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A Scalable Open-Set ECG Identification system Based on Compressed CNNs

IEEE Transactions on Neural Networks and Learning systems 2023年第8期34卷 4966-4980页

作者： Wu, Shun-Chi Wei, Shih-Ying Chang, Chun-Shun Swindlehurst, A. Lee Chiu, Jui-Kun National Tsing Hua University Department of Engineering and System Science Hsinchu30013 Taiwan University of California at Irvine Department of Electrical Engineering and Computer Science IrvineCA92697 United States

Deep learning (DL) is known for its excellence in feature learning and its ability to deliver high-accuracy results. Its application to ECG biometric recognition has received increasing interest but is also accompanied by several deficiencies. In this study, we focus on applying DL, especially convolutional neural networks (CNNs), to ECG biometric identification to address these deficiencies. Using prestored user-specific feature vectors, the proposed scheme can exclude unregistered subjects to realize 'open-set' identification. With the help of its scalable structure and 'transfer learning,' new subjects can be enrolled in an existing system without the need for storing the ECGs of those previously enrolled. Finally, schemes based on the quantum evolutionary algorithm (QEA) are presented to prune unnecessary filters in the proposed CNN model. The performance of the proposed scheme was evaluated using the ECGs of 285 subjects from the PTB dataset. The experimental results demonstrate an identification rate of more than 99% in closed-set identification. Although incorporating the proposed method for unregistered subject exclusion degraded the identification performance slightly, the ability of the approach to resist a dictionary attack was evident. Finally, using the QEA-based filter pruning method and its two-stage extension reduced the number of floating-point operations required to complete one identity recognition to 1.20% and 0.22% of the original value without significantly impacting the identification accuracy. © 2012 IEEE.

关键词： Biometrics

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Improving LoRaWAN Connectivity in Smart Agriculture Contexts Using Aerial IoT

Improving LoRaWAN Connectivity in Smart Agriculture Contexts...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Sanguesa, Julio A. Torres-Sanz, V. Serna, Felix Martinez, Francisco J. Garrido, Piedad Calafate, Carlos T. University of Zaragoza Computer Science and System Engineering Department Spain Universitat Politècnica de València Department of Computer Engineering Spain

ISBN: (纸本)9798350370218

The emergence of the Internet of Things (IoT) has enabled the creation of new solutions to real-world problems, including digitalization and real-time monitoring. Plenty of research has been done in applying IoT schemes in various areas, such as Industry 4.0, smart cities or, as in the case of this paper, smart agriculture. One of the main problems in rural areas is the Internet coverage. In many cases, the infrastructure is very poor, and mountainous terrain further complicates the communication among devices. In this paper, we develop an IoT framework based on the Long Range (LoRa) wireless technology, specially designed to solve this issue (i.e., the lack of communication coverage). In particular, we have endowed Unmanned Aerial Vehicles (UAVs) with a LoRa gateway to improve the coverage of LoRa devices in mountainous regions. Our experiments show that not only the distance between sender and receiver but also both the height of the UAV and the Fresnel zone clearance are critical factors for communications performance in mountainous areas. © 2023 IEEE.

关键词： communications coverage IoT LoRaWAN smart agriculture UAV

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Indian Supreme Court Judgement Dataset for Prediction Models

Indian Supreme Court Judgement Dataset for Prediction Models

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2023 Intelligent Computing and Control for engineering and Business systems, ICCEBS 2023

作者： Sivaranjani, N. Karthik, R. Sancith, Jaya Siva, P. Sri Sairam Engineering College Department of Computer Science and Business System India

ISBN: (纸本)9798350394580

The development of legal records requires extra time and their absurd length raises the need for programmed legal record handling frameworks. One of the handling steps is to recognize the essence of the reports expressions for legitimate case records. A computerized framework that could help a legal professional in anticipating the result of a case would help facilitate the legal interaction. For such a framework to be essentially valuable, expectations by the framework ought to be reasonable. To advance exploration in growing such a framework, this paper presents the Indian Legal Judgment Dataset (ILJD). ILJD is a huge corpus of 35k Indian Supreme Legal disputes commented on with unique court choices. A part of the corpus (a different test set) is commented on with best quality level clarifications by lawful specialists. © 2023 IEEE.

关键词： chi2 Legal Dataset Legal judgement Prediction Machine Learning Supreme Court of India

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Quadruped Stanford Pupper Type Robot Modeling and Simulation 18

Quadruped Stanford Pupper Type Robot Modeling and Simulation

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18th Iberian Conference on Information systems and Technologies, CISTI 2023

作者： Mija, Nelu Leba, Monica Rus, Cosmin Muntean, Emanuel University of Petrosani UPET Department of System Control and Computer Engineering Petrosani Romania

ISBN: (纸本)9789893347928

Nowadays there are many research efforts in the field of artificial intelligence applied in all the fields of robotics. There are developed and trained new models both supervised and unsupervised learning. ln order to be able to get a reliable final product, a very important step is modeling and simulation. This paper focuses on the analysis of a quadruped robot, Stanford Pupper type, for which the mathematical model for the locomotor system was developed in order to be then implemented in a simulator that allows the development of machine learning algorithms for training it. There can be implemented in the simulator various use cases, like: learning to walk both supervised and unsupervised scenarios, learning to navigate on a zone with uneven surface and with small obstacles, or learning to climb stairs or other big obstacles. © 2023 ITMA.

关键词： Musculoskeletal system

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Combinatorial Kinetic Dualistic Auction Model for Metaverse Service Allocation 1

Combinatorial Kinetic Dualistic Auction Model for Metaverse ...

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IEEE AITU: 1st International Student Conference: "Digital Generation", AITU 2024

作者： Singh, Raj Sarkar, Pritam Bandyopadhyay, Anjan Swain, Sujata Sardar, Tanvir Habib Pandey, Bishwajeet School of Computer Engineering KIIT 751024 India Department of Computer Science GITAM University Bengaluru India Department of Intelligent System and Cybersecurity Astana IT University Kazakhstan

ISBN: (纸本)9798350364378

The focus of this research work revolves around the utilization of a specialized"Combinatorial Kinetic Dualistic Auction Model" designed specifically for"metaverse Services." The main goal is to improve the allocation of resources in this ever-changing environment and enhance the overall experience for end-users. Through the exploration of metaverse services, we aim to further develop the comprehension and practical application of the Combinatorial Kinetic Dualistic Auction Model, ultimately contributing to a more immersive and responsive virtual experience. ©2024 IEEE |

关键词： Resource allocation

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Battery Monitoring system on IoT-Based Smart Micro Grid 4

Battery Monitoring System on IoT-Based Smart Micro Grid

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4th International Conference on Electronic and Electrical engineering and Intelligent system, ICE3IS 2024

作者： Iksan, Nur Widodo, Djoko Adi Djuniadi, Djuniadi Putri, Riana Defi Mahadji Suryanto, Agus Udayanti, Erika Devi Universitas Negeri Semarang Faculty of Engineering Department of Electrical Engineering Indonesia Universitas Dian Nuswantoro Faculty of Computer Science Department of Information System Indonesia

ISBN: (纸本)9798350378368

The use of batteries as an energy storage medium has a very important role in the installation of renewable energy power plants, such as photovoltaics to overcome intermittency in photovoltaics and to maintain stable power quality. In this study, the installation of solar plants that was used as the research object was a smart micro grid that integrates photovoltaic electrical energy sources with the Utility electricity network for residential scale. The battery monitoring system is used to manage the performance of existing batteries on the electricity grid through analysis of the battery's electrical values such as current, voltage, power and energy, so that it can function optimally in storing electrical energy and supplying electricity to users. Internet of things (IoT) technology is used to provide easy transfer of data obtained through the process of data acquisition to data visualization provided to be accessed by users in real time. This research aims to develop an IoT-based battery monitoring system. The development carried out focuses on the data acquisition process, the data transfer process using IoT technology, the process of analyzing and visualizing data on the IoT cloud and remote access to users. © 2024 IEEE.

关键词： Battery IoT Monitoring Smart Micro Grid

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A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

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computers, Materials & Continua 2023年第12期77卷 3969-3992页

作者： Naeem Ullah Javed Ali Khan Sultan Almakdi Mohammed S.Alshehri Mimonah Al Qathrady Eman Abdullah Aldakheel Doaa Sami Khafaga Department of Software Engineering University of Engineering and TechnologyTaxila4400Pakistan Department of Computer Science Faculty of PhysicsEngineeringand Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Departments of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Tomato leaf diseases significantly impact crop production,necessitating early detection for sustainable *** Learning(DL)has recently shown excellent results in identifying and classifying tomato leaf ***,current DL methods often require substantial computational resources,hindering their application on resource-constrained *** propose the Deep Tomato Detection Network(DTomatoDNet),a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome *** Convn kernels used in the proposed(DTomatoDNet)framework is 1×1,which reduces the number of parameters and helps in more detailed and descriptive feature extraction for *** proposed DTomatoDNet model is trained from scratch to determine the classification success rate.10,000 tomato leaf images(1000 images per class)from the publicly accessible dataset,covering one healthy category and nine disease categories,are utilized in training the proposed DTomatoDNet *** specifically,we classified tomato leaf images into Target Spot(TS),Early Blight(EB),Late Blight(LB),Bacterial Spot(BS),Leaf Mold(LM),Tomato Yellow Leaf Curl Virus(YLCV),Septoria Leaf Spot(SLS),Spider Mites(SM),Tomato Mosaic Virus(MV),and Tomato Healthy(H).The proposed DTomatoDNet approach obtains a classification accuracy of 99.34%,demonstrating excellent accuracy in differentiating between tomato *** model could be used on mobile platforms because it is lightweight and designed with fewer *** farmers can utilize the proposed DTomatoDNet methodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.

关键词： CNN deep learning DTomatoDNet tomato leaf disease classification smart agriculture

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Curve Monitoring and Alert system for Smart Transportation 1

Curve Monitoring and Alert System for Smart Transportation

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1st IEEE International Conference on Cognitive Robotics and Intelligent systems, ICC - ROBINS 2024

作者： Natarajan, Yuvaraj Sri Preethaa K, R. Viswanathan, Shrinithi Jayarathinam, Sandhiya Balasubramaniam, Vikas Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kyungpook National University Department of Robot and Smart System Engineering Daegu Korea Republic of

ISBN: (纸本)9798350372748

The proposed IoT-based Smart Curve Monitoring and Alert system is designed to improve road safety through the fusion of embedded systems, machine learning and edge computing technologies. The proposed system is primarily powered by ESP32 microcontroller, continuously gathers data from an array of sensors, including accelerometers, gyroscopes and a potentiometer to set the speed limit. This collected data is then subjected to analysis by a Convolutional Neural Network (CNN) model, which has been trained to recognize driving patterns, especially when navigating curves. Here, CNN classifies the vehicle's behavior as safe and risky, and if a risky behavior is identified, it triggers an alert and dispatches relevant data to a NodeMCU device. The NodeMCU plays a crucial role in the system by monitoring the communication tasks. It receives the alert from the ESP32, validates the vehicle's speed against the preset limit, and, if necessary, activates a GSM module to send an immediate notification to designated guardian. To ensure real-time monitoring and awareness, all pertinent information will be displayed on the LCD screen, including the vehicle's behavior classification, current speed, speed limit and alert status. By addressing reckless driving behaviors, particularly on curves, the system contributes significantly to safer roads and reinforces the importance of responsible driving practices. © 2024 IEEE.

关键词： Convolution

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Design an Optimal Augmented PID Controller for Electric Vehicle Speed Control 23th

Design an Optimal Augmented PID Controller for Electric Vehi...

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23rd International Conference on Intelligent systems Design and Applications, ISDA 2023

作者： Abood, Layla H. Ali, Inaam I. Abood, May H. Control and System Engineering Department University of Technology Baghdad Iraq Electrical Engineering Department University of Technology Baghdad Iraq Computer Engineering Department University of Al Iraqia Baghdad Iraq

ISBN: (纸本)9783031648465

The prominence growth of the Electric Vehicle (EV) industry requests from researcher to adopts effective controlling techniques for marinating different issues. In this paper an augmented PID controller is suggested for controlling speed in EV based using sigmoid function relation and the smart and unique Gray wolf Optimization (GWO) algorithm to achieve a satisfied and robust system desired value, a comparison is utilized with PID controllers (Optimal PID, Normal PID) to evaluate its behavior then a robustness analysis is applied by varying system desired speed in multiple periods, the controller track the desired value perfectly and force the system to an efficient response with robust system behavior. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Electric vehicles

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A Multi-Agent Garage Service Search and Recommendation with Hybrid MLs and LLMs

A Multi-Agent Garage Service Search and Recommendation with ...

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2025 International Conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Shuvo, Mahfuzur Rahman Rahman, Ashifur Akuthota, Vishwanath Paul, Tanay Islam, Mahfujul Ashraf, Md Sadi Roy, Prosenjit Reza, Md Tanzim International American University Department of Management Information System United States Chittagong University of Engineering and Technology Department of Computer Science and Engineering Bangladesh TechOptima Hyderabad India Brac University Department of Computer Science and Engineering Bangladesh Prairie View A & M University Department of Computer Science TX United States

ISBN: (纸本)9798331513894

The automobile service industry's explosive growth highlights the need for creative approaches to boost operational effectiveness and user experience. This study introduces a Hybrid Garage Assistance system, integrating Classical Machine Learning (ML) techniques with Generative AI to optimize garage service discovery and analysis. The system employs sophisticated data processing methods, including Term Frequency-Inverse Document Frequency (TF-IDF) vectorization and regex-based service detection, to extract actionable insights from unstructured garage *** to the system are machine learning models Random Forest (RF) and XGBoost (XGB) which achieve high precision and recall in classifying garage services. A hybrid search mechanism, combining cosine similarity with ML-driven predictions, ensures the delivery of highly personalized search results. To further refine decision-making, the system incorporates Generative AI models such as Perplexity for web-based research, Gemini for location-specific analysis, Mistral for email sending and GPT-4 for detailed service recommendations and dall-e for creating user specific parts images. These advanced tools provide users with comprehensive information that enables them to make well-informed decisions about garage *** evaluation of the system is conducted using robust metrics, including precision, recall, F1-score, and system latency. Experimental results reveal a precision of 85%, recall of 70.8%, and an F1-score of 77.2%, demonstrating the efficacy of integrating classical ML with generative AI. The system's average latency of 5.9 seconds ensures a seamless and responsive user *** hybrid framework highlights the potential of blending classical ML and Large Language Models (LLMs) to enhance search and recommendation functionalities, offering a scalable and robust blueprint for future advancements in the automotive service sector. The system's Propose a Multi-Agent system With high accuracy,

关键词： Inverse problems

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