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检索条件"任意字段=12th International Conference on Intelligent Systems and Embedded Design, ISED 2024"

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2024 12th international conference on intelligent systems and embedded design, ised 2024

2024 12th International Conference on Intelligent Systems an...

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12th international conference on intelligent systems and embedded design, ised 2024

ISBN: (纸本)9798331522612

the proceedings contain 56 papers. the topics discussed include: space robotics-the influence of advanced technology;hybrid classical-quantum neural network for n-dimensional embedding;testing the effect of hardware trojans: a case study;zero area overhead based scan-chain watermarking;lane change prediction for autonomous vehicles in dynamic traffic using machine learning;energy efficient approximate ternary adder for image processing applications;towards polynomial formal verification of neuromorphic architectures;a comprehensive examination of deep learning techniques for sentiment analysis on twitter data;droplet routing in the presence of blockages: challenges in digital microfluidics biochip;a logic testing approach for hardware trojan detection using nature inspired algorithms;and automatic histopathological image classification using multi-deep features with handcrafted features and shallow machine learning models.

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In-Cabin Gesture Control with embedded ML for Airbag Activation 12

In-Cabin Gesture Control with Embedded ML for Airbag Activat...

引用

12th international conference on intelligent systems and embedded design, ised 2024

作者： Supriya, K. Vastrad, Om Rajashekhar Shree Raksha, B.M. Shanbhag, Shreya Vishnu Desai, Atharv Mutalik Rohini, H. Rajeshwari, M. KLE Technological University Dept. of Electronics and Communication Hubballi India

ISBN: (纸本)9798331522612

this paper proposes a perspective on the integration of smart technology within embedded automotive systems, em phasizing the role of machine learning and artificial intelligence in enhancing in-cabin interactions and safety mechanisms. these technologies have transformed sectors that rely on embedded systems as controllers, driving advancements in performance and efficiency while fostering groundbreaking innovations. By leveraging an array of sensors, industries are enabling sophisticated, real-time responsiveness that improves both safety and operational efficiency. Gesture sensors, for instance, detect hand movements to support contact-free interactions across various domains such as gaming, smartphone applications,and automotive interfaces, where they enhance user experience and interaction efficiency. In the automotive sector, gesture-based systems are increasingly used not only for infotainment but also to control other in-cabin functionalities, reflecting a broader trend toward intuitive, contact-free vehicle interactions. this study introduces a gesture-based control system for vehicle functions, streamlining task initiation and usability. Additionally, we examine an accident detection system integrated with airbag control, powered by embedded machine learning. this integration facilitates real time decision-making and data processing, minimizing latency, enhancing data privacy, and boosting efficiency, contributing to the evolution of intelligent, user-centric, and safer automotive environments. © 2024 IEEE.

关键词： Autonomous driving embedded ML Gesture Control In-cabin sensing application Smart technology

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A Machine Learning Based Approach for Dynamic Hand Gesture Recognition in Human-Robot Interaction 12

A Machine Learning Based Approach for Dynamic Hand Gesture R...

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12th international conference on intelligent systems and embedded design, ised 2024

作者： Kaur, Arpneek Bansal, Sandhya Maharishi Markandeshwar Engineering College Maharishi Markandeshwar Deemed to be University Department of Computer Science and Engineering Haryana Ambala Mullana133207 India

ISBN: (纸本)9798331522612

Today's modern world is equipped with many smart robotic gadgets and devices to make our day-to-day jobs easier. these robotic devices need to capture human orders using different kinds of audio-visual inputs, hand gestures being one of them. therefore, hand gesture recognition is an important step in human-robot interaction. the response of the robot directly depends on the time taken in classification of the performed gesture by the human. However, processing all the redundant frames in the visual input increases the response time of the device. In this paper, we propose a machine learning based approach to dynamic hand gesture recognition, by extracting only unique key-frames from the input visuals. To obtain them, firstly the hand skeleton landmarks are focused in order to obtain the important spatial features. the similarity in these landmark features is analyzed and used to determine the unique key-frames from each input video. the unique key-frames so decided by similarity detection are further input to SVM machine learning model to perform hand gesture classification, achieving a classification accuracy of 79.30% at 20 key-frames within a training time of 1241 sec, which is comparable to the one using all frames, taking a training time of 6455 sec. the obtained results have been further compared with regular feature extraction method using PCA, showing improved results of proposed method over PCA. © 2024 IEEE.

关键词： intelligent robots

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MBConvNet-CNS: A Lightweight Convolutional Neural Network (CNN) Architecture for Plant Disease Detection 12

MBConvNet-CNS: A Lightweight Convolutional Neural Network (C...

引用

12th international conference on intelligent systems and embedded design, ised 2024

作者： Burman, Shikhar Pandey, Nilabh Srivastava, Chetna Prusty, Manas Ranjan School of Computer Science and Engineering Vellore Institute of Technology Chennai India Vellore Institute of Technology Centre for Cyber Physical Systems Chennai India

ISBN: (纸本)9798331522612

Plant diseases pose a big threat to agricultural productivity worldwide, so their detection technique should be strong and efficient. Classically, plant disease identifications are time-consuming and usually require expert knowledge;this makes the use of large-scale agriculture difficult. the following paper presents a review of how the use of lightweight CNN models can be used for real-time plant disease detection with models that are computationally efficient enough to be deployed on edge devices such as smartphones and drones. We shall propose a novel model based on state-of-the-art architectures comprising the likes of MobileNet, EfficientNet, Ghost Modules, and Squeeze-and-Excitation blocks that achieve high accuracy for plant disease identification with a low computation footprint. the proposed model was applied to different datasets of images of plant leaves. Experimental results proved it at par to the previous heavyweight and lightweight models based on deep learning for accuracy and time consumed. these findings point out that the correct integration of lightweight CNN architectures and innovative modules shall yield a feasible solution for effective management of plant diseases, making timely interventions possible for reducing crop losses. © 2024 IEEE.

关键词： EfficientNet Image Classification Light-weight CNN MobileNet Squeeze-and-Excitation blocks

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A Hybrid Approach to Rice Leaf Disease Detection Using Transfer Learning and SVM 12

A Hybrid Approach to Rice Leaf Disease Detection Using Trans...

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12th international conference on intelligent systems and embedded design, ised 2024

作者： Pal, Raman Kumar Panigrahi, Sibarama Pratihar, Sneha National Institute of Technology Intelligent Data Analytics Laboratory Computer Science& Engineering Rourkela India Kalinga Institute of Industrial Technology Computer Science & Engineering Bhubaneshwar India

ISBN: (纸本)9798331522612

Rice is a prominent crop in India, with the country ranking second globally in rice production. However, rice cultivation is severely affected by several diseases, impacting crop yield and quality. Early detection of these diseases can significantly mitigate their adverse effects. this study focuses on detecting primary rice leaf diseases: brown spot, tungro, bacterial blight, and blast. We explore various models, including recent publications, and develop hybrid approaches employing deep features from VGG16, VGG19, and ResNet50 models, alongside handcrafted features such as Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG). these features combined with shallow machine learning classifiers like Random Forest and Support Vector Machine (SVM), are evaluated for rice leaf disease classification. Different combinations of these extracted features, with and without Principal Component Analysis (PCA), are tested using various classifiers to assess their impact on classification accuracy. the models that stack deep features using ResNet50 and handcrafted features using LBP, followed by dimensional reduction via PCA and transformation using SVM, achieved near-perfect metrics across F1 score, precision, accuracy, recall, and AUC. these results indicate strong generalization and balanced performance, outperforming methods published in recent literature. © 2024 IEEE.

关键词： CNN HOG LBP ReNet50 VGG16 VGG19

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Fusion of Efficient Transformer-CNN Hybrid Network (ETCHNet) and Adaptive Deep Clustering Optimization Network (ADCONet) for Advanced IoMT-Based Surgical Monitoring With Robotics and Artificial Intelligence 12

Fusion of Efficient Transformer-CNN Hybrid Network (ETCHNet)...

引用

12th international conference on intelligent systems and embedded design, ised 2024

作者： Valivarthi, Dharma Teja Peddi, Sreekar Narla, Swapna Kethu, Sai Sathish Natarajan, Durai Rajesh Swain, Jyotiranjan Tek Leaders TX United States Tek Yantra Inc CA United States NeuraFlash GA United States Estrada Consulting Inc CA United States School of Comuting and Information Technology REVA University Bangalore India

ISBN: (纸本)9798331522612

Traditional health care systems face the challenge of real-time data processing and decision-making, particularly in the surgery domain. Health gadgets based on advanced technologies like IoMT, blockchain, and AI-based systems are used for improving surgical precision and real-time monitoring. While these systems aim to enhance surgical monitoring accuracy, scalability, and efficiency, handling high-dimensional data and real-time analysis still present challenges. this study focuses on the development of a robust and scalable enhanced IoMT-based surgical monitoring system that improves precision. the framework enhances feature extraction, clustering, and decision-making using real-time feedback and blockchain for secure data storage. the proposed framework integrates ETCHNet and ADCONet with robotic automation. the first algorithm, ADCONet, clusters dynamically and builds decisions based on optimised approaches and ETCHNet extracts hybrid features using transformers and CNNs. Robotic automation provides real-time feedback and a fluid surgical interaction. the proposed method achieved 96% classification accuracy and 95% clustering accuracy, which exceeded the existing methods. It was so effective that the method detected 94.5% of anomalies and prevented 97% of collisions. Even more impressively, it did so with an inference latency of only 120 milliseconds. As a result, the solution is still ideal upon surgical real time monitoring where split second decisions are needed. these impressive results clearly prove that the system improves healthcare accuracy and effectiveness. © 2024 IEEE.

关键词： ADCONet Blockchain Data Clustering ETCHNet Healthcare systems IoMT Machine Learning Real-Time Feedback Robotic Automation Surgical Monitoring

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Automatic Histopathological Image Classification using Multi-Deep Features with Handcrafted Features and Shallow Machine Learning Models 12

Automatic Histopathological Image Classification using Multi...

引用

12th international conference on intelligent systems and embedded design, ised 2024

作者： Banerjee, Sunrit Panigrahi, Sibarama National Institute Of Technology Rourkela Intelligent Data Analytics Laboratory Department Of Computer Science and Engineering Odisha India

ISBN: (纸本)9798331522612

Invasive ductal carcinoma (IDC) is one of the most common variant of breast cancer affecting women more frequently than any other cancer. One crucial clinical duty is to correctly detect and classify breast cancer subtypes. Automated techniques can help to reduce errors and save time. However, determining the malignancy from tissue biopsies is difficult and dependent on the subjectivity of the observer. To diagnose breast cancer, a novel classification model using fusion of deep features VGG16 and VGG19 with handcrafted feature GLCM alongside the Medium Gaussian SVM classifier is developed in this paper. Additionally, thirteen hybrid classifiers are used to study the classification of breast cancer. these classifiers use four deep features, three handcrafted features, and a further fusion of deep features to produce six more combinations. Resnet50, VGG16, VGG19 and GoogleNet (Inception v3) are the pre-trained networks used to extract the deep features. Similarly, the handcrafted feature extraction techniques used are HOG, LBP and GLCM. Following evaluation, the classification model is framed by considering the best-performing deep features (VGG16 and VGG19 fusion model), handcrafted feature (GLCM) and classifier (Medium Gaussian SVM). Area Under Curve (AUC) of 0.91, Accuracy of 87.8% and Positive Likelihood Ratio (LR+) of 12.2 were attained by the Medium Gaussian SVM based classification model that combined the features of GLCM with the fusion model of VGG16 and VGG19. According to STARD guidelines, the recommended diagnostic technique is Excellent (AUC is between 0.9 and 1.0 and LR+ is above 10.0). the achieved results also outperformed the recently published articles on the same domain tackling similar problems. © 2024 IEEE.

关键词： Deep and Handcrafted Features Feature Fusion Histopathology Images Invasive Ductal Carcinoma (IDC) Diagnosis Shallow Machine Learning Models

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Enhancing Fashion design with Conditional Generative Adversarial Networks: A CGAN Approach Using the Fashion-MNIST Dataset 12

Enhancing Fashion Design with Conditional Generative Adversa...

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12th international conference on Internet of Everything, Microwave, embedded, Communication and Networks, IEMECON 2024

作者： Kumar, Mukul Sharma, Arpit Kumar Rathore, Pramod Singh Department of IoT and Intelligent Systems Manipal University Jaipur Rajasthan Jaipur India Department of Computer and Communcation Engineering Manipal University Jaipur India

ISBN: (纸本)9798350387315

this research delves into the promising realm of Conditional Generative Adversarial Networks (CGANs) to explore their potential for crafting unique fashion item images. Leveraging the robust capabilities of the TensorFlow framework, along with essential tools such as NumPy and Matplotlib, we embark on a journey of model development and visualization. Our canvas of choice is the Fashion-MNIST dataset, meticulously curated to provide a rich tapestry for our CGAN to glean insights into the intricate nuances of various clothing articles. throughout this paper, we peel back the layers of the CGAN architecture, unraveling its inner mechanisms, elaborating on the challenging yet gratifying training process, and meticulously evaluating the quality of the images it generates. © 2024 IEEE.

关键词： Generative adversarial networks

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SecureV2X: Overview of Secure Cellular based Vehicle-to-Everything Communication in intelligent Transportation System 12

SecureV2X: Overview of Secure Cellular based Vehicle-to-Ever...

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12th international conference on Internet of Everything, Microwave, embedded, Communication and Networks, IEMECON 2024

作者： Sharma, Giriraj Dept. of Electronics & communication Malaviya National Institute of Technology Jaipur India

ISBN: (纸本)9798350387315

Advanced vehicles are increasingly incorporated into intelligent Transportation systems (ITS) to improve road safety, traffic efficiency, and driver comfort. these vehicles utilize wireless communication technologies, including Vehicle-to-Vehicle (V2V), Vehicle-to-Pedestrian (V2P), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Network (V2N), to share information. While V2X offers substantial benefits, it also introduces significant security and privacy risks. this paper examines the advantages of Cellular Vehicle-to-Everything (C-V2X) technology in facilitating communication between vehicles, infrastructure, and networks, as well as addressing related security challenges. © 2024 IEEE.

关键词： Mobile telecommunication systems

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2024 12th international conference on intelligent Computing and Wireless Optical Communications, ICWOC 2024

2024 12th International Conference on Intelligent Computing ...

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12th international conference on intelligent Computing and Wireless Optical Communications, ICWOC 2024

ISBN: (纸本)9798350374377

the proceedings contain 22 papers. the topics discussed include: SDN-based optimized controller deployment strategy for satellite network;multi-UAV enabled maritime relay and edge-computing service migration;a TDoA-based single-LED VLP system assisted by circular photodiodes array receiver;research on parabolic cores and high refractive index ring assisted fibers;FUFQ: a heterogeneous-based full-amplitude quantum simulator;design of network topology control algorithm for multi-agent systems;aerial reconfigurable intelligent surface-assisted secrecy energy-efficient communication based on deep reinforcement learning;conceptual design to achieve stable communication performance in OWC systems with dimming control;and an improved label propagation algorithm for undirected weighted networks.

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