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Medical Plant Detection using Transfer learning Coupled with K-Nearest Neighbour Algorithm 3

Medical Plant Detection using Transfer learning Coupled with...

引用

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

作者： Srambickal, Sai Subramanian Srivastava, Aman Somasundaram, S.K. School of Computer Science and Engineering Vellore Institute of Technology Department of Computer Science and Engineering Tamil Nadu Vellore India Vellore Institute of Technology School of Computer Science and Engineering Department of Information Security Tamil Nadu Vellore India

ISBN: (纸本)9798350372120

Medical Plants are being used for more than a thousand year, with evidences dating before the Mauryain Era (around 322 BCE), they are widely used in the Ayurvedic school of medicine for therapeutic as well as medicinal purposes, it requires an expert to identify the species as well as the medical property associated with a given plant. This paper aims to use deep learning in order to identify a given plant using the leaves of the plant by using a pretrained Convolutional Neural Network (CNN) model namely ResNet50 and MobileNet to extract the latent vector which will then be passed to K-Nearest Neighbour (KNN) algorithm in order to identify the plant class, the best accuracy of the proposed models comes out to be 97.65%. © 2024 IEEE.

关键词： Neural network models

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Deep Reinforcement Learning With Mutual Information for Selecting Bands in Hyperspectral Image Classification 2

Deep Reinforcement Learning With Mutual Information for Sele...

引用

2nd International Conference on Power, electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Tisha, Fahmida Tasnim Alam, Monmoy Mishu, Sadia Zaman Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi Bangladesh

ISBN: (纸本)9798331517984

Uyperspectral imaging (USI) is a critical technology in remote sensing that provides extensive spectral information for material identification and classification on Earth's surface. Despite its benefits, the high dimensionality of USI data introduces significant challenges, such as elevated computational costs and extensive storage needs due to band redundancy. This research proposes an innovative approach to hyperspectral band selection using Deep Reinforcement Learning (DRL) with Mutual Information as a reward mechanism, aiming to more effectively capture inter-band dependencies than traditional methods. This approach selects the most informative bands that provide significant contributions to achieving better classification results. The study also introduces an advanced classifier array incorporating Convolutional Neural Networks (CNNs) alongside traditional machine learning classifiers like K-Nearest Neighbors (KNN) and Support Vector Machines with Radial Basis Function (SVM-RBF) to leverage both spectral and spatial data relationships. The DRL model, framed as a Markov Decision Process, learns an action-value function to select optimal band combinations, enhancing classification accuracy and reducing data redundancy. Experiments conducted on the Indian Pines and Botswana datasets demonstrate that the Mutual Information-based DRL model significantly outperforms traditional entropy-based methods across all used classifiers, confirming the efficacy of the proposed approach. This integration of Mutual Information and a DRL framework offers promising advancements for efficient and effective hyperspectral imaging systems, setting a new benchmark in the field. © 2024 IEEE.

关键词： Deep reinforcement learning

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Learnscan: An Interactive Learning Platform for Enhanced Education Using Augmented Reality 4

Learnscan: An Interactive Learning Platform for Enhanced Edu...

引用

4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Kamath, Ashwini Deepikaa, T.R. Godlin Donya, S.P. Pavithra, R. Madhumathi, R. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331529635

An Interactive Learning Platform for Enhanced Education using Augumented Reality (AR) presents the development of an innovative educational website and app designed to enhance learning experiences through the integration of QR codes and Augmented Reality technology. The platform features a curated list of QR codes, each assigned to specific educational topics. Upon scanning a QR code, users are directed to a dedicated website showcasing a detailed 3D model related to the QR code's assigned subject matter. Scanning a QR code associated with 'heart' will lead to a 3D model of the human heart. The website offers an interactive interface that allows users to explore and manipulate the 3D models in real-time. Also, the platform incorporates AR functionality, enabling users to view and interact with the models in their physical environment. It is also aided with audio recordings and description related to the information which is provided in the website. This immersive approach aims to provide a more engaging and effective learning experience, especially for complex subjects where visualizations are beneficial. Key features of the platform include user-friendly navigation, highquality 3D models, and seamless integration with AR technology. © 2024 IEEE.

关键词： 3D modeling

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Apparel Customization Using Generative AI 10

Apparel Customization Using Generative AI

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Menon, Gokulraj Peter, Amal P. Jomon, Ebin Varghese, Joe Anly Antony, M. Sahrdaya College of Engineering and Technology Department of Computer Science and Engineering Thrissur India

ISBN: (纸本)9798350384369

Our project pioneers a transformative platform, marrying artificial intelligence with fashion customization. Users engage with an intuitive interface, seamlessly translating their style visions into unique apparel designs. Leveraging machine learning, the system analyzes content and styles images, intelligently transferring selected stylistic elements to create a harmonious blend. The output showcases personalized, stylized objects each a reflection of the user's chosen style fused with the original content subset. This endeavor not only redefines fashion personalization but also marks a distinctive application of AI in creative expression. We invite collaboration and support as we shape a future where technology reimagines the landscape of personalized apparel design. © 2024 IEEE.

关键词： Economic and social effects

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Escherichia Coli Bacterial Identification using Deep Learning Approach 1

Escherichia Coli Bacterial Identification using Deep Learnin...

引用

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

作者： Tasneem, Nelufar Sunanda Ramesh Babu, D.R. Dayananda Sagar College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350352931

Urinary Tract Infections (UTIs), predominantly caused by Uro-Pathogenic Escherichia Coli (UPEC), account for 80% of cases, posing a significant health challenge. Traditional methods for bacterial identification, such as culturing, are both time-consuming and costly. In this study, a Deep Learning (DL) approach using a Convolutional Neural Network (CNN) model developed with TensorFlow's Keras API is presented to quickly and accurately identify E. coli and non-E. coli bacteria colonies from a custom dataset of 978 images collected using mobile devices. The model, comprising three convolutional layers (32, 64, and 128 filters), was trained for 30 epochs, achieving 91% accuracy. Max pooling, dropout layers, L 2regularization and early stopping was implemented to prevent overfitting. Data augmentation was also applied to enhance model generalization. The findings demonstrate that this cost-effective, real-time deep learning method provides an efficient alternative to conventional identification techniques, offering a promising tool for rapid diagnostics in clinical environments. © 2024 IEEE.

关键词： bacterial colonies convolutional neural network deep learning escherichia coli urinary tract infection

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Design and Analysis of a Multipath Routing Protocol to Enhance QoS in MANET 1

引用

3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Saraswat, Devdas Khan, Nikhat Raza Department of Computer Science and Engineering IES University Bhopal India Department of Computer Science and Engineering IES College of Technology Madhya Pradesh Bhopal India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

A combination of their ability to adapt to a broad spectrum of applications and their ability for self-organization, mobile ad hoc networks, or MANETs, have attracted much interest. However, the fundamental characteristics of MANETs—such as limited resources and node mobility—present challenges for ensuring reliable communication and maintaining Quality of Service (QoS). The study presents the Multipath Routing Protocol (MRP) designed for enhancing QoS in MANETs with the goal of getting around these issues. The recommended MRP produces multiple inconsistent routes among source and destination nodes using the dynamic topology of MANETs the goals of this multiplepath’s strategy are to enhance throughput, reduce packet loss, improve network reliability, and minimize the effects of node movement and link failure. For determining the most suitable routes for the transfer of data, the protocol utilizes a dynamic routing method that takes into account multiple factors such as node energy levels, bandwidth at hand, and link quality. For the purpose of determining the MRP’s efficiency, thorough simulations have been carried out utilizing an established network simulation environment. There are many QoS measurements that are important, such as packets transferred, received, the proportion of delivered packets of data, and the usual routing load. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Mobile ad hoc networks

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Image Segmentation and Feature Extraction for Gastric Cancer Detection 3

Image Segmentation and Feature Extraction for Gastric Cancer...

引用

3rd International Conference for Innovation in Technology, INOCON 2024

作者： Yamuna, S. Vinothiyalakshmi, P. Sri Venkateswara College of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350381931

Gastric cancer is a prevalent and life-threatening disease that necessitates early detection for effective *** recent years, image segmentation techniques have shown promise in aiding the detection of gastric cancer. This study introduces a novel approach for detecting gastric cancer by utilizing image segmentation and feature extraction *** primary objective is to accurately determine the presence of cancer in the stomach based on medical images. The proposed method involves segmenting the stomach region from the input image using advanced segmentation algorithms. Subsequently, relevant features are extracted from the segmented region to differentiate between cancerous and non-cancerous tissues. The experimental results validate the effectiveness of the proposed approach in accurately detecting gastric cancer. If implemented, this method has the potential to assist medical professionals in making timely and accurate diagnoses, ultimately leading to improved patient outcomes. © 2024 IEEE.

关键词： Feature extraction

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Autonomous Transmission Route Planning of Large Sport Utility Vehicle Using Ensemble RNN-Based Reinforcement Learning 3rd

Autonomous Transmission Route Planning of Large Sport Utilit...

引用

3rd International Conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2024

作者： Karthikeyan, K. Karthik, S. Periyakaruppan, K. Kavitha, M.S. Department of Computer Science and Engineering SNS College of Engineering Tamilnadu Coimbatore641107 India Department of Computer Science and Engineering SNS College of Technology Tamilnadu Coimbatore641035 India

ISBN: (纸本)9789819739905

The field of autonomous vehicle technology is constantly evolving, and the transmission of large Sport Utility Vehicles (SUVs) presents a unique challenge. In this research, a novel route planning framework specifically designed for large SUVs, leveraging the capabilities of Ensemble Recurrent Neural Networks (RNNs) in conjunction with Reinforcement Learning (RL). The aim is to create an adaptive system capable of learning and optimizing route planning strategies through interactions with the SUV dynamic environment. This research seeks to fill the existing gap in literature by proposing a comprehensive method that caters to the specific requirements of large SUVs, enhancing their efficacy in autonomous transmission scenarios. The proposed method involves training the ensemble RNN using a comprehensive dataset that incorporates diverse driving scenarios for large SUVs. The reinforcement learning component fine-tunes the model based on feedback from simulated and real-world environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Recurrent neural networks

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DRA Overlapping Function Hardware Implementation Via Waveguide Beamformer

DRA Overlapping Function Hardware Implementation Via Wavegui...

引用

2024 Panhellenic Conference on Electronics and Telecommunications, PACET 2024

作者： Kaifas, T.N.F. Babas, D. Kyriacou, G. Sahalos, J. Democritus University of Thrace Department of Electrical and Computer Engineering Xanthi Greece Aristotle University of Thessaloniki Department of Physics Thessaloniki Greece University of Nicosia Department of Electrical and Computer Engineering Cyprus

ISBN: (纸本)9798350318845

In the work at hand we present the design procedure of a waveguide beamformer network that implements the Direct Radiating Arrays' Overlapping Function. Such a design is notorious for its complexity and challenge since it should deliver wideband, dual polarization overlapped feeding to planar antenna arrays. Such a feeding network is to be viewed as a building block that would tile the background, on top of which the fed sub-array antenna elements would respectively tile the radiating aperture host area. Thus, each feeding network should provide ports on the lower face, (input feeding ports), the upper face, (input to antenna elements), but also to the side faces to provide overlapping. Here, utilizing transmission matrix factorization, the beamformer is designed as a cascade connection of elementary components, hybrids and phase shifters. The 3d actual implementation of the multiport network in a rectangular waveguide technology, supporting both polarizations, is indeed challenging. In the current work such a device is presented and its merit is demonstrated. © 2024 IEEE.

关键词： Waveguides

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Advanced Construction Materials Based on Concrete to Protect the Living Space from Non-Ionizing Radiation 1

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32nd International Conference on Organization and Technology of Maintenance, OTO 2023

作者： Mandrić, Vanja Rupčić, Slavko Vinko, Davor Dominković, Ivica Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9783031514944

ISBN: (纸本)9783031514937

This paper presents measurements of the reflection and transmission coefficient of electromagnetic waves through concrete and two concrete-based composites: concrete with steel fibers and concrete with carbon fibers with the aim of enhancing the attenuation of electromagnetic waves passing through concrete and concrete composites. The frequency range for which the measurements were carried out extends from 30 MHz to 18 GHz and includes most of the existing stationary (and mobile) radiation EM sources in the environment in which the general population moves (FM, DVBT2, 2G - 5G mobile radio systems, stationary radar border area control systems). The measurement results show that as the thickness of the concrete increases, the transmission through concrete and concrete composites decreases (parameter S12 decreases). Furthermore, composites with carbon fibers partially reduce, while those with steel fibers significantly reduce, the transmission of EM waves through such composites. The reduction of parameter S12 by composites with steel is up to 52 dB (at a frequency of 2.9 GHz) for a block thickness of 100 mm. It is important to emphasize that in the case of composites with steel fibers, the influence of the fibers on the transmission parameters is more significant than the thickness of the sample. The reason for this is the increase in electrical conductivity of composites with steel fibers due to the increased proportion of conductive components in the concrete. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Steel fibers

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