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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Debnath, Jeet Praveen Joe, I.R. School Of Computer Science And Engineering Vellore Institute Of Technology Chennai India

ISBN: (纸本)9798350364828

Sign language is a vital mode of communication for the deaf and dumb community. This research presents a robust and real-time Gesture-Based Sign Language Detection System that leverages computer vision and deep learning techniques. The system is designed to recognize and interpret American Sign Language (ASL) gestures, enabling efficient communication between individuals who are proficient in ASL and those who are not. The core of the system utilizes Python, OpenCV (Open-Source computer Vision Library), and MediaPipe Holistic for real-time hand and body pose estimation. By accurately tracking the movements of hands and key body parts, the system captures the nuances of sign language gestures. The captured data is then fed into a Long Short-Term Memory (LSTM) neural network, which excels in sequence modeling tasks. The LSTM model is trained on a comprehensive dataset of ASL gestures, encompassing a wide range of signs and expressions. Transfer learning techniques are also employed to fine-tune the model for improved performance in sign language recognition. The model's architecture allows it to learn the temporal dependencies and context inherent in sign language, making it capable of recognizing gestures within sentences or phrases. The system's evaluation demonstrates its effectiveness in real-world scenarios, achieving high accuracy and low latency in sign language recognition. It opens new avenues for accessible and inclusive communication, aiding both deaf and hearing individuals in bridging the communication gap. Future work may explore the integration of natural language processing (NLP) to facilitate two-way communication between sign and spoken language. In conclusion, this Gesture-Based Sign Language Detection System represents a significant step towards harnessing the power of computer vision and deep learning to make sign language more accessible and inclusive in various domains, including education, accessibility, and social interaction. © 2024 IEEE.

关键词： Long short-term memory

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A Comprehensive Examination of Toxic Tweet Classification on Twitter 9

A Comprehensive Examination of Toxic Tweet Classification on...

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9th International Conference on Science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Gokulnath, Shreejith Suthraye Jayaraman, Shrikar Nathezhtha, T. School of Computer Science And Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350365092

In recent times, daily interactions have been taking place largely on online platforms, with forms of interactions such as ***,an important problem lies in the understanding of these messages. These textual interchanges can be interpreted in multiple different ways by different people which can cause significant misunderstandings and negative perceptions. The aim of this project is to address this challenge and overcome it by developing a system that is capable of identifying the negativity and toxicity in the tone of the messages. Through rigorous analysis the model aims to identify parts or sequences of a message that might appear as offensive or negative, giving the user constructive feedback on how they can better improve their messages. This helps users to write messages that are not only clearer but also easily received in the intended manner by their audience, thereby increasing the effectiveness of the communication process and reducing misunderstandings. The main goal of this paper is to contribute to the development of an inclusive and understanding online community by using techniques of Natural Language Processing and Machine Learning to solve the complexities of human communication. © 2024 IEEE.

关键词： Sentiment analysis

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Multi-modal Knowledge Distillation-guided Artery-Vein Segmentation Network for OCT/OCTA Images 16

Multi-modal Knowledge Distillation-guided Artery-Vein Segmen...

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2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Li, Shuo Zhang, Wenbo Chen, Qiang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

The analysis of artery and vein differences in Optical Coherence Tomography Angiography (OCTA) is of great significance for diagnosing various eye diseases and systemic diseases (such as diabetic retinopathy, hypertension, and cardiovascular diseases). We propose a multi-modal knowledge distillation-guided Artery-Vein Segmentation Network for OCT/OCTA images (MKD-Net). Firstly, we introduce 2D projection image information in the process of segmenting from 3D to 2D to enhance the semantic representation capability in the horizontal direction of the network. Secondly, we transfer knowledge from a trained multi-modal (3D and 2D input) teacher network to a single-modal (3D input) student network, enabling the student network to not rely on the projection images generated by retinal layer segmentation, avoiding segmentation failure under diseased conditions, and enhancing its ability to capture details and feature changes in the images. Extensive evaluations on the OCTA500 and OCTA100 datasets demonstrate that the proposed method achieves superior performance, with an MIou of 84.96% and 84.25% on the OCTA500 and OCTA100 datasets respectively. © 2025 SPIE.

关键词： Optical coherence tomography

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An Improved Nominal Attribute Distance Measure Based on Attribute Dependent Representation 6

An Improved Nominal Attribute Distance Measure Based on Attr...

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6th International Conference on Robotics and computer Vision, ICRCV 2024

作者： Liu, Kuayue Gong, Fang Cui, Jinhao Zhang, Zhiqiang School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798331527426

In real data, there are dependencies between at-tributes, and most distance metrics make attribute independence assumptions, and although they show superior performance, when there are strong dependencies between attributes, it causes a certain degree of degradation to the distance metric performance. For this reason, this paper proposes a nominal attribute distance metric based on attribute dependencies. We conduct an extensive evaluation on a large number of UCI datasets and find that the method significantly outperforms currently available structural extension schemes in terms of negative log-likelihood and root-relative-square, while ensuring the robustness of the algorithm. © 2024 IEEE.

关键词： attribute independence assumption attributes dependence distance measure spec-tral clustering symmetry uncertainty

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AgriAid: An Intelligent Farmer Companion Using Deep Learning Approach 2

AgriAid: An Intelligent Farmer Companion Using Deep Learning...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Ilakiyaselvan, N. Dhandapani, Aarthi Khadar Nawas, K. Bhattacharya, Abhinov Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350306415

The percentage of farmers in India's enormous population is roughly 54.6%. However, despite having such a large population, they only contribute roughly 13.9% of the nation's total yearly GDP. This severe inequality may be reduced by improving farmers' access to information and expert advice. In order to increase crop output and quality, farmers typically depend on agricultural counselors and professionals to provide correct information for crop decision-making. Frequently, advisors or specialists in agriculture are not always available. A voice-based chatbot has several benefits, such as being real-time, constantly available, requiring little to no training to use, and providing instant access to information rather than requiring users to walk convoluted paths. In this study, we explain my design work for a farmer's helper, named Farmer Companion, to meet the needs of both rural and urban Indian farmers utilizing Supervised ML Algorithms and Deep Neural Networks, of which the best one is selected. A comparison of recent models that have been used in the literature is also included in the paper. © 2024 IEEE.

关键词： Artificial Neural Network Natural Language Processing Neural Networks Voice based Query System Voice-based Chatbot

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Energy-Efficient Computation Offloading in Edge Computing for Multi-UAV Networks 8

Energy-Efficient Computation Offloading in Edge Computing fo...

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8th International Conference on Smart Cities, Internet of Things and Applications, SCIoT 2024

作者： Ghasemi, Kimia Movahedi, Zeinab Iran University of Science and Technology School of Computer Engineering Tehran Iran

ISBN: (纸本)9798350374674

With the pervasive advancement of communication networks, the utilization of IoT permeates various spheres of societal life. To address the inherent limitations of IoT devices concerning computational power and energy, coupled with the imperative of minimizing access delays, computing offloading to edge computing emerges as a pragmatic solution. Moreover, propelled by the advent of 5G networks, drones have gained significant traction in augmenting network coverage across diverse environments. This paper delves into the intricacies of computation offloading and UAV path planning within an edge computing framework, particularly focusing on multi-UAV scenarios. We formulate the problem with the overarching objective of maximizing the energy efficiency across both UAV and IoT device layers, while accounting for the costs associated with locally executed tasks alongside offloaded ones. Given the formidable computational complexity entailed, we propose a solution algorithm predicated on the functional coverage set paradigm, aimed at enhancing the system's energy efficiency holistically. Evaluation results corroborate the efficacy of the proposed algorithm, demonstrating marked enhancements in energy efficiency and consumption by 128/% and 25%, respectively, compared to prior endeavors. © 2024 IEEE.

关键词： Computation offloading

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An Earthquake Alert system using Internet of Things 10

An Earthquake Alert system using Internet of Things

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Teja, N. Vishnu Akshay, S.P. Khaleed, K.Abu Kumar, C.Santan Manoj Kumar, G.P. Bhavadharini, R.M. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350353068

This project introduces an innovative earthquake detection system leveraging accelerometers for seismic sensing. By incorporating accelerometers from widely available devices like smartphones into a distributed sensor network, the system aims to provide a cost-effective and scalable solution for enhancing seismic monitoring capabilities. Real-time data processing, supported by advanced signal processing and machine learning algorithms, enables the system to differentiate between normal vibrations and seismic activities. Upon detecting anomalies, the system generates alerts to facilitate swift response and early warning mechanisms. Noteworthy features of this system include its affordability, easy deployment, and suitability for implementation in diverse environments, both urban and remote. The project contributes to advancing earthquake detection technologies, thereby bolstering preparedness and resilience against seismic events © 2024 IEEE.

关键词： Accelerometers

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Brain Tumor Disease Classification Using Weighted Average Ensemble Deep Learning Model 1

Brain Tumor Disease Classification Using Weighted Average En...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Yimer, Ketemaw Alebachew Sahoo, Abhaya Kumar Pradhan, Chittaranjan School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (纸本)9798350361780

In the context of medical research, brain tumors present a challenging problem due to their potential for rapid growth and their impact on overall health. Classifying brain tumors is an essential task for evaluating the tumors and selecting a course of treatment under their classes. In this study, we propose a weighted average ensemble technique for brain tumor classification. The proposed method combines the classification abilities of three pre-trained models, namely InceptionV3, Vgg16, and DenseNet121. We evaluate in detail how well each of these separate models performs in automatically classifying brain tumor MRI images. We then integrate their outputs using an ensemble learning approach by giving the appropriate weight for each base model. Our findings indicate a superior accuracy rate of 99.43% on the test dataset, surpassing the accuracy achieved by each base model. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Point Cloud Classification Fall Detection Method Based on Millimeter Wave Radar 24

Point Cloud Classification Fall Detection Method Based on Mi...

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4th International Joint Conference on Robotics and Artificial Intelligence, JCRAI 2024

作者： Zhou, Manyue Gong, Pengcheng Wu, Yuntao Liu, Jianyu School of Computer Science and Engineering Wuhan Institute of Technology Hubei Wuhan China

ISBN: (纸本)9798400710100

Falling at home is the most common cause of injury among the elderly. Wearable devices are inconvenient and costly, while camera systems pose risks to privacy. A fall detection method employing millimeter-wave radar coupled with a classification network is proposed to address these issues. This approach leverages the benefits of millimeter-wave radar and deep learning. The raw data processed by the radar yields the required information, which is then rendered into a point cloud. Subsequent voxelization enhances feature learning, enabling the model to function even with sparse data. The information is then fed into our custom-designed network for time series extraction to classify behaviors. Finally, the time series extraction network utilized in this study is compared with several popular networks, demonstrating that our system offers an effective technological avenue for target detection and posture analysis, and boasts high algorithmic precision in millimeter-wave radar fall detection. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Fall detection

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Teacher Probability Reconstruction based knowledge distillation within intelligent network compression

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International Journal of Intelligent Networks 2025年 6卷 47-56页

作者： Chen, Han Teng, Xuyang Su, Jiajie Li, Chunhao Hu, Chang Han, Meng School of Communication Engineering Hangzhou Dianzi University Hangzhou China Department of Computer Science National University of Singapore Singapore College of Computer Science and Technology Zhejiang University Hangzhou China

In the optimization of intelligent network architecture, limited resources at each node, including edge computing devices, have posed challenges for deploying large models in performance-demanding scenarios. Knowledge distillation serves as a model compression method that extracts knowledge from a large-scale teacher model and transfers it to a more lightweight student model. Previous knowledge distillation methods mainly focus on the intermediate layers of the network. However, due to privacy protection regulations that limit data sharing and access as well as computational efficiency requirements in practical scenarios, feature-based distillation encounters challenges in practical applications. We start with logit-based distillation to address these issues, enabling students to learn more representative knowledge from the teacher's output probability distribution. Due to the structural limitations of the teacher network such as insufficient depth or width, and potential issues in the training data like noise and imbalance, the output probability distribution contains many errors. Therefore, we propose a knowledge distillation method that improves the student by correcting errors in the teacher model. Nevertheless, teacher's errors not only bring mistakes to students but also give students greater subjectivity, enabling them to break free from the limitations of the teacher. We also retain the teacher's thinking to prevent students from becoming biased on the remaining (non-target) categories while correcting teacher errors for students. Extensive experiments demonstrate that our method achieves competitive performance on multiple benchmarks without extra parameters. © 2025 The Authors

关键词： Normal distribution

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