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检索条件"机构=dept. of Computer Computer Systems Engineering"

共 66490 条记录，以下是4961-4970 订阅

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You Only Look Once Version 8 (YOLOv8)-Driven Emergency Vehicle Detection and Graph Neural Networks (GNNs) based Traffic Signal Prioritization

You Only Look Once Version 8 (YOLOv8)-Driven Emergency Vehic...

引用

Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： B. Shyamala Gowri Harini Harisitha S Abishek Kanna M Amirthavarshini N S Dept. of Computer Science & Engineering Easwari Engineering College TN India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

The research develops a traffic management system that integrates You Only Look Once version 8 (YOLOv8) for emergency vehicle detection and Graph Neural Network (GRAPH NEURAL NETWORKS (GNNs))-based traffic signal prioritization to address the inefficiencies of traditional traffic management systems. These systems often fail to detect emergency vehicles accurately and adapt traffic signals in real-time, leading to delayed emergency responses and traffic congestion. The proposed system leverages You Only Look Once version 8 (YOLOv8)'s superior object detection capabilities and GRAPH NEURAL NETWORKS (GNNs)-based reinforcement learning to dynamically adjust traffic signals based on real-time road conditions. Experimental results demonstrate a 40% reduction in emergency vehicle response time and a 35% improvement in traffic flow, showcasing the system's ability to enhance urban traffic performance and emergency response efficiency.

关键词： YOLO Smart cities Vehicle detection Reinforcement learning Traffic control Emergency services Real-time systems Graph neural networks Time factors Traffic congestion

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Alzheimers Disease Detection through LASSO and RFE based Feature Selection from EEG Data

Alzheimers Disease Detection through LASSO and RFE based Fea...

引用

IEEE International Conference on Biomedical engineering, computer and Information Technology for Health

作者： Zahrul Jannat Peya Nurzahan Akter Joly Md. Sharzul Mostafa Sharmina Al-Azad Md. Shymon Islam Sk. Ahadul Alam Dept. of Computer Science and Engineering North Western University Khulna Bangladesh Dept. of Computer Science and Engineering Khulna University of Engineering & Technology Khulna Bangladesh

ISBN: (数字)9798331534356

ISBN: (纸本)9798331534363

Alzheimer’s disease is a progressive neurological condition that affects memory, cognition, and basic daily functions, with symptoms typically appearing later in life. This study aims to develop a reliable, non-invasive method for early detection of Alzheimer’s using electroencephalogram (EEG) data. Given the absence of a cure, precise diagnostic tools are crucial. EEG is an affordable and non-invasive alternative, particularly beneficial for disadvantaged populations. Our approach focuses on improving early diagnosis, monitoring disease progression, and providing individualized care. Using Recursive Feature Elimination and Lasso for feature selection, we classified EEG data with machine learning algorithms (Random Forest, Support Vector Machine, and Decision Tree), with Random Forest achieving the highest accuracy of 95.45%.

关键词： Support vector machines Accuracy Machine learning algorithms Feature extraction Electroencephalography Decision trees Reliability Alzheimer's disease Random forests Monitoring

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Gammatone Visualization based Cough Sound Classification: Performance Comparison with Delta and Delta-Delta Features 2

Gammatone Visualization based Cough Sound Classification: Pe...

引用

2nd International Conference on Smart Technologies and systems for Next Generation Computing, ICSTSN 2023

作者： Priya, B.Lakshmi Jayalakshmy, S. Saraswath, D. Vellore Institute of Technology School of Computer Science and Engineering Chennai Campus Chennai India IFET College of Engineering Dept. of Electronics and Communication Engineering Villupuram India

ISBN: (纸本)9798350348002

Cough being a common symptom for most respiratory disease is considered as a predictor in the diagnosis of the diseases. In recent years, time frequency representations of signals are acclaimed for its efficacy in the classification of signals. This work explores the potential of time frequency representation derived from gammatone features in the classification of cough signals. Accordingly, visualization of gammatone cepstral coefficients (GTCC) and its delta and delta-delta variants are employed for classifying cough signals using GoogLeNet, a prominent pre-trained CNN architecture. The results of this study evinces that the delta-delta variant of GTCC with a classification accuracy of 98.02% has significantly outperformed GTCC and its delta variant which recorded accuracies of 97.22% and 94.44% respectively. © 2023 IEEE.

关键词： Visualization

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Leveraging a Hierarchical Selection of Communities in Social Networks to Maximize Diffusion 9

Leveraging a Hierarchical Selection of Communities in Social...

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9th International Conference on Web Research, ICWR 2023

作者： Heidari, Mohsen Fasahat, Mohamad Ali Afsharian, Sanaz Artishdar, Parinaz Esfarayen University Dept. of Electrical Computer and Industrial Engineering North Khorasan Iran Mehr Alborz Higher Education Institute Dept. of Computer Science Tehran Iran Islamic Azad University Damavand Branch English Department Tehran Iran Islamic Azad University Yadegar Emam Khomeini Branch Dept. of Software Engineering Tehran Iran

ISBN: (纸本)9798350399691

For businesses, discovering key nodes in social networks has a high computational overhead and cost. Because of this, in the problem of influence maximization, at least key nodes with the most influence in social networks are chosen at the lowest possible cost. Numerous algorithms have been proposed to find key nodes in social networks using the technique of influence maximization under the characteristics of submodularity and monotonicity;however, these algorithms struggle with the issue of optimal influence spread because some graph characteristics are ignored when considering diffusion and influence spread in the social network. The HSMD method, which gives special weight to the structure of communities and selects key nodes hierarchically utilizing the structure of communities, has been presented as a solution to this challenge. Based on the characteristics and strategic placement of the nodes within each community, weight is given to each community in this algorithm. Seed nodes are then chosen hierarchically using BFS traversal. This method enhanced the influence spread rate and execution time of recently introduced algorithms such as CTIM, IMBC, RNR, HEDVGreedy, and k-shell. © 2023 IEEE.

关键词： Diffusion

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A Machine Learning Approach for Predicting Personality Types from Text Patterns Using Natural Language Processing(NLP)

A Machine Learning Approach for Predicting Personality Types...

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International Conference on Electronics, Materials engineering and Nano-Technology (IEMENTech)

作者： Kinshuk Ganguly Subhajit Saha Anupam Mondal Monalisa Dey Dept. of Computer Science & Engineering Institute of Engineering & Management Kolkata India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

This study explores the use of Natural Language Processing (NLP) approach to predict Myers-Briggs Type Indicator (MBTI) personality types from Twitter texts. Initially, we approached the problem as a 16-class classification task, but encountered significant challenges due to the lack of instances representing certain class labels adequately. To address this, we decomposed the MBTI types into four dimensions and trained individual classifiers for each dimension. Leveraging the best performing classifiers, we constructed a stacking ensemble model to predict personality classes corresponding to each dimension. The results demonstrate improved accuracy compared to the initial 16-class classification approach. Additionally, the performance of ensemble model is found to be better than the individual classifiers. Thus, ensemble approach paired with the separate dimension based classification method can be used for effective prediction of personality types for psychological profiling.

关键词： Support vector machines Training Logistic regression Accuracy Social networking (online) Stacking Blogs Predictive models Natural language processing Robustness

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Speech-based Dementia Prediction using Artificial Intelligence

Speech-based Dementia Prediction using Artificial Intelligen...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Vivekanand, Chettiyar Vani Nayagam, Shamini Rajeev, Sreena Rajalakshmi Engineering College Dept. Computer Science Engineering Chennai India

ISBN: (纸本)9798331521394

The global healthcare, social, and economic impacts that affect the patient, family, and society as a whole are significantly affected by dementia. This study's objective is to look at how the analysis of speech patterns might support the early diagnosis of dementia. We will create and assess machine learning models to categorize people with and without dementia using acoustic characteristics taken from voice data. In order to determine the best practices for dementia detection, the study will mix a number of conventional machine learning techniques. The goal of using speech data is to help identify the condition early, which can save a lot of money and allow for prompt interventions to stop its progression. Early detection is vital because it makes room for therapeutic methods that work and protect. It looks at important elements such as methods for collecting data, pre-processing strategies, features classifiers, and extraction techniques. The extract of acoustic features in Python modules such as Librosa was utilized for the analysis of speech data. The Praat software's Python interface, Parselmouth, utilized for OpenSMILE, a toolkit for phonetic analysis, and obtaining a variety of acoustic characteristics that are utilized deep acoustic embedding to efficiently record and reflecting essential aspects of speech that are intimately associated with cognitive deficits. The findings of this investigation could help the creation of accessible and noninvasive dementia-related screening tools. © 2025 IEEE.

关键词： Deep learning

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IoT-Enabled Smart Shoes for Visually Impaired Navigation and Safety

IoT-Enabled Smart Shoes for Visually Impaired Navigation and...

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Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： Vavilapalli Joyce Ashamgari Saipriya Akki Karthik M. Lakshmi Prasad Dept. of Computer Science And Engineering Institute of Aeronautical Engineering Hyderabad India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Navigating safely and independently is a significant challenge for visually impaired individuals due to their inability to detect obstacles, uneven terrain, and other environmental hazards. Traditional mobility aids such as white canes provide limited assistance, and guide dogs are costly and require extensive training. To overcome these limitations, this research introduces an IoT-enabled smart shoe designed to enhance mobility and safety. The proposed system utilizes ultrasonic and infrared sensors to detect obstacles in real time, an Arduino Nano microcontroller for processing sensor data, and a Bluetooth module to transmit alerts to a connected mobile application. The system provides audio feedback through earphones, allowing users to receive real-time verbal alerts for safe navigation. Experimental evaluation demonstrates that the smart shoe achieves high accuracy in obstacle detection (96%), offers stable and reliable real-time feedback, and is designed for ease of use. A comparison with existing assistive technologies highlights the system’s effectiveness in improving independent mobility and reducing collision risks. Future developments may include AI-based navigation enhancements, extended battery life, and advanced sensor fusion for better adaptability in complex environments.

关键词： Bluetooth Accuracy Navigation Footwear Dogs Assistive technologies Infrared sensors Real-time systems Batteries Fall detection

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Advanced Decision-Making for Autonomous Vehicles Utilizing Deep Q Networks

Advanced Decision-Making for Autonomous Vehicles Utilizing D...

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2025 International Conference on Emerging Technologies for Intelligent systems, ETIS 2025

作者： Britto, Akash Ij Abhishek, S. Dhanush, S. Salim, Soja Soniya, B. Sct College of Engineering Dept. of Computer Science Engineering Thiruvananthapuram India

ISBN: (纸本)9798331507541

Autonomous vehicles are poised to revolutionize the transportation industry by offering safer and more efficient navigation in dynamic environments. A critical challenge is managing interactions with other vehicles, pedestrians, and road obstacles, which complicates framing autonomous driving as a supervised learning problem. This study leverages Deep Q-Networks (DQN), a reinforcement learning technique, to control steering, throttle, and braking in autonomous vehicles. The DQN approach enables vehicles to autonomously learn and improve driving strategies, thereby enhancing their ability to navigate complex traffic scenarios effectively. Our objective is to train a model using a Deep Reinforcement Learning (DRL) algorithm, guiding the vehicle to adhere to a predetermined itinerary while adapting to real-time traffic conditions. This work emphasizes the potential of reinforcement learning for enabling continuous learning and adaptation in complex traffic scenarios, laying the groundwork for more reliable and adaptive autonomous driving systems. © 2025 IEEE.

关键词： Deep reinforcement learning

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Integrating SHAP, LIME and Grad-CAM with CNNs for Transparent Kidney Condition Classification

Integrating SHAP, LIME and Grad-CAM with CNNs for Transparen...

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International Conference on computer and Information Technology (ICCIT)

作者： Md. Ismiel Hossen Abir Hasibul Islam Peyal Sumya Rashid Md Jahid Syed Mustafizur Rahman Chowdhury Md. Nazrul Islam Mondal Dept. of Computer Science & Engineering International Standard University Dhaka Bangladesh Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

This research introduces a method for diagnosing kidney conditions by combining Convolutional Neural Networks (CNNs) with cutting-edge explainable AI techniques. The study aims to classify CT kidney images into four categories: Normal, Cyst, Tumor, and Stone. The proposed lightweight CNN model demonstrates high performance with validation and test accuracy of 92.71% and 92.12%, respectively, and a training accuracy of 97.92%. Precision, recall, and F1-score are 0.92%, 0.86%, and 0.88%, respectively, with an outstanding AUC of 0.99%. To improve the clarity of the model's decision-making process, SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and Grad-CAM (Gradient-weighted Class Activation Mapping) were used. LIME provided insights into the most influential image regions, SHAP values explained feature contributions to predictions, and Grad-CAM visualizations highlighted critical areas of images impacting classification. The integration of these explainable AI methods not only improves diagnostic Kidney condition accuracy but also fosters trust in the model prediction and makes medical image analysis.

关键词： Training Visualization Accuracy Explainable AI Decision making Predictive models Convolutional neural networks Kidney Medical diagnostic imaging Tumors

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Challenges in Working with Unstructured Data in the LLM Era: NER Processes Using Graph Neural Networks 4

Challenges in Working with Unstructured Data in the LLM Era:...

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4th International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2024

作者： Bouquet, Paolo Molinari, Andrea Sandri, Simone University of Trento Department of Information Engineering and Computer Science Trento Italy University of Trento Dept. of Industrial Engineering Trento Italy Okkam S.r.l. Trento Italy

ISBN: (纸本)9798350391183

The application of neural networks and large language models (LLMs) in the financial technology (fintech) sector has significantly enhanced capabilities of providing better services based on unstructured documents, mainly thanks to the astonishing interactive capabilities of large language models (LLMs). However, in these contexts, precisely recognizing named entities (for example, the beneficiary of an insurance policy) and creating reliable, domain-dependent, private knowledge graphs is more crucial than conversational capabilities. Moreover, optimizing these models for dynamic data environments and ensuring Explainability presents notable challenges. This paper aims to present our approach for the creation of a knowledge graph that uses graph neural nets (GNN) to provide a better named-entity recognition (NER) process, to identify entities in the text precisely and to solve other issues in knowledge graph management, like the co-ference resolution problem or focusing on dynamic data environments and the critical need for Explainability of modern AI-based solutions. The paper presents our preliminary results on one of the various pipeline steps that creates the navigable knowledge graph. © 2024 IEEE.

关键词： Knowledge graph

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