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Synergistic application of neuro-fuzzy mechanisms in advanced neural networks for real-time stream data flux mitigation

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Soft Computing 2024年第20期28卷 12425-12437页

作者： Goyal, Shivam Kumar, Sudhakar Singh, Sunil K. Sarin, Saket Priyanshu Gupta, Brij B. Arya, Varsha Alhalabi, Wadee Colace, Francesco Chandigarh College of Engineering and Technology Panjab University Chandigarh160019 India Department of Computer Science and Information Engineering Asia University Taichung413 Taiwan Symbiosis International University Pune India Kyung Hee University 26 Kyungheedae-ro Dongdaemun-gu Seoul Korea Republic of Dehradun India Department of Business Administration Asia University Taichung Taiwan Department of Electrical and Computer Engineering Lebanese American University Beirut Lebanon Department of Computer Science Immersive Virtual Reality Research Group King Abdulaziz University Jeddah Saudi Arabia Department of Industrial Engineering University of Salerno Fisciano Italy

Stream mining, especially with concept drift, presents significant challenges across various domains. As data streams evolve over time, initial models become less effective. We present a novel approach using fuzzy ARTMAP’s adaptability and neural networks’ robustness to address concept drift. Our method dynamically updates models based on changing data distributions, enabling real-time adap- tation. By integrating fuzzy ARTMAP with backpropagation, it facilitates agile learning and accurate predictions in evolving scenarios. Through rigorous exper- iments, we demonstrate the effectiveness of our method in managing concept drift and achieving substantial performance improvements. The achieved accu- racy of 85.07% and F1 score of 72.47 demonstrate the effectiveness of the approach in real-time classification tasks. This research extends beyond just performance metrics. By leveraging the interpretability of fuzzy ARTMAP, we gain valuable insights into the mechanisms that enable our model to adapt to concept drift. This deeper understanding paves the way for further advancements in this area. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Backpropagation

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Enhanced UAV Pursuit-Evasion Using Boids Modelling:A Synergistic Integration of Bird Swarm Intelligence and DRL

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computers, Materials & Continua 2024年第9期80卷 3523-3553页

作者： Weiqiang Jin Xingwu Tian Bohang Shi Biao Zhao Haibin Duan Hao Wu School of Information and Communications Engineering Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an710049China China Academy of Electronics and Information Technology China Electronics Technology Group Corporation(CETC)Beijing100041China Department ofAutomatic Control School of Automation Science and Electrical EngineeringBeihangUniversityBeijing100191China

TheUAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles(UAVs),which is pivotal in public safety applications,particularly in scenarios involving intrusion monitoring and *** address the challenges of data acquisition,real-world deployment,and the limited intelligence of existing algorithms in UAV pursuit-evasion tasks,we propose an innovative swarm intelligencebased UAV pursuit-evasion control framework,namely“Boids Model-based DRL Approach for Pursuit and Escape”(Boids-PE),which synergizes the strengths of swarm intelligence from bio-inspired algorithms and deep reinforcement learning(DRL).The Boids model,which simulates collective behavior through three fundamental rules,separation,alignment,and cohesion,is adopted in our *** integrating Boids model with the Apollonian Circles algorithm,significant improvements are achieved in capturing UAVs against simple evasion *** further enhance decision-making precision,we incorporate a DRL algorithm to facilitate more accurate strategic *** also leverage self-play training to continuously optimize the performance of pursuit *** experimental evaluation,we meticulously designed both one-on-one and multi-to-one pursuit-evasion scenarios,customizing the state space,action space,and reward function models for each *** simulations,supported by the PyBullet physics engine,validate the effectiveness of our proposed *** overall results demonstrate that Boids-PE significantly enhance the efficiency and reliability of UAV pursuit-evasion tasks,providing a practical and robust solution for the real-world application of UAV pursuit-evasion missions.

关键词： UAV pursuit-evasion swarm intelligence algorithm Boids model deep reinforcement learning self-play training

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Ensuring IoT controller Reliability with Colored Generalized Stochastic Petri Net 28

Ensuring IoT Controller Reliability with Colored Generalized...

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28th International computer science and engineering Conference, ICSEC 2024

作者： Samngamnoi, Kruntarat Pomsiri, Sutinun Kamput, Apipath Dechsupa, Chanon College of Computing Khon Kaen University Intelligent Software Engineering Research Group Department of Computer Science Khon Kaen40002 Thailand

ISBN: (纸本)9798350366860

In IoT systems managing multiple devices simultaneously, errors in system controllers often undermine intended operations. Formal verification offers a method to assess system reliability. Colored Generalized Stochastic Petri Net (CGSPN), a formal language, facilitate correctness checks of such systems. This study proposes a verification approach by translating a C++-based system controller of a self-service machine into a CGSPN models and validating it using the Snoopy Tool. Mapping techniques employed to transform components in the controller into CGSPN models are provided. Results demonstrate the method's efficacy in verifying system safety properties, simulating system events, and enabling quantitative verification. © 2024 IEEE.

关键词： Stochastic systems

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Novel 1-$\varphi$ High-Voltage Boosting Transformerless Inverter Topology with Optimal Power Components and Negligible Leakage Currents

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IEEE Transactions on Industry Applications 2023年第5期59卷 1-14页

作者： Chamarthi P.K. Muduli U.R. Moursi M.S.E. Al-Durra A. Khadkikar V. Hosani K.A. EL-Fouly T.H.M. Real-Time Control and Optimization Laboratory (RT-COOL) in Holcombe Department of Electrical and Computer Engineering Clemson University USA APEC Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi UAE

Inverter topologies for integrating a rooftop photovoltaic (PV) unit into a microgrid are becoming increasingly complex. This paper proposes a high-voltage boosting transformerless inverter (HVBTI) topology for enhancing such applications. The coupled inductor-based high voltage gain feature of the HVBTI configuration allows power to be delivered into the grid from a lower voltage PV source without using higher duties. In addition, HVBTI suppresses leakage current as the common connection shared between the

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terminal of the PV source and the grid neutral point. In comparison to existing topologies, the HVBTI topology uses a compact pulse width modulation strategy to control only six controllable switching devices. Again, using lower-rated switching devices is more cost-effective while increasing reliability and efficiency. In a laboratory prototype of a 1 kVA grid integrated system, the proposed HVBTI configuration is validated, and the maximum efficiency of the HVBTI is estimated at approximately 95%. Author

关键词： Boosting Inverter Inductors Inverters Leakage currents Low Switch Count Microgrid Rooftop Photovoltaics Switches Topology Transformers Voltage Voltage Gain

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Memristive effects on an improved discrete Rulkov neuron model

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science China(Technological sciences) 2023年第11期66卷 3153-3163页

作者： BAO Han LI KeXin MA Jun HUA ZhongYun XU Quan BAO BoCheng School of Microelectronics and Control Engineering Changzhou UniversityChangzhou 213159China Department of Physics Lanzhou University of TechnologyLanzhou 730050China School of Computer Science and Technology Harbin Institute of TechnologyShenzhenShenzhen 518055China

A change in neuronal-action potential can generate a magnetically induced current during the release and propagation of intracellular *** better characterize the electromagnetic-induction effect,this paper presents an improved discrete Rulkov(ID-Rulkov)neuron model by coupling a discrete model of a memristor with sine memductance into a discrete Rulkov neuron *** ID-Rulkov neuron model possesses infinite invariant points,and its memristor-induced stability effect is evaluated by detecting the routes of period-doubling and Neimark-Sacker *** investigated the memristor-induced dynamic effects on the neuron model using bifurcation plots and firing ***,we theoretically expounded the memristor initial-boosting mechanism of infinite coexisting *** results show that the ID-Rulkov neuron model can realize diverse neuron firing patterns and produce hyperchaotic attractors that are nondestructively boosted by the initial value of the memristor,indicating that the introduced memristor greatly benefits the original neuron *** hyperchaotic attractors initially boosted by the memristor were verified by hardware experiments based on a hardware *** addition,pseudorandom number generators are designed using the ID-Rulkov neuron model,and their high randomness is demonstrated based onstrict test results.

关键词： neuron model invariant point memristive effect complex dynamics pseudorandom number generators

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Rapid computational algorithm with minimum user input for reconstructing phase images in structured illumination Digital Holographic Microscopy

Rapid computational algorithm with minimum user input for re...

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3D Image Acquisition and Display: Technology, Perception and Applications, 3D 2024 - Part of Optica Imaging Congress

作者： Obando-Vásquez, Sofía Castaneda, Raúl Restrepo, René Trujillo, Carlos Doblas, Ana Applied Optics Group School of Applied Science and Engineering EAFIT University Medellin Colombia Electrical and Computer Engineering Department University of Massachusetts Dartmouth United States

A rapid computational algorithm is presented for Structured Illumination in Digital Holographic Microscopy. The proposed algorithm is based on the minimization of two cost functions to reconstruct improved resolution ... 详细信息

关键词： Cost functions

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LSTM Neural Networks for Brain Signals and Neuromorphic Chip 2

LSTM Neural Networks for Brain Signals and Neuromorphic Chip

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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Goel, Akash Katiyar, Alok Goel, Amit Kumar Kumar, Adesh Department of Computer Science & Engineering Galgotia's University NCR Greater Noida India KIET Group of Institutions Delhi-NCR Ghaziabad India Department of Computer Science & Engineering Apeejay Stya University Haryana Gurugram India Department of Electrical & Electronics Engineering UPES Uttarakhand Dehradun248007 India

ISBN: (纸本)9798331541217

Long Short-Term Memory (LSTM) is a type of RNN architecture commonly employed in natural language processing, speech recognition, and various sequence modeling applications. A normal recurrent neural network (RNN), which features a simple architecture of input, hidden state, and output, an LSTM is distinguished by a more complex structure that incorporates supplementary memory cells and gates. This architecture allows the LSTM to selectively retain or discard information from previous time steps. The fundamental component of these devices is a memory cell, which has two input gates, one output gate, and a forget gate. These gates manage the inflow and outflow of data, allowing LSTMs to choose to retain or eliminate information. The articles concentrate on the hardware chip design of the LSTM-like mechanism in neuromorphic systems, which incorporates two LSTM layers and processes eight neurons together with their weights in Xilinx ISE 14.7 and simulates the design for logic verification test cases. LSTM networks could inspire neuromorphic technologies that process time-sensitive information and memory like the brain. The novelty of the work is in the scalable hardware chip design and its performance is synthesized on the FPGA that supports the frequency 570.00 MHz. The design has shown the optimal performance in terms of parameters such as delay, frequency, memory, flip flops, and IoBs onVirtex-5 FPGA for scalable LSTM that is tested up to 64 neurons. The hybrid architecture is based on two intermediate LSTM layers processing 8 neurons in a single slot. © 2024 IEEE.

关键词： Long short-term memory

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Intrusion Detection in the Internet of Things Using Fusion of GRU-LSTM Deep Learning Model

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Intelligent Automation & Soft Computing 2023年第8期37卷 2279-2290页

作者： Mohammad S.Al-kahtani Zahid Mehmood Tariq Sadad Islam Zada Gauhar Ali Mohammed ElAffendi Department of Computer Engineering Prince Sattam Bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Computer Engineering University of Engineering and TechnologyTaxila47050Pakistan The FAMLIR Group The University of LahoreLahore54000Pakistan Department of Computer Science University of Engineering&TechnologyMardan23200Pakistan Faculty of Computing International Islamic UniversityIslamabad44000Pakistan EIAS Data Science and Blockchain Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

Cybersecurity threats are increasing rapidly as hackers use advanced *** a result,cybersecurity has now a significant factor in protecting organizational *** detection systems(IDSs)are used in networks to flag serious issues during network management,including identifying malicious traffic,which is a *** remains an open contest over how to learn features in IDS since current approaches use deep learning *** learning,which combines swarm intelligence and evolution,is gaining attention for further improvement against cyber *** this study,we employed a PSO-GA(fusion of particle swarm optimization(PSO)and genetic algorithm(GA))for feature selection on the CICIDS-2017 *** achieve better accuracy,we proposed a hybrid model called LSTM-GRU of deep learning that fused the GRU(gated recurrent unit)and LSTM(long short-term memory).The results show considerable improvement,detecting several network attacks with 98.86%accuracy.A comparative study with other current methods confirms the efficacy of our proposed IDS scheme.

关键词： Cyber security deep learning intrusion detection PSO-GA CICIDS-2017 intelligent system security and privacy IoT

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Fraud Detection in Financial Domain using Machine Learning

Fraud Detection in Financial Domain using Machine Learning

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Pathak, Nilotpal Singhal, Swasti Gl Bajaj Institute of Technology and Management Department of Computer Science Engineering Greater Noida India Kiet Group of Institutions Department of Computer Science and Information Technology Ghaziabad India

ISBN: (纸本)9798350322309

Digital fraud has become a menace in every industry. It is critical for any firm to have a concentrated focus on detecting and preventing fraudulent activities. Security is a priority. The way we communicate has changed dramatically as a result of digitization. A simple click of a mouse, complete our day-to-day transactions. On the other hand, it has created concerns from swindlers who take advantage of absent protections in current financial systems and mimic real customers, undertake time-consuming transactions on their behalf that result in a profit causing financial setbacks to the organizations and customers. Organizations will need to pay attention as a result of this. Its brand value is also affected. Organizations have learned from their mistakes. To prevent fraud and keep ahead of the criminals, it is necessary to maintain a constant focus. It's critical to keep an eye on major trends. We might be able to tell the difference between a legitimate and a fraudulent transaction, obtaining customer data such as geolocation, authentication, and so on, it is possible to keep track of the device's IP address during the session. Machine Learning (ML) will assume a significant part in the future in identifying examples of such frauds consequently. We use algorithms like decision trees, XGBoost, K-NN and others to find an optimal solution for our concerning project. © 2023 IEEE.

关键词： Feature extraction

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Machine Learning Based Sentiment Analysis of Tweets

Machine Learning Based Sentiment Analysis of Tweets

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2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Khatter, Harsh Aggrawal, Niyati Shrivastava, Ajay Kumar Kiet Group of Institutions Department of Computer Science Delhi-NCR Ghaziabad India Jaypee Institute of Information Technology Department of Computer Science & Engineering Uttar Pradesh Noida India

ISBN: (纸本)9798350344387

Sentiment Analysis is one of the computer science study areas that is expanding quickly, making it difficult to keep up with all the activity to fit in the business *** Analysis is used in fields of Stock Markets, Social sites like Twitter, Instagram, Facebook, elections, disasters, medicine, Sentiment Analysis in Arabic languages, software engineering to analyse the products based on the polarity scores that are classified in Positive Sentiments, Negative Sentiments and Neutral Sentiments. Here we also analysed the Sentiments of peoples outside the eco places such as park are more positive than peoples inside the park. We here used NLTK, VADER module, some deep learning modules like Roberta, Text blob and a Tweepy API. Based on the dataset utilized, the domain covered, the Arabic language type, the pre-processing techniques, the features used, the word representation, the methodology employed, and the evaluation metrics used to evaluate the suggested techniques, the included research were examined. Using the input given by Twitter popups and characterizing impressions, we present an economic analysis and responses to the wildfires that occurred in Portugal and Spain. We employ methods for machine learning technique to find the info in this text. We calculate a value for the relationship between attitudes toward wildfires and air quality and exposure are determined by Euclidean distance from the catastrophic event. © 2023 IEEE.

关键词： Information Retrieval Machine Learning NLTK Sentiment Analysis Text blob Twitter Tweets

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