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Chaos in chains: Exploring a novel supply chain model through bifurcation analysis, multi-stability and complete synchronization via backstepping control

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Partial Differential Equations in Applied Mathematics 2024年 11卷

作者： Johansyah, Muhamad Deni Vaidyanathan, Sundarapandian Hannachi, Fareh Sambas, Aceng Foster, Bob Aruna, Chittineni Ramesh, Repudi Rusyaman, Endang Faculty of Mathematics and Natural Sciences Universitas Padjadjaran School of Electrical and Communication Engineering Vel Tech University Department of Management Sciences University of Tebessa Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Department of Mechanical Engineering Universitas Muhammadiyah Tasikmalaya Faculty of Business and Economics Universitas Informatika dan Bisnis Indonesia Department of Computer Science and Engineering KKR & KSR Institute of Technology and Sciences

Chaotic systems are highly sensitive to initial conditions, meaning small changes can lead to significant outcomes. This sensitivity can be leveraged to enhance the flexibility and adaptability of supply chains, allowing them to respond quickly to changing market demands and conditions. In this research work, we obtain a New Chaotic Supply Chain Model (NCSCM) by introducing a sinusoidal model uncertainty in the third differential equation of the Hamidzadeh chaotic supply chain model (2023). The sinusoidal uncertainty in the model accounts for the volatility and fluctuations of the supply chain model. We investigate the dynamic behaviors of NCSCM by performing a detailed Bifurcation Diagram (BD) analysis and Lyapunov Exponents (LEs). Next, we carry out a 0−1 test for the NCSCM to confirm the chaotic attractor of the system. We also study the multi-stability properties of the NCSCM. Finally, with the help of backstepping control, we derive new results for the complete synchronization of the novel chaotic supply chain models. © 2024

关键词： Chaotic systems Dynamical systems Finance models Stability

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Development of a chickpea disease detection and classification model using deep learning

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informatics in Medicine Unlocked 2022年 31卷

作者： Belay, Abebech Jenber Salau, Ayodeji Olalekan Ashagrie, Minale Haile, Melaku Bitew Department of Information Technology College of Informatics University of Gondar Ethiopia Department of Electrical/Electronics and Computer Engineering Afe Babalola University Nigeria Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences India

Ethiopia is the largest producer of chickpeas in Africa. Crop production and yield in Ethiopia is greatly affected by plant diseases which cause loss of agricultural products every year. One of these plant diseases is chickpea disease which is a fungal disease. Ascochyta blight and Fusarium wilt are the most common chickpea diseases in Ethiopia that affect crop production quality and quantity. The identification of these diseases requires experienced experts or systems. Although numerous methods have been previously adopted in literature, deep learning (DL) is suggested as an efficient approach for disease recognition and classification since it can automatically learn features from the input image. In this paper, a chickpea disease detection model was developed using deep learning techniques by combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for feature extraction and Softmax for classification. To develop the proposed model, various image preprocessing stages such as image resizing, normalization, and noise filtering using a combination of Gaussian filter (GF) and Median filter (MF) were performed. To prevent the problem of overfitting, augmentation was applied, while to train and test the effectiveness of the developed model, 8391 images were used. From the acquired images, 80% of the dataset was used for training, 20% of the dataset was used for testing and out of the 80% training data, 20% was used for validation. The proposed CNN-LSTM performed well in identifying chickpea disease, with an accuracy of 92.55%. According to the study's findings, the proposed CNN-LSTM outperforms existing methods. © 2022 The Authors

关键词： Ascochyta blight Chickpea CNN-LSTM Deep learning Fusarium wilt

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Real-Time Robust Video Object Detection System Against Physical-World Adversarial Attacks

arXiv

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arXiv 2022年

作者： Han, Husheng Hu, Xing Xu, Kaidi Dang, Pucheng Wang, Ying Zhao, Yongwei Du, Zidong Guo, Qi Yang, Yanzhi Chen, Tianshi SKL of Processors Institute of Computing Technology CAS Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Cambricon Technologies Beijing China Department of Computer Science College of Computing & Informatics Drexel University Philadelphia19104 United States SKL of Computer Architecture Institute of Computing Technology CAS Beijing100190 China Department of Electrical and Computer Engineering Northeastern University Boston02115 United States

DNN-based video object detection (VOD) powers autonomous driving and video surveillance industries with rising importance and promising opportunities. However, adversarial patch attack yields huge concern in live vision tasks because of its practicality, feasibility, and powerful attack effectiveness. This work proposes Themis, a software/hardware system to defend against adversarial patches for real-time robust video object detection. We observe that adversarial patches exhibit extremely localized superficial feature importance in a small region with non-robust predictions, and thus propose the adversarial region detection algorithm for adversarial effect elimination. Themis also proposes a systematic design to efficiently support the algorithm by eliminating redundant computations and memory traffics. Experimental results show that the proposed methodology can effectively recover the system from the adversarial attack with negligible hardware overhead. © 2022, CC BY.

关键词： Object detection

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Effect of lead time prediction accuracy in trust-based resource sharing

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IFAC-PapersOnLine 2021年第1期54卷 1126-1131页

作者： Ádám Szaller Botond Kádár Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control Budapest Hungary Department of Manufacturing Science and Engineering Budapest University of Technology and Economics Budapest Hungary

Nowadays, challenges are changing production facilities: decentralized production networks are replacing centralized organizations to remain competitive. This paper investigates a resource sharing approach where matching resource offers and requests are made by an intermediate platform. One of the main pillars of collaboration is to keep promises, especially about deadlines: in the presented model, facilities could rate each other based on trustfulness and choose from offers based on this setting. Lead time prediction accuracy has a direct effect on the real processing intervals: if the prediction was accurate, the deadline could be met, which results in good ratings and a higher possibility to win more jobs. In the paper, effect of lead time prediction accuracy is investigated in trust-based resource sharing, and the performance of facilities is compared with agent-based simulation.

关键词： Co-operative control/manufacturing Multi-agent simulation Manufacturing System engineering

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Intelligent Verbal Interaction Methods with Non-Player Characters in Metaverse Applications

Intelligent Verbal Interaction Methods with Non-Player Chara...

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International Conference on Advanced Information and Communication Technologies (AICT)

作者： Vladislav Pariy Khrystyna Lipianina-Honcharenko Ruslan Brukhanskyi Anatoliy Sachenko Fedir Tkachyk Dmytro Lendiuk Department for Information Computer Systems and Control West Ukrainian National University Ternopil Ukraine Department of Business Analytics and Innovative Engineering West Ukrainian National University Ternopil Ukraine Research Institute for Intelligent Computer Systems West Ukrainian National University Ternopil Ukraine Department of Informatics and Teleinformatics Kazimierz Pulaski University of Technology and Humanities in Radom Radom Poland Department of Taxes and Fiscal Policy West Ukrainian National University Ternopil Ukraine

This article is devoted to the development of Intelligent Verbal Interaction Methods with Non-Player Characters in Metaverse Applications. The creation of a character model using MetaHuman Creator and the implementation of dialogue animation are considered. The construction of answers based on text analysis and their submission to the chat window is described. The system has been tested and found to be stable and have a satisfactory response time of approximately 2 seconds. The results show the successful implementation of methods of verbal interaction with non-game characters in metaverse, which can be useful for further research and development in this area.

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Mutual information based logistic regression for phishing URL detection

Cyber Security and Applications

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Cyber Security and Applications 2024年 2卷

作者： Vajrobol, Vajratiya Gupta, Brij B. Gaurav, Akshat Institute of Informatics and Communication University of Delhi India International Center for AI and Cyber Security Research and Innovations (CCRI) Asia University Taiwan Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan Ronin Institute Montclair NJ United States Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India Department of Electrical and Computer Engineering Lebanese American University Beirut 1102 Lebanon Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India

Phishing is a cybersecurity problem that hackers employ to deceive individuals and organizations. Phishing is dynamic in nature;the hackers change several tricks to deceive the victims in multiple ways. It is important to track the tricks of hackers with recent technology. This study makes a notable contribution to enhancing cybersecurity defences by offering insights that aid in the detection and mitigation of phishing threats. Specifically, the study's analysis of URLs using mutual information and logistic regression techniques yielded a remarkably high accuracy rate of 99.97%, surpassing previous efforts. The identification of the most informative features for distinguishing phishing attempts provides valuable intelligence for cybersecurity professionals, enabling them to bolster defenses and stay ahead of evolving phishing tactics. © 2024

关键词： Feature selection Logistic regression Mutual information Phishing URL analysis

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Darloc: Deep Learning and Augmentation Based Robust Magnetic Indoor Localization

SSRN

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SSRN 2022年

作者： Wang, Qinghu Jia, Jie Deng, Yansha Chen, Jian Wang, Xingwei Huang, Min Aghvami, Abdol Hamid School of Computer Science and Engineering Northeastern University Shenyang110819 China School of Computer Science and Technology Institute of Computer Engineering Inner Mongolia Minzu University Tongliao028000 China Department of Informatics King’s College London LondonWC2R 2LS United Kingdom School of Information Science and Engineering Northeastern University Shenyang110819 China

Magnetic-based indoor localization has attracted considerable attention due to the pervasiveness of geomagnetic fields and is free of extra infrastructures. However, existing approaches still face heterogeneity problems caused by the type of devices, the holding attitudes, and the moving speeds of pedestrians. To cope with it, we propose a novel deep learning and augmentation-based magnetic localization framework (DarLoc). First, we invoke orientation-insensitive magnetic signal extraction to remove the Direct Current (DC) component of the sequence and to eliminate the impact caused by different holding attitudes and various mobile devices. Second, we propose novel data augmentation and feature augmentation methods to extract features of speed information, thus tackling the multi-scale sequence problem caused by different moving speeds. Finally, we propose a deep multi-scale spatial-temporal learning model to extract both spatial and temporal features of the augmented sequences and to provide a robust localization for people with various moving speeds and attitudes. Extensive experiments, including a total of 189 volunteers with 4 different mobile devices and multiple moving speeds, are employed to evaluate the performance of DarLoc over 14 months. Experiment results reveal that: 1) DarLoc obtains an average localization accuracy of 0.47 m and 0.56 m under constant and variable moving speeds scenarios, respectively;2) DarLoc obtains about 41% and 60% localization accuracy improvement compared with the state-of-the-art localization methods. © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Construction of Ternary Bent Functions From Ternary Linear Functions

Construction of Ternary Bent Functions From Ternary Linear F...

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International Symposium on Multiple-Valued Logic

作者： Milena Stanković Radomir S. Stanković Claudio Moraga Jaakko T. Astola Faculty of Electronic Engineering Department of Computer ScienceNi&#x0161 Serbia Mathematical Institute of SASA Belgrade Serbia Faculty of Computer Science Technical University of Dortmund Dortmund Germany Department of Informatics Technical University &#x201C Federico Santa Mar&#x00ED a&#x201D Valpara&#x00ED so Chile Department of Signal Processing Tampere University of Technology Tampere Finland

ISBN: (数字)9781665423953

ISBN: (纸本)9781665423960

As in the binary case, ternary bent functions are defined as most non-linear ternary functions meaning that they are at the largest possible Hamming distance from affine ternary functions. It is therefore interesting to observe that some ternary bent functions can be constructed as various combinations of linear ternary functions. By starting from the combination of linear functions corresponding to the basic ternary bent functions the construction of different other ternary bent functions can be performed by the application of different combinations of FFT-like permutation matrices for ternary functions.

关键词： Hamming distance

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IRS Channel Estimation in Cell-free MIMO Network: A Coalition Formation Guided Federated Learning Approach

IRS Channel Estimation in Cell-free MIMO Network: A Coalitio...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Liu, Haoxuan Qi, Nan Li, Xiaojie Boulogeorgos, Alexandros A. A. Tsiftsis, Theodoros A. Xiao, Ming Röning, Juha Nanjing University of Aeronautics and Astronautics Nanjing China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China University of Western Macedonia Department of Electrical and Computer Engineering Kozani50100 Greece University of Thessaly Department of Informatics and Telecommunications Lamia35100 Greece University of Nottingham Ningbo China Department of Electrical and Electronic Engineering Ningbo315100 China School of Electrical Engineering KTH Royal Institute of Technology Stockholm Sweden University of Oulu Biomimetics and Intelligent Systems Group Finland

ISBN: (纸本)9798350368369

The downlink channel estimation is currently a critical bottleneck for IRS-assisted cell-free multiple input multiple output communication. Conventionally, most studies have employed deep learning methods to estimate the high-dimensional, complex cascaded channels generated by IRS, necessitating data collection from all users for centralized model training, which results in excessively large overheads, and data privacy problems. To tackle this challenge, a federated learning (FL)-based channel estimation framework incorporates coalition formation to guide the formation of FL user groups. We propose a coalition formation-enabled federated learning framework for channel estimation, utilizing a deep reinforcement learning (DRL) approach to intelligently group users into multiple coalitions, thereby improving channel estimation accuracy. Moreover, considering that nodes with similar distances to the base station and similar received signal power have a strong likelihood that they experience similar channel fading, we designed a transfer learning method that incorporates both received reference signal power and distance similarity metrics. The transfer learning technique is designed to accelerate the convergence of DRL-federated learning process. Simulations reveal that the proposed algorithms significantly reduce communication overhead for local users and improve data privacy while maintaining commendable channel estimation accuracy. © 2025 IEEE.

关键词： Deep reinforcement learning

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A two-stage deep learning-based hybrid model for daily wind speed forecasting

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Heliyon 2025年第1期11卷 e41026页

作者： Band, Shahab S. Ameri, Rasoul Qasem, Sultan Noman Mehdizadeh, Saeid Gupta, Brij B. Pai, Hao-Ting Shahmirzadi, Danyal Salwana, Ely Mosavi, Amir Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz 6803 Yemen Water Engineering Department Urmia University Urmia Iran Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India Department of Big Data Business Analytics National Pingtung University Pingtung Taiwan of Engineering Science and Technology National Yunlin University of Science and Technology 123 University Road Yunlin Douliou 64002 Taiwan Institute of Visual Informatics Universiti Kebangsaan Malaysia Selangor Bangi 43600 Malaysia John von Neumann Faculty of Informatics Obuda University Budapest Hungary Ludovika University of Public Service Budapest Hungary

Global adoption of wind energy continues to increase, while improving the efficiency of turbine settings requires reliable wind speed (WS) models. The latest models rely on artificial intelligence (AI) optimizations which constructs tests on a range of novel hybrid models to examine the reliability. Gradient Boosting (GB), Random Forest (RF), and Long Short-Term Memory (LSTM) are used in new combinations for data pre-processing. A Time Varying Filter-based Empirical Mode Decomposition (TVFEMD) model is coupled with the GB and LSTM standalone models, to create TVFEMD-GB and TVFEMD-LSTM hybrids, which are run in competition with each other. Eventually, a preferred hybrid form is established, simultaneous hybridization of TVFEMD with GB and LSTM. This study is the first to hybridize these fundamental systems, and create a TVFEMD-GB-LSTM model that can forecast WS. This study finds that the novel hybrid models exhibit superior performance to standalone GB and LSTM models, opening the pathway to alternative WS prediction techniques. © 2024 The Authors

关键词： Artificial intelligence Big data Data science Forecasting Gradient boosting Long short-term memory Machine learning Wind speed

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