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检索条件"机构=Computer Science and Engineering SRM Institute of Science And Technology"

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Smart Home Gardening with Robotic Assistance using Cloud-Connected Plant Care and Neural Networks Growth Analysis 1

Smart Home Gardening with Robotic Assistance using Cloud-Con...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Rajalakshmi, P. Dhivya, K. Anitha, D. Sandhya, Potturi Gnanavel, N. Muthulekshmi, M. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tami Nadu Chennai India SRM Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Department of Information Technology Tamil Nadu Namakkal India Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376517

This research introduces a new method for home gardening that uses neural networks to analyze plant development, cloud-connected plant care, and robotic help. The goal of combining these technologies is to make home gardening more efficient and productive while also allowing for remote management and monitoring. To understand how plants develop, our system collects data in real-time from a variety of sensors that measure things like temperature, humidity, and light levels, and then runs that data through neural networks. To further alleviate homeowners' workloads and guarantee plants get ideal care, robotic assistance is used to automate activities like watering, fertilizing, and pest management. Connecting to clouds allows customers to manage their garden from anywhere, get alerts when certain conditions are met, and get maintenance advice based on data. Through a series of battery tests, it can be observed that the proposed technique is superior to conventional gardening practices in terms of plant development and health. For eco-conscious green thumbs and busy gardeners, our system is the next big thing in smart home gardening, providing a practical and long-term solution to their problems. © 2024 IEEE.

关键词： Automation Plant health Remote monitoring Robotic assistance Sustainable gardening

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Smart Oral Health Monitor 3

Smart Oral Health Monitor

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Kamkar, Spandana Anilkumar Sanjay, Srushti Vaishnavi, P.S. Karthik, S. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331515911

Oral health is vital to overall well-being but is often overlooked due to inefficient monitoring tools and delayed diagnosis. This project presents a smart handheld device featuring a miniaturized camera for detecting oral issues like plaque, cavities, teeth discoloration and gum diseases in real-time. Using image processing and deep learning algorithms, the device provides precise feedback and actionable insights. Paired with a mobile application offering detailed analysis and recommendations, it empowers users to adopt proactive oral hygiene practices to prevent teeth disorder. © 2025 IEEE.

关键词： Electronic health record

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Detection of Wormhole Attacks Using the DCNNBiLSTM Model to Secure the MANET 3rd

Detection of Wormhole Attacks Using the DCNNBiLSTM Model to ...

引用

3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Rajalakshmi, B. Anandhi, R.J. Moorthi, K. Kavin, Balasubramanian Prabhu Dhanaraj, Rajesh Kumar New Horizon College of Engineering Ring Road Bellandur Post Bengaluru560103 India Department of Computational Intelligence College of Engineering and Technology SRM Institute of Science and Technology Chengalpattu District Tamil Nadu Kattankulathur603203 India Department of Data Science and Business Systems College of Engineering and Technology SRM Institute of Science and Technology Chengalpattu District Tamil Nadu Kattankulathur603203 India Pune India

ISBN: (纸本)9789819708918

The obvious response to an anytime, anywhere network in the current era of heterogeneous networks is MANET. It is a collection of wirelessly communicative mobile gadgets that operate on their own. In order to stop the damage that rogue network nodes could do, security in MANET is a crucial duty. A category of denial-of-service (DoS) assault, the wormhole attack bombards the network with numerous phoney packets and messages in an effort to exhaust its resources. Wormhole attacks are a particular type of network layer attack that imitates routing algorithms. Because of deep learning's exceptional performance in many detection and identification tasks, this research presents an approach for intelligent and effective attack detection in MANET. Our goal is to mimic a wormhole attack in a network environment with many wormhole tunnels. The CNN layer is used by the DCNNBiLSTM architecture to extract features from input data, and BiLSTMs are used to predict sequences. Five performance indicators were used to compare CNN, LSTM and DCNNBiLSTM under wormhole attack. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Long short-term memory

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Analysing Perception of Credibility of a Message in Online Social Network using SmartPLS 5

Analysing Perception of Credibility of a Message in Online S...

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5th IEEE India Council International Subsections Conference, INDISCON 2024

作者： Bhayyar, Akshata S Kiran, P. Ramaiah Institute of Technology Department of Computer Science and Engineering Bengaluru India Rns Institute of Technology Department of Cse Bengaluru India Visvesvaraya Technological University Belagavi India Rns Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376753

Nowadays online social networks are used to share information, but analyzing the information's credibility is imperative. The study's primary goal is to examine how 3 factors influence users' acceptance of messages in online social networks. 1. content likeability: which is a method of assessing the information value of social media content, 2. content legitimacy: the dissemination of false information poses a serious risk to our democracy since it can erode public confidence in reliable news sources, and 3. social media involvement: for many people, social media is their main source of information because it is a pervasive and ubiquitous digital communication tool. we use the dataset from Credibility Coalition which is a research community that promotes cooperative methods for comprehending the accuracy, reliability, and trustworthiness of online information, which is the cornerstone of civil society. Furthermore, the content credibility is analyzed using SmartPLS software, here sets of observable variables are used to measure the credibility of messages. © 2024 IEEE.

关键词： Message credibility Online Social Network PLS-SEM SmartPLS

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Despeckling SAR Images Using CNN-Based Approach Incorporating GAN and Gradient Estimation 27th

Despeckling SAR Images Using CNN-Based Approach Incorporatin...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Saha, Anirban Maji, Suman Kumar Department of Computer Science and Engineering Indian Institute of Technology Patna Bihar Patna801106 India

ISBN: (纸本)9783031783111

Synthetic Aperture Radar (SAR) technology stands at the forefront of capturing and processing Earth’s surface visuals due to its widespread acceptance across various organizations. However, the presence of unwanted random granular interference, commonly referred to as "speckle," poses a significant challenge in SAR data processing. Addressing this challenge, known as "despeckling," is crucial for extracting clear SAR visuals. This article introduces a novel CNN-based approach for despeckling SAR visuals contaminated with speckle. Our proposed model integrates a Generative Adversarial Network (GAN) module to estimate the distribution of contaminating speckle components from the input SAR data. Concurrently, a gradient estimator module captures the crisp changes in textural information within the input data. Subsequently, the input SAR data, the estimated noise distribution, and the extracted gradient undergo further processing through a deep convolutional module to generate a clean SAR visual. Unlike traditional methods that focus solely on learning the residual noisy component or the clean data, our proposed despeckling model learns the degradation pattern caused by noisy components while emphasizing gradient information, thereby capturing critical minute information. Experimental results demonstrate that our methodology significantly enhances despeckling performance compared to existing technologies in the literature. This research presents a promising step forward in advancing SAR visual despeckling techniques, with implications for improved data quality and interpretation in various applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Performance Assessment of Data Centric and Model Centric Approach for Premature Detection of Leaf Disease

Performance Assessment of Data Centric and Model Centric App...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Prabhu, A. Kumar, Sanjay Pundeer, Shivali Department Of Computer Science And Information Technology Bangalore India Noida Institute Of Engineering And Technology Department Of Mechanical Engineering Uttar Pradesh Greater Noida India Tula's Institute Department Of Computer Science And Engineering Dehradun India

ISBN: (纸本)9798350366846

After rice and maize, cassava is the third most common source of carbohydrates in the world. Recognizing the critical relationship between agricultural productivity and a country's economy, specialists have made significant investments in the early diagnosis of leaf diseases in order to protect the entire plant from degeneration. The abundance of data, improved resource availability, and improvements in learning algorithms are all credited with the recent achievements of Machine Learning (ML) models. In order to improve system efficiency, the traditional method for detecting leaf diseases uses a Model-Centric approach that optimizes hyperparameters. The emphasis has recently switched from model-entered to data-centered techniques, in which the model is kept constant, but dataset quality is increased by addressing issues like label consistency and systematic sampling of training data while choosing the right batches. The current paper comprises using confidence learning to locate and remove noisy labels from the Cassava Leaf Disease Classification dataset. The model is then fed with newly produced high-quality data, and the performance of the model-centric and data-centric approaches are contrasted. This paper intends to highlight the underappreciated potential for increasing system functionality using a data-centric approach rather than to minimize the significance of model-centric approaches. The findings indicate that the data-centric method leads to a performance enhancement of 7.84%. © 2024 IEEE.

关键词： Learning algorithms

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Performance enhancement Of GaAs and InP Based Multi-Junction Silicon Solar Cells using thickness and doping profile optimization

Performance enhancement Of GaAs and InP Based Multi-Junction...

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2023 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2023

作者： Roy, Arijit Bardhan Mounika, Mandla Kathi, Roshika Vishal, N Sai Srm Institute of Science and Technology University of Madras Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350337426

Among all the non-conventional sources of energy, solar energy has become increasingly popular in recent years. While there are several types of solar energy technologies available, including solar thermal and concentrated solar power but solar photovoltaic (PV) is the most widely used and preferred form. Due to this reason, increasing the efficiency of the solar cell is one of the key factors for establishing the PV technology as a primal source of energy. So multi-junction solar cell with proper bandgap matching is one of the best ways to achieve high efficiency through improving open circuit voltage and unified photon electron harvesting in a broad area of the spectrum. This paper is about the design and analysis of GaAs-Si and InP-Si based two types of multi-junction solar cells with the efficiency of 18 and 20 % respectively by optimizing doping profile and materials thickness through PC1D software. By utilizing PC1D software, we can design and analyze the performance of the solar cells in a virtual environment before producing physical prototypes, which can help to save time and resources. © 2023 IEEE.

关键词： Indium phosphide

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Blockchain based Identity Generator and Authenticator 3

Blockchain based Identity Generator and Authenticator

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Akhil, V. Hareesh, R. Yesudas, P.Y. Venugopalan, Anand Meera, K. Menon, Hema P. Sreepathy Institute of Management and Technology Department of Computer Science and Engineering Vavannor Kerala Palakkad India

ISBN: (纸本)9798331515911

The market today is flooded with the counterfeit branded products and there is currently no reliable solution to prevent the widespread flow of duplicates. However, blockchain technology offers a potential solution to stabilize the inflow of these counterfeits. As a decentralized and secure system, blockchain can assign a permanent, unique identity to each product, which can be accessed by anyone but cannot be altered, not even by the owner. This paper proposes the development of a blockchain-based system to assign a unique identity to every product, serving as a tag to distinguish between original and counterfeit items. This identity can also act as an ownership ID, enabling ownership transfer and helping to prevent the illegal trade of stolen or counterfeit products. © 2025 IEEE.

关键词： Blockchain

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Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views 38

Bridging Gaps: Federated Multi-View Clustering in Heterogene...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Xinyue Ren, Yazhou Xu, Jie Lin, Fangfei Pu, Xiaorong Yang, Yang School of Computer Science and Engineering University of Electronic Science and Technology of China China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China

Recently, federated multi-view clustering (FedMVC) has emerged to explore cluster structures in multi-view data distributed on multiple clients. Many existing approaches tend to assume that clients are isomorphic and all of them belong to either single-view clients or multi-view clients. While these methods have succeeded, they may encounter challenges in practical FedMVC scenarios involving heterogeneous hybrid views, where a mixture of single-view and multi-view clients exhibit varying degrees of heterogeneity. In this paper, we propose a novel FedMVC framework, which concurrently addresses two challenges associated with heterogeneous hybrid views, i.e., client gap and view gap. To address the client gap, we design a local-synergistic contrastive learning approach that helps single-view clients and multi-view clients achieve consistency for mitigating heterogeneity among all clients. To address the view gap, we develop a global-specific weighting aggregation method, which encourages global models to learn complementary features from hybrid views. The interplay between local-synergistic contrastive learning and global-specific weighting aggregation mutually enhances the exploration of the data cluster structures distributed on multiple clients. Theoretical analysis and extensive experiments demonstrate that our method can handle the heterogeneous hybrid views in FedMVC and outperforms state-of-the-art methods. The code is available at https://***/5Martina5/FMCSC. © 2024 Neural information processing systems foundation. All rights reserved.

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MLModeler5 @ Causal News Corpus 2023: Using RoBERTa for Casual Event Classification 6

MLModeler5 @ Causal News Corpus 2023: Using RoBERTa for Casu...

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6th Workshop on Challenges and Applications of Automated Extraction of Socio-Political Events from Text, CASE 2023

作者： Bhatia, Amrita Thomas, Ananya Jain, Nitansh Bedi, Jatin Computer Science and Engineering Department Thapar Institute of Engineering and Technology India

ISBN: (纸本)9789544520892

Identifying cause-effect relations plays an integral role in the understanding and interpretation of natural languages. Furthermore, automated mining of causal relations from news and text about socio-political events is a stepping stone in gaining critical insights, including analyzing the scale, frequency and trends across timelines of events, as well as anticipating future ones. The Shared Task 3, part of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE @ RANLP 2023), involved the task of Event Causality Identification with Causal News Corpus. We describe our approach to Subtask 1, dealing with causal event classification, a supervised binary classification problem to annotate given event sentences with whether they contained any cause-effect relations. To help achieve this task, a BERT based architecture - RoBERTa was implemented on four different datasets with the main difference being the inclusion and exclusion of both stopwords and various abbreiviations/acronyms present in the dataset. The results of this model are validated on the dataset provided by the organizers of this task. We achieved a rank of 8 with an F1 Score of 0.7475 on Dataset 1 (Removed Stop Words and Replaced Abbreviations). © CASE 2023 - Proceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-Political Events from Text, associated with 14th International Conference on Recent Advances in Natural Language Processing, RANLP 2023.

关键词： Extraction

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