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arXiv 2024年

作者： Zhang, Jing Gao, Sheng Feng, Xin Yang, Hongwei Sun, Geng The College of Computer Science and Technology Changchun University of Science and Technology Changchun130022 China The College of Communication Engineering Jilin University Changchun130012 China The College of Computer Science and Technology Jilin University Changchun130012 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

Unmanned aerial vehicles (UAVs) with flexible deployment contribute to enlarging the distance of information transmission to mobile users (MUs) in constrained environment. However, due to the high mobility of both UAVs and MUs, it is challenging to establish an accurate beam towards the target MU with high beam gain in real-time. In this study, UAV base stations (UAV-BSs) consisting of position-known assisted UAVs (A-UAVs) and position-unknown assisted UAVs (U-UAVs) are employed to transmit data to MUs. Specifically, a bi-directional angle-aware beam tracking with adaptive beam reconstruction (BAB-AR) algorithm is proposed to construct an optimal beam that can quickly adapt the movement of the target MU. First, the angle-aware beam tracking is realized within the UAV-BSs using a proposed global dynamic crow search algorithm without historical trajectory. Furthermore, the Gaussian process regression model is trained by A-UAVs to predict the azimuth and elevation angles of MUs. Meanwhile, we focus on the beam width and design a time interval adjustment mechanism for adaptive beam reconstruction to track high-speed MUs. Finally, the performance of the BAB-AR algorithm is compared with that of benchmark algorithms, and simulate results verifies that the BAB-AR algorithm can construct an accurate beam capable of covering high-speed MUs with the half power beam width in a timely manner. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

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An Object Driven Fuzzy Decision Model for Sugarcane Yield Prediction in Indonesia’s Sugar Industry

An Object Driven Fuzzy Decision Model for Sugarcane Yield Pr...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Ditdit Nugeraha Utama Muhammad Asrol Suharjito Christian Harito Napthaleni Muhammad Syafrudin Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia Industrial Engineering Department BINUS Graduate Program Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia Department of Artificial Intelligence and Data Science Sejong University Seoul South Korea

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

A sugarcane yield of one plantation area depends on several independent variables. Practically it is challenging to predict accurately by using conventional methods. This study aims to develop a decision model based on a combination of fuzzy logic and object-oriented methods to predict sugarcane yield. The research is conducted in four main stages, employing object-oriented methods for model design and fuzzy logic for model construction. Object and activity diagrams are used for the object-oriented model design. The fuzzy membership functions employed are a combination of trapezoidal and triangular shapes. The resulting decision model can simulate 2,225 data from plantation areas in Indonesia. Based on the 10 examples of plantation area data in Indonesia, plantation number one obtained the largest sugarcane yield, which was 4.79%, with a similarity value of 0.90 (when compared to manual calculations as its ground truth). This similarity value is a higher value when compared to the average similarity value, which is 0.89.

关键词： Fuzzy logic Seminars Shape Object oriented modeling Plantations Manuals Predictive models Sugar industry Intelligent systems Information technology

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AGU2-Net: Multi-Scale U2-Net Enhanced by Attention Gate Mechanism for Image Tampering Localization

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IEEE Access 2025年 13卷 99659-99671页

作者： Kefei Wu Lin Li Qingyan Li School of Science Jimei University Xiamen China Computer Engineering College Jimei University Xiamen China Digital Fujian Big Data Modeling and Intelligent Computing Institute Jimei University Xiamen China

The advancement of image editing and compositing technologies has posed significant challenges to the authenticity of digital images. Although deep learning algorithms based on convolutional neural networks (CNNs) have made notable progress in image forgery detection, they still face certain limitations in effectively detecting and localizing tampered areas due to the subtle nature of existing manipulation traces. To address this limitation, we propose AGU2-Net, a novel network architecture that integrates an advanced attention mechanism with multi-scale feature extraction. The architecture employs a nested U-structure as its backbone, enabling effective integration of image features across various scales. Additionally, AGU2-Net is an end-to-end autonomous training network with a lightweight framework that incurs low computational overhead. A significant innovation of the architecture is the introduction of the attention gate mechanism, which enhances the network’s ability to selectively process multi-scale features. By adaptively fusing feature information from the encoder and decoder, this mechanism increases spatial perception and improves the network’s ability to capture intricate local tampering details. Experiments on four publicly available benchmarks show that AGU2-Net achieves top F1 scores of 0.556, 0.338, and 0.273 on the CASIA, NIST16, and IMD2020 datasets, respectively, confirming its superior tamper-detection accuracy and sharper pixel-level localization compared with state-of-the-art methods.

关键词： Feature extraction computer architecture Location awareness Computational modeling Forgery Attention mechanisms Logic gates Splicing Convolution Accuracy

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Learning a Typhoon Bayesian Network Structure from Natural Language Reports 5th

Learning a Typhoon Bayesian Network Structure from Natural...

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5th IFIP TC 12 International Conference on Intelligence science, ICIS 2022

作者： Yao, Zhangrui Chen, Junhan Pan, Yinghui Zeng, Yifeng Ma, Biyang Ming, Zhong Department of Automation and Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision-Making Xiamen University Xiamen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer and Information Sciences Northumbria University Newcastle upon Tyne United Kingdom School of Computer Science and Engineering Minnan Normal University Zhangzhou China

ISBN: (纸本)9783031149023

Given the huge toll caused by natural disasters, it is critically important to develop an effective disaster management and emergency response technique. In this article, we investigate relationships between typhoon-related variables and emergency response from natural language (NL) reports. A major challenge is to exploit typhoon state information for typhoon contingency plan generation, especially from unstructured text data based on NL input. To tackle this issue, we propose a novel framework for learning typhoon Bayesian network structures (FLTB), which can extract typhoon state information from unstructured NL, mine inter-information causal relationships and then generate Bayesian networks. We first extract information about typhoon states through NL processing (NLP) techniques, and then analyze typhoon reports by designing heuristic rules to identify causal relationships between states. We leverage these features to improve the learned structures and provide user-interaction mechanisms to finalize Bayesian networks. We evaluate the performance of our framework on real-world typhoon datasets and develop the Bayesian networks based typhoon emergency response systems. © 2022, IFIP International Federation for Information Processing.

关键词： Bayesian networks

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Machine Learning-Based Analogue Circuit Design for Stage Categorization and Evolutionary Optimization

Machine Learning-Based Analogue Circuit Design for Stage Cat...

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International Conference on Computational and Characterization Techniques in engineering & sciences (CCTES)

作者： V. Srikanth P. Aswini Rakesh Chandrashekar N Sirisha Manish Kumar K. Adnan School of Sciences and Computer Studies CMR University (SATELLITE CAMPUS-OMBR LAYOUT) Bengaluru India Department of Computer Science and Engineering( Data Science) Institute of Aeronautical Engineering Hyderabad India New Horizon College of Engineering Bangalore India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Lovely Professional University Phagwara India Hilla University College Babylon Iraq

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

The goal of this study is to provide a two-stage bottom-up approach that uses machine learning to simplify the development of analogue devices. Analogue CMOS (complementary metal-oxide-semiconductor) circuit designers use their expertise to address complex problems, involving multiple factors and goals. The research delves into the possibilities of libraries housing open-source machine-learning models to aid designers and provides a framework outlining the specifics of ML-generated analogue circuits. Creating neural network designs has traditionally relied on commercialized CMOS or software simulations; however, these approaches don't guarantee optimal performance. The proposed approach is validated using a three-stage device design. In the first stage, the type is accurately predicted with a precision of 89.75% utilising a machine learning approach called the decision tree. Both of these rule induction strategies are used to generate prediction logic. The second stage involves forecasting the usual parameters for each stage type using four learning techniques: decision trees, gradient-boosted trees, random forests, and support vector machines. When compared to other approaches, the support vector machine outperforms them all while exhibiting the lowest error rates.

关键词： Support vector machines Semiconductor device modeling Performance evaluation Neural networks Software Topology Decision trees Random forests Machine intelligence Optimization

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A Game Between the Defender and the Attacker for Trigger-Based Black-box Model Watermarking

A Game Between the Defender and the Attacker for Trigger-Bas...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Chaoyue Huang Hanzhou Wu School of Communication and Information Engineering Shanghai University Shanghai China School of Big Data and Computer Science Guizhou Normal University Guiyang Guizhou China

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

Watermarking deep neural network (DNN) models has attracted a great deal of attention and interest in recent years because of the increasing demand to protect the intellectual property of DNN models. Many practical algorithms have been proposed by covertly embedding a secret watermark into a given DNN model through either parametric/structural modulation or backdooring against intellectual property infringement from the attacker while preserving the model performance on the original task. Despite the performance of these approaches, the lack of basic research restricts the algorithmic design to either a trial-based method or a data-driven technique. This has motivated the authors in this paper to introduce a game between the model attacker and the model defender for trigger-based black-box model watermarking. For each of the two players, we construct the payoff function and determine the optimal response, which enriches the theoretical foundation of model watermarking and may inspire us to develop novel schemes in the future.

关键词： Biological system modeling Digital forensics Closed box Modulation Watermarking Games Artificial neural networks Intellectual property Robustness Game theory

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Dynamic-Matrix-Encryption Based Secure Strong PUF for Device Authentication Protocols

Dynamic-Matrix-Encryption Based Secure Strong PUF for Device...

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International Conference on Solid-State and Integrated Circuit Technology

作者： Liangxiao Zhao Gang Li Pengjun Wang Xuejiao Ma Ziyu Zhou The College of Electrical and Electronic Engineering Wenzhou University Wenzhou China School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China

ISBN: (数字)9798350361834

ISBN: (纸本)9798350361841

Strong physical unsolvable function (PUF) has a wide range of applications in internet of things (IoT) security. However, it is vulnerable to machine learning (ML) modeling attacks. This paper proposes a strong PUF anti-ML attack method based on dynamic-matrix encryption. It obfuscates the challenge by multiplying the dynamic encryption matrix (DEM) with the challenge matrix synchronizing initial values during the registration phase of the device security authentication protocol prevents our obfuscation from being invalidated when the algorithms and structures motivating the obfuscation are made public. Each round of DEM is updated with respect to each previous round of challenge which is similar to the properties of sequential logic circuits in that the current response does not depend only on the current challenge but is related to previous challenge. Experimental results show that even if an attacker collects 1 million challenge response pairs, the prediction accuracies of the several ML attacks we use are below 53%, with negligible effects on randomness, reliability, and uniqueness.

关键词： Support vector machines Protocols Accuracy Heuristic algorithms Authentication Encryption Sequential circuits Internet of Things Synchronization Integrated circuit modeling

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Feature Fusion Based Deep Transfer Learning Based Human Gait Classification Model

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Intelligent Automation & Soft Computing 2023年第8期37卷 1453-1468页

作者： C.S.S.Anupama Rafina Zakieva Afanasiy Sergin E.Laxmi Lydia Seifedine Kadry Chomyong Kim Yunyoung Nam Department of Electronics and Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Candidate of Pedagogical Sciences Department of Industrial Electronics and Lighting EngineeringKazan State Power Engineering UniversityKazan420066Russia Department of Theories and Principles of Physical Education and Life Safety North-Eastern Federal University Named After M.K.AmmosovYakutsk677000Russia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea

Gait is a biological typical that defines the method by that people *** is the most significant performance which keeps our day-to-day life and physical *** electromyography(sEMG)is a weak bioelectric signal that portrays the functional state between the human muscles and nervous system to any *** classifiers dependent upon sEMG signals are extremely utilized in analysing muscle diseases and as a guide path for recovery *** approaches are established in the works for gait recognition utilizing conventional and deep learning(DL)*** study designs an Enhanced Artificial Algae Algorithm with Hybrid Deep Learning based Human Gait Classification(EAAA-HDLGR)technique on sEMG *** EAAA-HDLGR technique extracts the time domain(TD)and frequency domain(FD)features from the sEMG signals and is *** addition,the EAAA-HDLGR technique exploits the hybrid deep learning(HDL)model for gait *** last,an EAAA-based hyperparameter optimizer is applied for the HDL model,which is mainly derived from the quasi-oppositional based learning(QOBL)concept,showing the novelty of the work.A brief classifier outcome of the EAAA-HDLGR technique is examined under diverse aspects,and the results indicate improving the EAAA-HDLGR *** results imply that the EAAA-HDLGR technique accomplishes improved results with the inclusion of EAAA on gait recognition.

关键词： Feature fusion human gait recognition deep learning electromyography signals artificial algae algorithm

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Detection of Abnormalities in Chest X-Rays Using Convolutional Neural Networks

Detection of Abnormalities in Chest X-Rays Using Convolution...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Babu, Harshith Mishra, Rahul Karthigaikumar, P. Mahalle, Parikshit N. Parid, Prasanta Kumar Pund, Sachin Hindusthan College of Engineering & Tech Computer Science and Engineering Coimbatore India Centre for Interdisciplinary Research in Business and Technology Institute of Engineering and Technology Chitkara University Punjab India Karpagam College of Engineering Department of Electronics and Communication Engineering Coimbatore641032 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Kiit University Department of Rural Managementn Bhubaneswar India Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Maharashtra Nagpur India

ISBN: (纸本)9798350329773

The purpose of this look is to increase an automated gadget for detecting chest X-ray imaging abnormalities through a Convolutional Neural community (CNN). The usage of a CNN permits real-time detection of abnormalities that could otherwise be missed via radiologists, as well as increases the efficiency of clinical experts by using being able to hit upon ability abnormalities without the need for extra tests. In this observation, 285 chest scans had been accrued from a local health facility and analyzed using a custom-built CNN. Through the use of various convolutional layers, various functions have been extracted and applied to stumble on abnormalities. Adjustments to the model have been made by including additional convolutional layers and adjusting the filters. The ensuing model carried out a ninety-seven - 2 % accuracy fee in identifying abnormalities in chest X-rays. The outcomes of this look display the hit implementation of a CNN in detecting abnormalities in chest X-rays. In addition, further studies are had to refine the model and boom accuracy. Moreover, similar research is recommended to examine its use in scientific decision-making for sufferers. The results of this have a look at can be seen as a step forward in the improvement of automated clinical analysis structures and their usage in clinical decision-making. © 2023 IEEE.

关键词： Convolution

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A Novel Deep Learning Method for the Identification and Categorization of Footpath Defects based on Thermography 3

A Novel Deep Learning Method for the Identification and Cate...

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Vanitha, L. Kavitha, R. Panneerselvam, M. Prathima, C.H. Valantina, G.Mary Bharath Institute of Higher Education and Research Department of Electronics and Communication Engineering Chennai India Velammal Engineering College Department of Ai & Data Science Engineering Chennai India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Tirupati India Saveetha School of Engineering Simats Department of Computer Science and Engineering Chennai India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Automated detection represents one of the most important criteria for assessing the condition of the roadway, and it represents a challenging problem for the creation of advanced practices and methods. A large data set that includes complicated surface conditions is necessary for autonomous damage identification utilizing deep convolutional neural network. This study offers a thermal-RGB fusion picture-based sidewalk defect identification method, in which the merged RGB-thermal image is created by different sensor data to accomplish quick and precise defect identification considering complicated pavement circumstances. The suggested technique achieves high sidewalk damage recognition accuracy by using EfficientNet as the foundation structure and creating an argument database. The efficiency of various input information (RGB, thermal, MSX, and fused picture) is tested individually in this article to examine the effects of input information and system on the identification outcomes. The findings demonstrated that the merged image's degradation detection performance could reach 98.33 percent, and by applying the information after supplementation, the classification method is deemed to be significantly stable to attain 98.33 percent. © 2022 IEEE.

关键词： Pavements

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