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检索条件"任意字段=2023 3rd International Conference on Detection Technology and Intelligence System, DTIS 2023"

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Innovations in Voice Navigation Using Object detection to Empower the Visually Impaired

Innovations in Voice Navigation Using Object Detection to Em...

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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： Ranjeet Suryawanshi Annepu Arudra Dankan Gowda V Mathu Uthaman Swathi Pai M VK Satya Prasad Department of Electronics and Telecommunications Engineering Bharati Vidyapeeth's college of Engineering Kolhapur Maharashtra India Department of Computer Science & Engineering Rajiv Gandhi Institute of Technology R T Nagar Cholanagar Bangalore Karnataka India Department of Electronics & Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka India Department of Computer Science and Engineering Mahaguru Institute of Technology Kattachira Kayamkulam Alappuzha India Department of Artificial intelligence and machine learning NMAM Institute of Technology Nitte Karnataka India Symbiosis Institute of Business Management Hyderabad Symbiosis International (Deemed University) Pune India

The project focuses on addressing the challenges faced by visually impaired individuals in navigating environments, especially when encountering obstacles or misplacing essential tools like walking sticks. A novel solution is proposed: a voice-controlled smart stick, designed to enhance the autonomy of visually impaired people in their daily activities without reliance on others. This research introduces a sophisticated walking stick equipped with ultrasonic sensors, operated via voice commands for ease of use and accessibility The voice-controlled smart stick is distinguished by its user-friendly interface, rapid responsiveness, minimal power consumption, lightweight design, and ease of folding and handling. This project not only offers a practical solution for the visually impaired but also signifies a significant advancement in assistive technology, enhancing independence and safety in daily life.

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Improved the detection Algorithm of Safety Helmet Wearing Based on YOLOv8

Improved the Detection Algorithm of Safety Helmet Wearing Ba...

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Electronic Information Engineering and Computer Communication (EIECC), international conference on

作者： Luyao Chen Yihai Mao Hongyi Zhang Shen Luan School of Opto-Electronic and Communication Engineering Xiamen University of Technology Xiamen China

To settle challenges with low detection accuracy of small and strong objects, as well as complicated settings in practical construction scenarios like tunnels, coal mining operations, and building locations, an improved YOLOv8 safety helmet wearing detection algorithm has been established. To begin, the transformer part is the central component of the system. It uses the contextual knowledge stored in the feature diagram to help the model learn on its own and improve the linking of global feature information. Additionally, the bidirectional feature pyramid network (BiFPN) is employed to integrate characteristic data across various dimensions. Finally, important nodes in the network have the 3D weighted attention mechanism SimAM installed. This gets rid of unnecessary background details and focuses more on the unique features of the hard helmet. The experimental findings reveal that on the SHWD dataset, the revised algorithm's mAP (IOU = 0.5) increased by 3.8% to 95%, and the detection speed reached 127 FPS. In crowded, complex, and tiny object instances, detection increased significantly. It meets precision and real-time safety helmet detection standards and introduces a new solution for challenging construction situations.

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Islanding detection of Integrated Wind Energy system with Variations in DC Link Voltage

Islanding Detection of Integrated Wind Energy System with Va...

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Sustainable Energy and Future Electric Transportation (SEFET), international conference on

作者： Sareddy Venkata Rami Reddy T. R. Premila Ch. Rami Reddy Electrical and Electronics Engineering Vels Institute of Science Technology & Advanced Studies Chennai India Electrical and Electronics Engineering Joginpally B R Engineering College Hyderabad India

Renewable energy integration into the grid is growing in everyday life for meeting the universal energy consumption demand. The main difficulty with the integration of Distributed Generation to the main grid is unintentional islanding. It may affect the field persons and equipment involved with it. As per the DG integration criteria, the islanding must be recognized in less than 2 seconds. This paper has, a new passive islanding detection method has been recommended for detecting unintentional islanding using variations in the DC link voltage. The planned technique detects and classifies the non-islanding cases from islanding events when the changes in DC link voltage are more than a predefined threshold value. The simulation outcomes in Matlab/Simulink show the effectiveness of the recommended method at zero power mismatch balanced islanding.

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Brain Tumor Segmentation and Classification in MRI Images Using Deep Learning Techniques

Brain Tumor Segmentation and Classification in MRI Images Us...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： R Archana Reddy Hassan M. Al-Jawahry Ghazi Mohamad Ramadan Mohammed Ayad Alkhafaji B. Buvaneswari School of Computer Science and Artificial Intelligence SR University Warangal Telangana India The Islamic university Najaf Iraq College of MLT Ahl Al Bayt University Karbala Iraq National University of Science and Technology Dhi Qar Iraq Department of Information Technology Panimalar Engineering College Chennai India

The brain tumors are formed when groups of abnormal cells develop in the brain and possess the capacity to infiltrate nearby tissues. The early detection is necessary to aid doctors in treating cancer patients to increase their survival rate. The BraTS-2019 and BraTS-2020 dataset is utilized in this research for the segmentation and detection of brain tumor. Min-max normalization is used in this experiment for data preprocessing and fed to Kernel based Fuzzy C-means clustering (KFCM) which minimizes the computational time during segmentation process. Then, the Gray Level Cooccurrence Matrix (GLCM) is utilized for effective feature extraction and Convolutional Neural Network (CNN) is utilized for classification of tumors. The proposed KFCM-CNN performance is estimated by utilizing accuracy, precision, recall, specificity and f1-score. The obtained result demonstrations that the KFCM-CNN model achieves better accuracy of 99.62% on BraTS-2019 and 99.58% on BraTS-2020 dataset which ensures accurate classification compared to other existing techniques like ensemble and U-net model.

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Sound Event detection Using Multi-Scale Dense Convolutional Recurrent Neural Network with Lightweight Attention

Sound Event Detection Using Multi-Scale Dense Convolutional ...

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Electronic Information Engineering and Computer technology (EIECT), international conference on

作者： Hanyue Yang Liyan Luo Mei Wang Xiyu Song Fukun Mi School of Information and Communication Guilin University of Electronic Technology Guilin China School of Information Science & Engineering Guilin China

Neural network based sound event detection has attracted considerable attention due to high detection accuracy. Existing sound event detection algorithms often improve performance by constructing deeper and more complex detection models. To address the issue of high computational complexity in existing high performance sound event detection algorithms, this paper proposes a sound event detection algorithm using multiscale dense convolutional recurrent neural network with lightweight attention (MS-AttDenseNet-RNN). It utilizes multiscale dense convolutional neural network with lightweight attention (MS-AttDenseNet) to capture information from different scales of sound signal receptive fields and then generates importance-enhanced multi-scale feature. Finally, recurrent neural network (RNN) is used to model the temporal dependency of the enhanced multi-scale features. Compared to the system model that won the DCASE challenge, this method achieves a 18.9% improvement in F1 score and a 4.0% reduction in error rate, while reducing the number of parameters by 75.8% and the computational complexity by 47.5%. This demonstrates that the proposed method can achieve lower parameter count and computational complexity while maintaining accuracy.

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Coronary Artery Disease (CAD) Heart Beats Classification Using Recurrence Plots

Coronary Artery Disease (CAD) Heart Beats Classification Usi...

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international Symposium on Artificial intelligence and Signal Processing (AISP)

作者： Usha Desai Roshan J. Martis Ravichander Janapati Kandala N V P S Rajesh Department of Electronics and Communication Engineering SR University Warangal Telangana India Computer Science Artificial Intelligence Global Academy of Technology Bengaluru Karnataka India School of Electronics Engineering VIT-AP University Vijayawada Andhra Pradesh India

Coronary Artery Disease (CAD) is the world’s leading heart disease. The main reason for CAD is fat accumulation in coronary arteries, during which the myocardium lacks the supply of blood and oxygen. Stringent blocks of the arteries may lead to heart attack or stroke. Electrocardiogram (ECG) signal is a standard diagnostic test to identify cardiac abnormalities. The nonlinearities of the ECG can be quantified using Recurrence Quantification Analysis (RQA) structures. This work discriminates Normal Sinus Rhythm (NSR) and CAD using Recurrence Plots (RPs).Further, the RQA parameters are extracted for categorizing NSR and CAD using Decision Tree (DT) and ensemble learning models. The essential (p\lt 0.05) features are fed to these ensemble models: Random and Rotation Forests (RAF and ROF), Bagging (BGG), and Adaboost (ADB) to select an appropriate classifier. Among them, ROF yielded the highest overall accuracy of 99.42%. The proposed system is cost-efficient and can be applied to discriminate the NSR and CAD heartbeats.

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3rd international conference on Intelligent systems and Pattern Recognition, ISPR 2023

3rd International Conference on Intelligent Systems and Patt...

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3rd international conference on Intelligent systems and Pattern Recognition, ISPR 2023

ISBN: (纸本)9783031463372

The proceedings contain 44 papers. The special focus in this conference is on Intelligent systems and Pattern Recognition. The topics include: Staged Reinforcement Learning for Complex Tasks Through Decomposed Environments;policy Generation from Latent Embeddings for Reinforcement Learning;deep Learning Models for Aspect-Based Sentiment Analysis Task: A Survey Paper;A Real-Time Deep UAV detection Framework Based on a YOLOv8 Perception Module;a Deep Neural Architecture Search Net-Based Wearable Object Classification system for the Visually Impaired;Multicarrier Waveforms Classification with LDA and CNN for 5G;Weeds detection Using Mask R-CNN and Yolov5;Boruta-AttLSTM: A Novel Deep Learning Architecture for Soil Moisture Prediction;graph Autoencoder with Community Neighborhood Network;Study of Support Set Generation Techniques in LAD for Intrusion detection;question-Aware Deep Learning Model for Arabic Machine Reading Comprehension;a Hybrid Deep Learning Scheme for Intrusion detection in the Internet of Things;identifying Discourse Markers in French Spoken Corpora: Using Machine Learning and Rule-Based Approaches;A Comparative Study of the Impact of Different First Order Optimizers on the Learning Process of UNet for Change detection Task;Minimal Window Duration for Identifying Cognitive Decline Using Movement-Related Versus Rest-State EEG;modeling Graphene Extraction Process Using Generative Diffusion Models;bird Species Recognition in Soundscapes with Self-supervised Pre-training;on the Different Concepts and Taxonomies of eXplainable Artificial intelligence;Classifying Alzheimer Disease Using Resting State Coefficient of Variance BOLD Signals;proteus Based Automatic Irrigation system;How AI can Advance Model Driven Engineering Method ?.

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Multi-class multi-object detection and tracking system in 3 dimensions using YOLOv5

Multi-class multi-object detection and tracking system in 3 ...

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Range technology (ICORT), international conference on

作者： Aditi Bhateja Sanyam Sharma Abhishek Krishna Himanshu Chaudhary Brejesh Lall Prem K Kalra Computer Science Indian Institute of Technology Delhi India Software Engineering Delhi Technological University Delhi India Information Technology Delhi Technological University Delhi India Securities and Exchange Board of India India Electrical Engineering Indian Institute of Technology Delhi India

It is of great significance to detect and track multiple objects belonging to multiple classes in 3 dimensions and the same can be applied to various research domains. One such application pertains to multi-fish detection and tracking The problem is of significance to marine researchers who want to study the behaviour of fish in a constrained environment i.e. in an aquarium, using different sensors. For this study, a data set consisting of different fishes in an aquarium was constructed and Microsoft Kinect was used to capture the movements in 3 dimensions. The data set was cleaned, aligned, and calibration was done to make it suitable for object detection and tracking In this paper, YOLOv5s model was improvised, hereafter referred as YOLOv5-With Attention Mechanism (YOLOv5-WAM), and an object detection and tracking pipeline was designed to detect and track underwater objects (fishes in our work) belonging to multiple classes in 3D. YOLOv5-WAM can be adapted for use on devices having low compute availability, making it suitable for real time object detection and tracking using low cost experimental setup. YOLOv5-WAM showed about 1% improvement in mAP for our custom dataset while for Fish4Knowkdge and COCO dataset, the improvements were to the tune of 7% when compared to YOLOv5s. The results and analysis of the approach are presented in this paper.

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Application of Over-Sampling Techniques and Fuzzy ARTMAP to Condition Monitoring of a Power Generation system

Application of Over-Sampling Techniques and Fuzzy ARTMAP to ...

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Power Engineering Application (ICPEA), IEEE international conference in

作者： Timothy Zhi Wei Chang Shing Chiang Tan Kok Swee Sim Chee Peng Lim Pey Yun Goh Faculty of Engineering and Technology Multimedia University Melaka Malaysia Faculty of Information Science and Technology Multimedia University Melaka Malaysia Institute for Intelligent Systems Research and Innovation Deakin University Victoria Australia

Condition monitoring is a process of assessing the health status of a system, process, or machine. Monitoring and identifying any potential fault can be conducted by leveraging measurements from the installed sensors that provide information on the state of the system. In this respect, machine learning models are useful for processing and analyzing the sensor data for fault detection. However, the imbalanced nature of these sensory data can cause misleading high accuracy scores. In this study, we employ an over-sampling method to tackle the imbalanced class problem. Specifically, both Synthetic Minority Over-sampling Technique (SMOTE) and Gaussian SMOTE are used to generate minority class samples. The balanced data set is used by the Fuzzy ARTMAP (FAM) model for fault classification. The effectiveness of the developed method is evaluated using a real-world circulating water system in a power generation plant. The results indicate that both SMOTE variants can improve the performance of FAM in detecting faults corresponding to operating conditions of the circulating water system for efficient power generation.

关键词： Condition monitoring Fault diagnosis Power engineering Machine learning algorithms Fault detection Data models Sensor systems

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Navigating the Unseen: Proposing an IoT-Based Smart Shoe with Obstacle detection for the Visually Impaired

Navigating the Unseen: Proposing an IoT-Based Smart Shoe wit...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Kavita Arora Sonal Pathak Suhail Javed Quraishi Anupam Singh Malikhan Singh Danish Ather School of Computer Applications Manav Rachna International Institute of Research and Studies Faridabad Haryana Department of Computer Science and Engineering Graphic era hill University Dehradun BSA College of Engineering & Technology Mathura Amity University in Tashkent Uzbekistan

Navigating daily life remains a substantial challenge for the visually impaired, despite technological advancements. This paper presents an IoT-based Smart Shoe equipped with ultrasonic sensors, accelerometers, and gyroscopes to detect obstacles, providing real-time feedback to enhance navigation. The system communicates with a mobile application to offer auditory or haptic alerts, focusing on reliability, comfort, and affordability. Tested across varied terrains and lighting conditions, the prototype showed strong performance and accuracy. Visually impaired participants in trials reported increased mobility and independence. The paper also explores potential integration with navigation tools such as GPS and ethical considerations like user privacy. “Navigating the Unseen” showcases the potential of technology in creating a more inclusive future for the visually impaired, emphasizing human centered design.

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