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检索条件"机构=Department of Computing Technologies School of Computing SRM Institute of Science and Technology"

共 4461 条记录，以下是81-90 订阅

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Application of CNN Based Deep Learning Algorithm for Herbal Plant Leaf Detection

Application of CNN Based Deep Learning Algorithm for Herbal ...

引用

2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Pongiannan, R.K. Das, Sagardeep Purbia, Rudra Prakash Brindha, R. Jame, S. Lourde Shiju Kumar, P.S. Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur India

ISBN: (纸本)9798331509828

The medicinal plant market faces challenges due to inefficiencies and a lack of transparency. In this paper, a total of 10GB dataset with 10,000 entries, including 15 attributes such as plant name, region, price, and high-resolution images capture seasonal and geographical variations. Designed for precise plant identification and market forecasting, the dataset also contains metadata and historical data on prices and demand trends. Deep learning models, including recurrent neural networks (RNN) and convolutional neural networks (CNNs), are employed for accurate plant identification. Time series analysis is used to forecast market patterns. Data optimization techniques are applied to ensure real-time insights, allowing farmers and traders to make informed decisions. This system aims to enhance market transparency and improve the decision-making process in the medicinal plant industry. © 2025 IEEE.

关键词： Convolutional neural networks

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Leveraging HPC with GAN-CNN Integration for Enhanced Tuberculosis Detection using Synthetic Image Generation 3

Leveraging HPC with GAN-CNN Integration for Enhanced Tubercu...

引用

3rd International Conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Manasa, Pokala Veeramalla, Vaishnavi Vanusha, D. Vathana, D. Robinsha, Delsi S. Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chengalpattu603203 India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chengalpattu603203 India

ISBN: (纸本)9798331532420

Tuberculosis (TB) remains a significant global health challenge, especially in remote areas with limited medical resources. Early and accurate diagnosis is crucial to control its spread. This project harnesses high-performance computing (HPC) to address two key issues: managing unreliable medical data and improving TB diagnosis. Given the imbalance between healthy and TB-affected samples, Generative Adversarial Networks (GANs) are used to generate synthetic images, enriching the dataset and making it more diverse. This helps train Convolutional Neural Networks (CNNs), which are highly effective in medical image analysis. By leveraging HPC, we accelerate the training of CNNs on large, augmented datasets, significantly reducing processing time while maintaining accuracy. This system improves TB detection by combining GAN-enhanced data and CNN models, enabling faster and more reliable diagnosis. © 2024 IEEE.

关键词： X ray apparatus

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Detection and Classification of Monkeypox Disease with Chatbot 3

Detection and Classification of Monkeypox Disease with Chatb...

引用

3rd International Conference on Intelligent Data Communication technologies and Internet of Things, IDCIoT 2025

作者： Chandu, P. Siddardha, P. Priyadharshini, K. Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9798331527549

Monkeypox detection is essential for effective public health management and controlling its spread. Timely detection enables early intervention in outbreaks, reducing transmission risk. This project presents an innovative framework that enhances monkeypox detection and classification using deep learning and an interactive chatbot. It employs the VGG16 convolutional neural network (CNN) for accurate classification of monkeypox from medical images, utilizing performance metrics like precision, recall, and F1 score for reliability. Additionally, the system includes a neural network-based chatbot that provides interactive assistance, answers user questions, offers symptom information, and guides users through the diagnostic process. This chatbot improves user engagement and accessibility, linking clinical diagnostics with patient interaction. Built on the Flask framework, the system ensures a smooth, user-friendly interface for both image classification and chatbot functionalities. © 2025 IEEE.

关键词： Convolutional neural networks

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VisionAid: Enhancing Accessibility for the Visually Impaired with YOLO and gTTS

VisionAid: Enhancing Accessibility for the Visually Impaired...

引用

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

作者： Lakshmi Raghupathy, Bhagya Nithyashri, J. Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331521394

Daily environment navigating and accessing visual information are critical problems for people with vision disabilities. To reduce this discrepancy VisionAid, an assistive application was introduced to make visually challenged person have better accessibility via integration of YOLO for real-time object detection and gTTS which is library used for text-to-speech conversion. It recognizes objects, speaks text aloud and promotes good navigation skills that improve mobility and independence. VisionAid augments vision loss with modern technologies like GPS and SLAM for navigation, rather than traditional aids. This paper presents VisionAid as an end-to-end solution, fills the gaps in existing models and brings more autonomy to its users. © 2025 IEEE.

关键词： Object detection

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DocQueue: A Scheduling Algorithm for Optimizing Healthcare Resources

DocQueue: A Scheduling Algorithm for Optimizing Healthcare R...

引用

2025 IEEE International Students' Conference on Electrical, Electronics and Computer science, SCEECS 2025

作者： Joji, Jeff Johnson, Sobin Angayarkanni, V. Rama, P. Kalaivani, J. Srm Institute of Science & Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331529833

DocQueue is a cloud-based system that optimizes patient scheduling in order to maximize the delivery of healthcare by prioritizing cases (dynamically) based on real-time information and availability of resources. It uses a decision-tree algorithm to analyze symptoms in real time, and then flags high-priority cases immediately, but uses a round-robin scheduling mechanism to manage low-priority cases so things are fair. And the AWS based architecture of the system allows for scalable deployment throughout the healthcare continuum. In simulations, these changes have shown to drastically cut down patient waiting time and make better use of resources, and therefore provide better patient care and more efficient operations. DocQueue is different from the usual scheduling systems because it is flexible and data-driven, it can change its schedule based on the real time needs of health care. © 2025 IEEE.

关键词： Scheduling algorithms

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Beyond the Pixels: Detecting Originality and Assessing Product Condition using Digital Image Processing Techniques 7

Beyond the Pixels: Detecting Originality and Assessing Produ...

引用

7th International Conference on Circuit Power and computing technologies, ICCPCT 2024

作者： Arhatia, Ishit Abraham, Bryan SRM Institute of Science and Technology Department of Computing Technologies India

ISBN: (纸本)9798350372816

This paper introduces a novel approach using digital image processing for product management, focusing on authenticity verification and quality assessment. Advanced algorithms distinguish genuine from fraudulent products and evaluate their condition. The study highlights the potential of digital image processing to enhance supply chain security and consumer trust, promising significant business impacts by reducing counterfeit circulation, improving brand reputation, and optimizing inventory management © 2024 IEEE.

关键词： Inventory control

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WanderVision : Enhancing Dementia Care with Artificial Intelligence and Augmented Reality 5

WanderVision : Enhancing Dementia Care with Artificial Intel...

引用

5th International Conference on Emerging Systems and Intelligent computing, ESIC 2025

作者： Arhatia, Ishit Abraham, Bryan Sathianesan, Godfrey Winster Srm Institute of Science and Technology Department of Computing Technologies Chengalpattu India

ISBN: (纸本)9798331522100

This paper is an advanced application of Augmented Reality to form an intelligent navigation system designed for support towards individuals with dementia. This system aims to identify and mark paths that are safe in guiding users through complex environments and, ultimately, toward a safer and more immersive experience in navigation. Assisted with occlusion handling it will be able to enhance user awareness by overlaying critical safety cues within the user's field of view dynamically. This is even more enhanced beyond just the aspect of navigation, as with the integration of computer vision technologies, providing solutions on medicinal and dietary requirements supporting lifestyle choices towards a healthier living and personal safety. Concentration on safety and assistance towards health issues adequately comes under the general scope of the project, which may help ensure greater independence and personal autonomy in the lives of persons with dementia-managing life's routine activities or dealing with episodes of cognitive disorientation. This AR-based system would directly deliver personalized health and safety insights to users with an aim to enhance the autonomy and resilience of individuals with dementia, providing practical support in leading safer, more empowered lives. © 2025 IEEE.

关键词： Assisted living

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Web-Based Framework for Evaluating Clustering Algorithms Across Multiple Datasets

Web-Based Framework for Evaluating Clustering Algorithms Acr...

引用

2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Tiwari, Gyanendar Kumar Singh, Kshitij Dorathi Jayaseeli, J.D. Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur India

ISBN: (纸本)9798331509828

This research study develops a web-based framework to compare clustering algorithms based on multiple datasets, assess the performance of algorithmic based on dataset characteristics and clustering objectives. The framework designed to support such widely used algorithms such as KM, DBSCAN and Hierarchical Clustering, offers visual and quantitative analysis for performance comparison. This platform was designed to make it easier for users to try out various algorithms and different parameter settings and thus, help them to choose the best clustering methods for a range of data types. This work impacts that greater understanding of how effective clustering is based on different data structures and application contexts by providing a useful framework for data driven research and analysis. © 2025 IEEE.

关键词： clustering algorithms DBSCAN Hierarchical Clustering K-Means Web-based framework

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Enhanced Indoor Pathfinding Navigation in Multistoried Buildings Using a Hybrid of RRT-Connect and Dijkstra's Algorithm

Enhanced Indoor Pathfinding Navigation in Multistoried Build...

引用

2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Ramamoorthy, S. Yadav, Abhishek Pal, Subha Bal School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Tamil Nadu Chennai603203 India

ISBN: (纸本)9798331509828

This paper presents a hybrid approach to enhance indoor pathfinding and navigation within complex multistory environments by integrating RRT-Connect and Dijkstra's algorithm. The objective is to address the limitations of traditional algorithms like Dijkstra and A*, which struggle with computational inefficiency and adaptability in dynamic, high-dimensional spaces. The proposed solution leverages the rapid path generation capabilities of RRT-Connect, combined with Dijkstra's algorithm for refining and optimizing the final route to ensure accuracy and optimality. To further improve efficiency, parallel processing techniques utilizing multi-threaded CPU operations are incorporated, enabling real-time computation even in dynamic environments. Additionally, a frontend visualization system is developed using React, which provides users with real-time rendering of path nodes on multistory floor plan images, enhancing usability and system interactivity. Experimental findings reveal that the hybrid approach achieves a significant reduction in computation time - up to 25% compared to traditional methods - while maintaining high pathfinding accuracy. This novel combination of rapid exploration, optimal path refinement, and real-time visualization offers a robust and scalable solution for navigating complex multistory environments, making it a significant advancement in the field of indoor navigation. © 2025 IEEE.

关键词： Visualization

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An Improved Detection of Skin Melanoma Using Hybrid CNN-MobileNet V2 Approach with Multi-Scale Attention 2

An Improved Detection of Skin Melanoma Using Hybrid CNN-Mobi...

引用

2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Maniraj, S.P. Thamizhamuthu, R. School of Computing SRM Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Chennai603203 India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai603203 India

ISBN: (纸本)9798331505745

Identifying melanoma in dermoscopic images is an immense challenge due to the varied appearances of the lesions. This research introduces a hybrid CNN-LSTM model augmented with a multi-scale attention mechanism to tackle this complexity, utilising CNNs for spatial feature extraction and LSTMs for temporal pattern recognition. Our model surpasses traditional CNN and MobileNet V2-LSTM models, with an accuracy of 96.98%, a recall of 96.65%, a specificity of 97.56%, and a JSI of 95.78%. These measurements demonstrate enhanced sensitivity and reliability compared to baseline models, signifying strong diagnostic capabilities. This study improves melanoma detection, aiding in more precise diagnoses for clinical environments and remote telemedicine applications. Future initiatives involve enhancing the model for more comprehensive skin lesion categorisation. © 2025 IEEE.

关键词： Feature Selection

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