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A Comprehensive Assessment of Fuzzy Rule-based systems for Heart Attack Prediction 2

A Comprehensive Assessment of Fuzzy Rule-based Systems for H...

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2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Ugale, Om Puri, Chetan Gote, Pradnyawant M. Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

Heart disease is a leading global cause of death, highlighting the need for accurate and efficient risk assessment methods. Traditional models often fail to address the uncertainty and vagueness in medical data. A Fuzzy Rule-Based System (FRBS) offers a solution by leveraging fuzzy logic to handle imprecise inputs like age, blood pressure, and lifestyle factors. The system uses fuzzification to convert crisp inputs into fuzzy sets, applies expert-defined IF-THEN rules through an inference engine, and translates outputs into interpretable risk scores via defuzzification. FRBS provides a flexible and patient-centric approach, accommodating linguistic variables and ambiguous data. applications include real-time monitoring through wearable devices and telemedicine platforms, enhancing preventive care and empowering early detection of heart disease. This innovative system bridges the gap between medical uncertainty and actionable insights, improving healthcare © 2025 ieee.

关键词： Fuzzy rules

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Position-aware Hypergraph Message-Passing Neural Network

Position-aware Hypergraph Message-Passing Neural Network

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2025 ieee international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Xinyu Jiang, Qize Zhang, Hanyuan Sun, Weiwei School of Computer Science Shanghai Key Laboratory of Data Science Fudan University Shanghai China Shanghai Institute of Intelligent Electronics & Systems Shanghai China

ISBN: (纸本)9798350368741

Hypergraph neural networks can model more flexible connectivity relationships, are used to model higher-order interactions, and have produced strong results in many real-world applications. However, the currently existing hypergraph neural networks need more exploration in capturing the global positional information of nodes in hypergraphs. Although there have been many explorations of the problem in graph neural networks, extending these approaches to hypergraphs is fraught with challenges. The major challenge is that hyperedges in hypergraphs are the other dimensional element of the incidence structure, have more flexible definitions than edges in graphs, and require more attention when learning global positional information. We propose a novel position-aware hypergraph message-passing neural network framework to address the above challenges. Specifically, we propose a global positional embedding learning approach that can separately model global positional information for nodes and hyperedges. At the same time, we also optimize the learning of local structures with hyperedges. Experiments on several publicly available benchmark datasets find that our proposed method outperforms many state-of-the-art methods. © 2025 ieee.

关键词： Hypergraph Learning Hypergraph Neural Network Message Passing Neural Network

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real-time Pedestrian Detection and Tracking in Dynamic Urban Environments Using YOLO for Intelligent Video systems 3

Real-Time Pedestrian Detection and Tracking in Dynamic Urban...

引用

3rd international conference on Intelligent systems, Advanced computing, and Communication, ISACC 2025

作者： Aatrey Anand, V. Gautham, B. Sangeetha, K. Rajalakshmi Engineering College Department of AIML India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

This paper describes a comprehensive system for real-time pedestrian detection and tracking, which is intended to suit the needs of intelligent video surveillance in dynamic metropolitan areas. The main detection component makes use of the most recent version of the YOLO (You Only Look Once) algorithm, which is noted for its speed and accuracy in identifying pedestrians in complicated settings. The CrowdHuman Pedestrain Dataset, a difficult dataset that replicates actual urban situations where a variety of environments, fluctuating lighting, and occlusions provide major obstacles, is used to benchmark the system. Our findings show that the system can be used for intelligent traffic management, public safety, and urban surveillance applications by improving both detection accuracy and tracking resilience while preserving real-time performance. This work contributes to the growing field of intelligent video analytics by combining cutting-edge deep learning techniques with practical tracking and interpretability solutions. © 2025 ieee.

关键词： Deep Learning Intelligent Video systems Pedestrian Detection real-time Detection YOLO

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Deep Learning Techniques for Object Detection in Underwater Environments

Deep Learning Techniques for Object Detection in Underwater ...

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2025 ieee international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Praveen, R.V.S. Maindola, Meenakshi Anandhi, R.J. Thandra, Nagarjuna Bansal, Saloni Sharma, Vishal LTIMindtree Limited Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India New Horizon College of Engineering Department of Information Science Engineering Bangalore India MLR Institute of Technology Dept. Computer Science and Engineering Telangana Hyderabad India GLA University Department of Computer Engineering and Applications Mathura India Lovely Professional University Phagwara India

ISBN: (纸本)9798331512965

The degradation of image quality caused by things like light absorption, scattering, and distortion makes object detection in underwater environments a unique challenge. applications like marine research, environmental monitoring, and autonomous underwater vehicles (AUVs) are restricted in their effectiveness by traditional image processing techniques, which often struggle with these complexities. Recent years have seen the rise of deep learning techniques as a potent weapon in the fight against these obstacles. In order to accurately detect objects underwater, this paper investigates the use of deep learning models, specifically convolutional neural networks (CNNs). We explore cutting-edge models and methods like attention mechanisms, transfer learning, and hybrid deep learning architectures to improve detection performance in challenging underwater environments. Furthermore, we utilize data augmentation techniques and synthetic dataset generation to tackle the critical issue of limited labeled underwater datasets. From a detection accuracy and robustness perspective, the experimental results show that our suggested deep learning method is light years ahead of the competition. Marine exploration, autonomous underwater vehicle navigation, and underwater surveillance systems can all benefit from this study's new insights into how to better detect objects in real time while submerged. © 2025 ieee.

关键词： Convolutional neural networks

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A Lightweight Network Deployed on ARM Device for Hand Gesture Recognition

A Lightweight Network Deployed on ARM Device for Hand Gestur...

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Intelligent Control, computing and Communications (IC3), international conference on

作者： Shreyas P S Varshith V Hegde Shivaneeth Keshav Shetty Shreevanth R Bhandary Manjunath Kotari Alva's Institute of Engineering and Technology Mijar Moodbidri India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Enhancing interactions with natural and user-friendly computer interfaces (HCIs) requires the use of hand gestures. This article illustrates a particularly created brilliant neural network that can identify the flat-based platform's movements. real-time applications on low-power devices can benefit from the suggested approach, which is solely software-implemented and tailored for resource-constrained contexts. To classify hand gestures with high accuracy and minimal computing complexity, the network analyzes input from infrared (IR) sensors. Particular care is taken to reduce the model's memory and processing needs without sacrificing recognition efficiency. The system is tested using a variety of hand gestures and shows that, despite having little hardware resources, it is capable of quickly and accurately recognizing movements. The software solution offers a scalable method for integrating gesture detection systems into a range of HCI applications on wearable and embedded devices. The system demonstrated competitive performance and low power consumption following rigorous testing. This study demonstrates the practical application of lightweight, software-driven machine learning models in fields including home automation, assistive technology, and robotics.

关键词： Hands Program processors Power demand Computational modeling Neural networks Gesture recognition Robot sensing systems real-time systems Sensor systems Testing

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A Comprehensive Comparison Between YOLOv5 and YOLOv8 for embedded systems applications

A Comprehensive Comparison Between YOLOv5 and YOLOv8 for Emb...

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Smart systems for applications in Electrical Sciences (ICSSES), international conference on

作者： Sailesh Kumar Pasam Akshay G. Bhosale Symbiosis Institute of Technology Pune Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

Object detection has a vast potential in computer vision, which can be used in any application starting from autonomous vehicles to real-time monitoring systems. Among these methodologies, the YOLO (You Only Look Once) series has become one of the most successful ones in terms of speed and accuracy. This paper seeks to discuss and compare YOLOv5 to YOLOv8 to highlight the improvement made in the present model and their ability to be implemented in embedded systems. Hence, YOLOv5 adds and refines lightweight architecture learning and bounding box anchors for improved deployment. YOLOv6 enhances the primary parameter of real-time applications, namely inference speed, while YOLOv7 further introduces the techniques of model scaling and removing the concept of anchors for precision. YOLOv8 was subsequently improved with transformer-based modules and model scalability to work on both high-end and low-end devices. Specifically, the results obtained through the experiments are discussed, focusing on the so-called key performance indicators such as mean average precision mAP, frames per second FPS, and computational performance, and their possible intersection for real-time object detection applications. Further, a literature review highlights their reallife application in various business contexts. This paper provides the reader with a roadmap for choosing the best implementation for YOLO at the system level to maintain a high degree of accuracy, speed, and resource use.

关键词： YOLO embedded systems Accuracy Surveillance Scalability Key performance indicator Transformers Smart systems real-time systems Systematic literature review

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A 0.9pJ/b 108Gb/s PAM-4 VCSEL-Based Direct-Drive Optical Engine 72

A 0.9pJ/b 108Gb/s PAM-4 VCSEL-Based Direct-Drive Optical Eng...

引用

72nd ieee international Solid-State Circuits conference, ISSCC 2025

作者： Krishnamurthy, Sashank Mondal, Susnata Qiu, Junyi Acikalin, Tolga Bose, Soumya Yamada, Shuhei Jaussi, James Mansuri, Mozhgan Intel HillsboroOR United States

ISBN: (纸本)9798331541019

The surge in high-throughput applications necessitates advancements in data transmission. Increasingly complex, power-hungry equalization and digital signal processing (DSP) techniques limit electrical interconnect scalability and reach. Pluggable optical modules extend the reach, but at the cost of scalability/power challenges, due to their integrated re-timer (or DSP) which cleans up the received signal before sending it to the optical engine (OE). By directly connecting computing (XPU) systems to OEs, emerging co-packaged optics (CPO) [1-2] can address these challenges by eliminating the re-timer. Multi-mode vertical-cavity surface-emitting lasers (VCSELs) continue to improve their performance and enable high-BW connectivity over a few tens of meters [1-2] in conjunction with high bandwidth (BW) circuit techniques. Higher order pulse-amplitude modulation such as PAM-4 doubles the BW. This paper presents a PAM-4 VCSEL-based direct-drive OE targeting CPO applications [1-2]. The XPU IC is not implemented in this work. The directdrive OE integrates VCSEL driver (VCDRV) and transimpedance amplifier front end (TIAFE) ICs, with their VCSEL and photodiode (PD) counterparts which are fiber terminated with optical waveguides or direct optical wiring (DOW) technology [3]. Several circuit techniques are introduced to enable the >100Gb/s PAM-4 direct-drive OE: (1) a high-linearity coupledinductor-based compact complex-zero continuous-time linear equalizer (CZ-CTLE) to equalize a complex-pole pair in VCSEL optical response;(2) a high-linearity differential TIAFE to handle high input optical modulation amplitudes (OMAs);and (3) an active complex-zero CTLE to equalize for the complex-pole pair in the shunt-feedback TIA response. © 2025 ieee.

关键词： Continuous time systems

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Efficient Design and Functional Testing of a 32-Bit RISC Processor in Verilog

Efficient Design and Functional Testing of a 32-Bit RISC Pro...

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Smart systems for applications in Electrical Sciences (ICSSES), international conference on

作者： Saravanakumar C Mohanakrishnan E Manoj Kumar D Jayasri K Jobitha Lakshmi J Department of ECE SRM Valliammai Engineering College Potheri India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

This paper presents, a 32-bit Reduced Instruction Set Computer (RISC) processor has been implemented and simulated in Verilog through the focus on cost, simplicity and scalability. The architecture of its processor grants a lean, yet strong instruction set that supports very simple activities with maximum minimal overhead. An efficient Arithmetic Logic Unit (ALU) is central to its operation and is used to seamlessly execute basic arithmetic and logical operations that ideally fit the instruction flow. The structure of Verilog simulator is used to simulate, and verify the processor using Verilog, and simulations have been performed to prove the correctness, precision and speed of calculations by the processor. The processor executes instructions with predictable high consistency and low latency and can therefore be applied to many applications. This implementation satisfies architectural optimization while providing the ability to be further optimized. In addition to the present form, this processor makes for a good foundation for future development. Some of them are instruction set extension, pipelining and raise in data throughput, as the processor becomes capable of more advanced computational processes. This work contributes to the growing field of processor design. The design is optimal for use in embedded systems, real time processing, and learning, and has a stable learning platform for development and learning in processor design with its scalable and efficient nature. This work provides an addition to the emerging field of RISC computing through the introduction of a low cost and flexible processor architecture and general insight into future research and development for processors.

关键词： Reduced instruction set computing embedded systems Costs Process control Computer architecture Throughput real-time systems Hardware design languages Arithmetic Testing

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Optimization of real-time Dynamic Gesture Recognition Based on Multi-Scale Spatio-Temporal Residual Graph Convolutional Neural Networks

Optimization of Real-Time Dynamic Gesture Recognition Based ...

引用

Power Electronics, Computer applications (ICPECA), ieee international conference on

作者： Meixiu Zhou Hanying Chen Shancheng Zhao Xia Wu Department of Electronic Engineering Jinan University Guangzhou China Computing Center Jinan University Guangzhou China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

The challenge of real-time dynamic gesture recognition is how to accurately locate the gestures in the video and take into account the real-time and accuracy in the recognition process. Aiming at the problem of dynamic gesture recognition in video, this paper proposes a dynamic gesture recognition method based on the multi-scale spatio-temporal residual graph convolution. The convolution module based on spatio-temporal residual graph is used to capture the motion information at different time and spatial scales, expand the receptive field of spatio-temporal dimension, and use the residual structure system to process hierarchically. The dynamic gesture recognition algorithm is designed by using the characteristics of time series, gesture category, gesture coordinates and gesture area to complete the dynamic gesture recognition. The gesture recognition performance of the model is tested under simple and complex background. Under the same level model, the performance of this model has certain advantages.

关键词： Accuracy Convolution Heuristic algorithms Dynamics time series analysis Gesture recognition Streaming media real-time systems Power electronics Convolutional neural networks

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Quantum Dot-Based real-time Disease Detection Using embedded systems: A Novel Approach for Portable Health Monitoring

Quantum Dot-Based Real-Time Disease Detection Using Embedded...

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international conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech)

作者： Soumil Sarna Mukesh K Sarna School of Electronics Engineering (SENSE) Vellore Institute of Technology Vellore India Department of General Medicine Mahatma Gandhi Medical College and Hospital MGUMST Jaipur

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

This paper presents an innovative approach to real-time disease diagnostics using a microcontroller integrated quantum dot sensor device. Quantum dots, renowned for their high sensitivity and precise fluorescence properties, are deployed to detect specific biomarkers in biological samples, offering rapid and accurate health diagnostics. This work lays the foundation for future advancements in embedded systems for biomedical applications, offering an efficient and low-cost solution to improve patient outcomes through timely intervention.

关键词： embedded systems Accuracy Sensitivity Microcontrollers Quantum dots Point of care Biomarkers Fluorescence real-time systems Human immunodeficiency virus

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