing weapon detection tasks in an indoor environment with changes in illumination conditions with few false alarms. © 2024 ieee.

关键词： Big data

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LDCP: A Novel Approach to Predict Fake Reviews in Online Social Network by using Learning based data Classification Principle

LDCP: A Novel Approach to Predict Fake Reviews in Online Soc...

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

作者： Mercy, S.Sudha Venkatesan, S. Bharaneedharan, M. Nayagan, S. Venkateswarlu, L. Padmaja, Nimmagadda R.M.K. Engineering College Department of Computer Science and Design Chennai India Qis College of Engineering and Technology Department of Electrical and Electronics Engineering AndhraPradesh Ongole India S.A. Engineering College Department of Ece Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Cse Chennai India Qis College of Engineering and -Technology Department of Electrical and Electronics Engineering Andhra Pradesh Ongole India Department of Ece Tirupati India

ISBN: (纸本)9798331543617

The proliferation of online reviews has significantly impacted consumer decision-making processes, making the detection of fake reviews crucial for maintaining trust in e-commerce platforms. This study presents a novel approach for detecting fake reviews using machine learning (ML) and deep learning (DL) models, with a focus on achieving high accuracy through advanced feature extraction techniques and data augmentation. Various models, including classical ML algorithms such as Support Vector Machine (SVM) and Random Forest, alongside DL models like Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Cascaded Multi-Layer Perceptron (MLP), were evaluated. Feature extraction methods like TF-IDF, GloVe, and combined approaches were used to enhance performance. The DL model RoBERTa achieved the highest accuracy of 99.5%, while Cascaded MLP reached 99.2%. Classical models like Random Forest and SVM also performed well, achieving accuracies of 97.1 % and 96.8%, respectively. data augmentation techniques, such as back-translation, further improved the models' ability to handle imbalanced datasets. The results demonstrate that combining DL architectures with advanced feature extraction leads to superior performance in fake review detection. © 2024 ieee.

关键词： Adversarial machine learning

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