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Empowering Limited Data Domains: Innovative Transfer Learning Strategies for Small Labeled Datasets

Empowering Limited Data Domains: Innovative Transfer Learnin...

引用

2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Navaneetha Krishnan, S. Mohnish Karthikeyan, B. Arun, Shriman K. Christopher Columbus, C. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331529765

Traditional Indian Medicine faces the risk of extinction due to insufficient transfer of knowledge. This research highlights the critical need for preservation through modern technology and techniques. It addresses the challenges of limited labeled data by employing pioneering transfer learning approaches for classifying Indian Medicinal Plants, specifically leaves. The study utilizes diverse trained models and a bespoke model tailored for this task, systematically investigating transfer learning dynamics to mitigate issues such as overfitting and underfitting in neural network training. Rooted in Artificial General Intelligence (AGI) concepts, the research demonstrates how transfer learning enhances neural network efficiency, especially when labeled data is scarce. Practical application is illustrated with a dataset of plant images, providing a real-world context for evaluating transfer learning strategies. By leveraging models trained with large data in relevant domains, the research facilitates effective feature extraction and generalization despite limited labeled data. A significant contribution of the research is the comparison of outcomes from various models with the proposed custom CNN model, HerboraNet, which achieves a remarkable accuracy of 95%. This research underscores the superiority of transfer learning over training models with limited input, particularly in data-constrained domains, highlighting its pivotal role in enhancing model efficiency and adaptability. © 2024 IEEE.

关键词： Transfer learning

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Transaction Fraud Detection Using SMOTE Oversampling 1

Transaction Fraud Detection Using SMOTE Oversampling

引用

1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Marimuthu, M. Lekshmi, K. Saravanan, P. Nagaveni, Dasari Manikandan, P. Natarajan, B. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350352931

Credit card fraud detection remains a critical challenge in financial transactions, particularly due to the prevalence of unbalanced datasets and the limitations of traditional algorithms. This paper suggests a novel hybrid ensemble approach that reduces false negatives and improves fraud detection accuracy by merging the Random Forest (RF) and XGBoost (XGB) algorithms. Unbalanced data presents challenges for traditional algorithms such as Random Forest, Decision Trees, XGBoost, Logistic Regression, and Deep Neural Networks (DNN). Our hybrid technique, which is enhanced by under-sampling and the Synthetic Minority Over-sampling Technique (SMOTE), capitalizes on the benefits of RF and XGB while limiting their drawbacks. We prioritize recall over accuracy in order to reduce false negatives, which are important for detecting fraud. With an emphasis on recall, we evaluate the performance of Decision Trees, Random Forest, Logistic Regression, XGBoost, DNN, and our hybrid model. Utilizing techniques to address data imbalance, the evaluation makes use of an actual credit card dataset with different attributes. The results demonstrate the effectiveness of our hybrid ensemble approach in enhancing fraud detection accuracy and reducing false negatives, highlighting its potential for practical application in financial security systems. © 2024 IEEE.

关键词： Logistic regression

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Dual-Model Approach for Diabetic Retinopathy and Macular Edema Detection

Dual-Model Approach for Diabetic Retinopathy and Macular Ede...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Micheal, A. Ancy Sai, Ladi Jeevan Vellore Institute of Technology Chennai School of Computer Science and Engineering India

ISBN: (纸本)9798350378092

Diabetic Retinopathy (DR) and Diabetic Macular Edema (DME) are major causes of vision loss in diabetic patients. Early detection is crucial for preventing permanent vision impairment. This research paper proposes a novel dual-model architecture for detecting Diabetic Retinopathy and Diabetic Macular Edema. The proposed framework integrates preprocessing techniques such as circular cropping and Ben's preprocessing, followed by data augmentation. The Diabetic Retinopathy model is trained on the APTOS2019 dataset, while the Diabetic Macular Edema model is trained using the IDRid dataset. InceptionV3 outperforms other models with an accuracy of 88% for Diabetic Retinopathy classification, while MobileNetV2 outperforms other models with an accuracy of 83% for DME classification. The dual-model architecture enables simultaneous evaluation of retinal images for DR and DME diseases. The Experimental results demonstrate promising outcomes, suggesting the potential effectiveness of the proposed dual-model architecture in diagnosing diabetic eye diseases. © 2024 IEEE.

关键词： Ophthalmology

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Real-Time Gesture Based Sign Language Recognition System

Real-Time Gesture Based Sign Language Recognition System

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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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ResNet50 Integrated Vision Transformer for Enhanced Plant Disease Classification 3

ResNet50 Integrated Vision Transformer for Enhanced Plant Di...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Mohnish Kumaar, D. Palani, Saravanan Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350372120

This study introduces an innovative hybrid model that integrates the powerful feature extraction capabilities of ResNet 50 with the advanced pattern recognition and contextual understanding afforded by the Vision Transformer (ViT) for the classification of plant diseases. By embedding a class token into the input feature sequence and leveraging a novel multi-head attention mechanism, this model captures both local and global image features, leading to a significant enhancement in diagnostic accuracy and efficiency. Our approach involves a meticulous configuration process that adjusts hyperparameters such as the number of transformer encoder layers and attention heads to optimize performance. Rigorous testing on a meticulously curated and pre-augmented dataset underscores the model's superiority over existing methodologies, demonstrating its readiness for real-world agricultural applications. The findings from this research not only mark a substantial advancement in plant disease diagnostics but also set a new benchmark for the application of hybrid deep learning models in precision agriculture, paving the way for sustainable farming practices in the face of escalating global food demands. © 2024 IEEE.

关键词： Deep learning

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Flowing Asanas: Enhancing Yoga Posture Detection With BiLSTM Attention and MediaPipe 2

Flowing Asanas: Enhancing Yoga Posture Detection With BiLSTM...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Noel Jeygar Robert, V. Aakash Kumar, P. Rani, Abha Pandey, Shraddha Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331534950

Yoga is an ancient practice that the combines harmony of mind, body, and soul with discipline, relaxation, and happiness. Besides its breathing and meditation exercises, a central aspect of yoga is its asanas (postures). Keeping the importance of asanas of yoga in mind, this research paper aims to describe a novel and compact posture detection system for yoga that integrates MediaPipe for real-time skeletal landmark placement and deep learning models for posture analysis and accuracy evaluations. The architecture acquires live yoga poses and hand gestures directly from a webcam through an RGB camera where MediaPipe recognizes key body points such as the knees, eyes, hands, etc., to create a skeleton representation of the pose taken by the user. Next, these skeletal structures are evaluated by deep learning algorithms against correct yoga postures that have been pre-trained and can tell how aligned or accurate the user's postures may be. It automatically stops the timer once it identifies the wrong posture to ensure enhanced safety and efficiency. The technique helps expand the horizons of yoga to greater potential in physical training and rehabilitation while enhancing the practice itself. © 2024 IEEE.

关键词： Deep learning

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Congestion Based Adaptive Association Mechanism for IEEE 802.11ah Based Large IoT Network 5th

Congestion Based Adaptive Association Mechanism for IEEE 80...

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： R, Nandhini R, Radha School of Computer Science and Engineering Vellore Institute of Technology–Chennai Chennai India

ISBN: (纸本)9783031770807

The IEEE 802.11 ah standard is the prominent protocol for medium range communication in the domain of Internet of Things (IoT), facilitating interaction between wireless sensor nodes and access points. Supporting up to approximately 8192 nodes connected to a single access point through single hop strategies, IEEE 802.11 ah employs CSMA/CA for the authentication and association process of sensor nodes with access point. However, the standard encounters challenges such as significant collisions and prolonged network setup times when attempting to associate an excessive number of wireless sensor nodes with a single access point, primarily due to authentication thresholds set at intervals. In this research, we propose a dynamic adjustment of the authentication threshold based on network congestion status. Learning parameters including the number of requests served successfully by the access point in the past, present and a congestion threshold are leveraged to adapt to current network congestion levels. Through evaluation using the LILD method in the ns-3 simulator, our approach demonstrates considerable enhancements in terms of collision reduction and network setup efficiency. This research endeavours to promote responsible network management practices for improving the performance and reliability of IEEE 802.11 ah based IoT deployments. © ICST institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Internet of things

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Change Vector Change Indices Method for Unsupervised Remote Sensing Change Detection

Change Vector Change Indices Method for Unsupervised Remote ...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Sujith Kumar, A. Sundaram, Venkatesan Meenakshi Prabhavathy, P. National Institute of Technology Department of Computer Science and Engineering Puducherry India School of Computer Science Engineeering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331540685

Unsupervised Remote sensing change detection is very important because it addresses the problem of scarcity in the data availability in training. Some of the researchers are successful in finding the change regions but are not able to classify what changes happened in those regions. Nearly every study faces challenges in generalizing the fluctuations occurring during both the growing and non-growing seasons. The current study utilizes a novel architecture consisting of two parts. The first part involves proposing change regions through the change vector calculation method and applying Otsu's Thresholding on the calculated change vectors, while the second part entails classifying these proposed change regions into vegetation change, building change, and non-change regions. non-change region classification process aims to eliminate false changes identified by the change vector analysis and Otsu's Thresholding. In the second stage, NDVI, NDWI, and NDMI indices are compared and the resulting change mask obtained is subjected for element-wise multiplication operation with the change mask obtained in the first stage. The study addressed the challenge of generalizing short-term changes, such as those occurring during the growing and non-growing seasons, by computing the median of pixels over a time range encompassing both types of changes. Additionally, the method was fine-tuned using NIT Puducherry data from two time periods. Subsequently, the entire Karaikal district was considered to examine the changes comprehensively. The present study outperformed different state of the art methods like CVI_OTSU, PCI_Kmeans, Continuous Change Detection and Classification (CCDC). The present study got better results with an accuracy of 86.07 and kappa coefficient of 0.7898. © 2024 IEEE.

关键词： Change detection

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Transforming Electric Vehicle Navigation Strategies: An AI-Enhanced Framework for Achieving Superior Route Efficiency in Varied Conditions

Transforming Electric Vehicle Navigation Strategies: An AI-E...

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2024 International Conference on Advancement in Renewable Energy and Intelligent Systems, AREIS 2024

作者： Prakash, P. Anandha Radha, R. School of Electrical Engineering Vellore Institute of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350387230

Electric vehicles (EVs) are essential for environmentally friendly transportation, there are significant challenges in maximizing their routing in many scenarios. This paper explores advanced predictive analytics strategies aimed at enhancing electric vehicle routing in India. Electric vehicles (EVs) are essential to the progress of sustainable transportation;yet, there are significant challenges in optimizing EV routing in a range of challenging scenarios. This paper focuses on enhancing EV routing in the Indian setting, where varied road conditions, dynamic traffic patterns, and infrastructural constraints are common. We provide a sophisticated routing algorithm that accounts for these problems by including a traffic coefficient, which denotes a 50% increase in effective distance due to high traffic. This method makes use of this traffic coefficient to modify routing computations, guaranteeing more precise and useful route planning in practical situations. By dynamically adjusting to real-time traffic data, the system maximizes route efficiency and lowers EV operating costs. We describe the algorithm's implementation in detail and illustrate its efficacy with simulated results and case studies showing significant gains in routing accuracy. The suggested methodology bridges the gap between theoretical models and real-world implementations, providing insightful information to stakeholders in the Indian EV ecosystem. This work intends to promote EV infrastructure and routing efficiency in India by offering a strong foundation for EV route optimization, which will inform future research and policy-making in this crucial field. © 2024 IEEE.

关键词： charging optimization charging prediction Dijkistra Routing and comparative analysis electric vehicle routing Machine learning traffic prediction

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