检索结果-内蒙古大学图书馆

2nd International Conference on Applied Artificial Intelligence and computing, ICAAIC 2023

作者： Kuchimanchi, Akhil Vagdevi, Maddineni Reddy, Meghana Avugaddi, Ganesh Kumar, Sanjay S Amrita School of Engineering Department of Electronics and Communication Engineering Kerala India Amrita School of Computing Department of Computer Science Kerala India

ISBN: (纸本)9781665456302

Security problems have traditionally arisen when messages are exchanged through unsecured channels. Despite the fact that there are various strategies for encrypting messages, attempts on gathering Information continues to be sent. Furthermore, the fact that the vast majority of applications store their data in centralized databases is grounds for worry. Blockchain technology offers new ways to ensure data privacy, censorship resistance, immutability, and decentralization. Each node in the network receives a copy of the data sent directly into the Blockchain by users. Using their private key, only approved persons may access the data on the blockchain. It eliminates the need for trustworthy intermediates. Because the system is totally decentralized, users may send messages with confidence. This software shows how to hash messages and mine them in the blockchain. To provide decentralized storage and quick lookup, we'll use a distributed hash table (DHT) and blockchain. This paper describes how to build a decentralized chat program. © 2023 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

A Cloud-Based Prediction and Self-Diagnosis System for PCOS Using Machine Learning Models

A Cloud-Based Prediction and Self-Diagnosis System for PCOS ...

引用

Virtual International Conference on Futuristic Communication and Network Technologies, VICFCNT 2021

作者： Mittapalli, Jishnu Saurav Khanna, Kush Mutha, Jainav Amit Nair, Saranya School of Computing Science and Engineering Vellore Institute of Technology Chennai India School of Electronics Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789811983375

Polycystic ovary syndrome (PCOS) is one of the common medical conditions observed in women nowadays wherein the male sex hormone, androgen, produced by the ovaries is higher than normal which causes problems in the release and development of eggs, also leading to enlargement of the ovaries. This, in turn, can further cause many other diseases like Type-II diabetes and infertility. In many cases, women are not able to identify PCOS as the main issue which is why this paper is aimed at developing a tool based on sophisticated machine learning models such as random forests, Naive Bayes, KNN, to predict with high accuracy if a woman is suffering from PCOS, based on the symptoms faced by her and other factors such as age and family medical history, and this paper also aims to send this data for medical supervision via a cloud based web application. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing Driver Safety Using Deep Learning Models 5

Enhancing Driver Safety Using Deep Learning Models

引用

5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Shokeen, Ishaan Uday, Krishnendu M Venugopalan, Manju Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electronics and Computer Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

This proposed work emphasizes the critical role of eye movement detection, particularly open and closed eye states, in identifying drowsiness, providing a clear focus on the project's main technological approach and its importance for road safety. The proposed model ensures constant checks on the driver's safety and provides necessary observations on their behavioral activities. To ensure effective drowsiness detection, different methodologies such as Convolutional Neural Networks (CNN), customized Neural Networks and Recurrent Neural Networks (RNN) were employed for image processing. The CNN model achieved the best results. Two different data sources from Kaggle provided various training images for the model to operate in different scenarios. The proposed model aims to achieve accurate predictions across various case scenarios and to develop the most efficient model for real-time detection and alert systems, ensuring driver safety. Among the models our system gives the best result using CNN reporting an accuracy of 98.53%, and precision, recall and F-score of 98.71%, 98.04% and 98.37% respectively. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

NLP-Based SMS Spam Detection Using Ensemble Learning 4

NLP-Based SMS Spam Detection Using Ensemble Learning

引用

4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Rajalakshmi, M. Rengaraj, R. Karthikeyan, M. Aruna, S. Srm Institute of Science and Technology School of Computing Dept of Computing Technologies KTR Campus Chennai India Sri Sivasubramaniya Nadar College of Engineering Dept of Electrical and Electronics Engineering Kalavakkam Chennai India Srm Insitute of Science & Technology Dept of Electrical and Electronics Engineering Ramapuram Chennai India

ISBN: (纸本)9798331508845

In today's environment, spam text messages are a common issue and a source of frustration for users. This study suggests a novel method for identifying SMS spam that makes use of natural language processing (NLP), multiple model creation, and ensemble learning. The suggested approach entails pre-processing the data using natural language processing (NLP) techniques including lemmatizing, stemming, and stopword removal in order to extract pertinent features from the text data and convert it into numerical representations. The suggested approach's effectiveness was assessed using an actual dataset and contrasted with conventional machine learning algorithms like SVM and Naïve *** altered data is used to train the numerous classifiers, and an ensemble learning technique is then used to combine their predictions and produce a more accurate outcome. More precision was provided by the final model than by conventional models. In order to lessen the quantity of spam messages that users get, the research's conclusions may be helpful in the development of an efficient spam SMS detector. Furthermore, the model was hosted using the streamlit web app framework on the cloud-based platform Railway, making it instantly accessible to users without the need to install any software. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Regularized Hybrid Deep Learning for DDoS Attack Prediction in Software Defined Internet of Things (SD-IoT) 3

Regularized Hybrid Deep Learning for DDoS Attack Prediction ...

引用

3rd International Conference on Power, Control and computing Technologies, ICPC2T 2024

作者： Deepthy, J. Radhika, N. Denny, Alosh Department of Electronics & Communication Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India School of Engineering Ernakulam Cochin University of Science and Technology Division of Computer Science and Engineering India

ISBN: (纸本)9798350349207

The availability of online resource is seriously threatened by the fatal DDoS (Distributed Denial of Service) attacks, which can earnestly break up a targeted network or website by flooding it with traffic. DDoS attacks can use a variety of tactics and can be launched from different points around the world, making it difficult to predict when they will occur. In order to improve accuracy by minimizing overfitting of data in CNN, this work proposes an efficient method by combining CNN with Bi-LSTM for predicting DDoS attacks based on a modified loss function with regularization. We test our method on the CICDDOS2019 dataset of network traffic and demonstrate that it can accurately and successfully predict DDoS attacks with an accuracy of 99.8% and an F-score of 97.29%. © 2024 IEEE.

关键词： Denial-of-service attack

来源：评论

学校读者我要写书评

暂无评论

Enhancing Content Based Collaborative Filtering Recommendations Using Weighted Word Embeddings 15

Enhancing Content Based Collaborative Filtering Recommendati...

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Reddy, Pennabadi Devendra Reddy, K. Satya Sampath Gnaneswarachary, P. Reddy, P. Lakshmikanth Venugopalan, Manju Vekkot, Susmitha Nair, Priyanka C. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350370249

In the current era of excessive information, recommendation systems are essential for assisting users to discover relevant content efficiently, ensuring high levels of user satisfaction and engagement. The proposed work focuses on enhancing content-based collaborative filtering using advanced word embed ding techniques like Word2Vec, GloVe, and BERT to capture semantic relationships between items. This study utilizes weighted word embeddings, derived from user-provided ratings, to enhance the recommendation system's capability in identifying and predicting user preferences. By incorporating these weighted embeddings, our approach seeks to improve the precision and relevance of recommendations, eventually increasing user satisfaction. The study conducts a comparative analysis of these word embedding techniques based on cosine similarity scores. Among the three weighted embeddings, GloVe shows the highest cosine similarity score for selected users UserId-68 and UserId-122. © 2024 IEEE.

关键词： BERT collaborative filtering GloVe Recommendation Systems Word embeddings Word2Vec

来源：评论

学校读者我要写书评

暂无评论

ECO-Guard: An Integrated AI Sensor System for Monitoring Wildlife and Sustainable Forest Management 8th

ECO-Guard: An Integrated AI Sensor System for Monitoring Wil...

引用

8th International Conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Nikhilesh Krishna, Ch. Rauniyar, Avishek Kireeti Sai Bharadwaj, N. Raj, Sujay Bharath Valsan, Vipina Suresh, Kavya Ravikumar Pandi, V. Sathyan, Soumya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9789819994885

In India, 12% of wild animals are threatened with extinction, and human activity threatens more than 80% of East and Southeast Asia's wetlands. Every year, an estimated 400 people are killed by wildlife in India, with Maharashtra alone seeing 88 human deaths and 9258 cattle killings by tigers in 2020. The ECO-Guard is an intelligent device which uses the You Only Look Once (YOLO) model and various sensors and machine learning models with algorithms that support and promote forest monitoring and tourist guidance in forest areas addressing Sustainable Development Goal (SDG) 12 (Life on Land). The paper presents a linear chain-like mesh of devices speeded across bio-area which are used in correlation for intended surveillance. Each device can multimodule apps and tally their sightings automatically to analyze coherence of the situation with the forest rangers, organizations, and figures. It catches from the information, the ill substitutes like human spotting with beasts. It suggests that the advice can be further put into practice by constantly monitoring any closeness of alternatives that match to create an unfavorable combination. Also having to help detect many forest crimes, some including illegal logging, preventing, and deforestation on a large scale. This paper highlights a novel ECO-Guard, an intelligent and autonomous monitoring system which includes artificial intelligence (AI)-enhanced strategies and algorithms to conserve wildlife and forest ecosystems by integrating various hardware and software components. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deforestation

来源：评论

学校读者我要写书评

暂无评论

Advancements and applications of position-sensitive detector(PSD): a review

引用

Optoelectronics Letters 2024年第6期20卷 330-338页

作者： Shaher Dwik G.Sasikala S.Natarajan Department of Electronics&Communication Engineering School of Electrical&CommunicationVel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India Department of Computer Science and Engineering Amrita School of ComputingAmrita Vishwa VidyapeethamChennai 601103India1

This paper presents a review of the position-sensitive detector(PSD) sensor, covering different types of PSD and recent works related to this field. Furthermore, it explains the theoretical concepts and provides information about its structure and principles of operation. Moreover, it includes the main information about the available commercial PSDs from different companies, along with a comparison between the common modules. The PSD features include high position resolution, fast response, and a wide dynamic range. These features make it suitable for various fields and applications, such as imaging, spectrometry, spectroscopy and others.

关键词： Advancements and applications of position-sensitive detector a review PSD

来源：评论

学校读者我要写书评

暂无评论

Blockchain Technology and Smart Contracts for Transformative Energy Trading 4

Blockchain Technology and Smart Contracts for Transformative...

引用

4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Valsan, Vipina Sai, P. Sarvan Sri Mukesh, Koraganji Raju, Abbaraju Sree Tharun Sindukuri, Teja Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electrical and Electronics Engineering Amritapuri India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798331518981

The trend in renewable energy calls for safe peer-to-peer(P2P)energy trading in a microgrid. Blockchain technology (BCT) addresses this by providing a secure, tamper-proof ledger that enables transparent and sustainable energy exchange. This paper explores how smart contracts can make energy trading more efficient for microgrid users while securely recording all transactions for easy verification. The architecture designed for these smart contracts ensures reliable mechanisms such as simultaneous submission of bids, user registration, order placement and sorting, and automatic execution of the trading system. The proposed smart contract-based energy trading system improves energy efficiency and flexibility while supporting decentralized control within the microgrid. BCT directly contributes to the Sustainable Development Goal (SDG) 7 (Affordable and Clean Energy) by promoting this technology, increasing renewable energy in the global energy mix, and improving energy efficiency. Furthermore, BCT supports SDG 9 by creating a resilient and sustainable infrastructure through technological innovation. This paper provides a detailed analysis of the P2P trading scenario, smart contract pseudocode, and visualization of the smart contract implementation in Solidity, aiming to navigate the real-world application of P2P energy markets. © 2024 IEEE.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

IoT-Enabled Solar-Powered Water Trash Collector with Conveyor Belt 8th

IoT-Enabled Solar-Powered Water Trash Collector with Conveyo...

引用

8th International Conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Devapriya, S. Krishna, M. K. Nandana Gondi, Bhavika Vinuha, Chinta Pandi, V. Ravikumar Sathyan, Soumya Valsan, Vipina Suresh, Kavya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9789819994854

This study presents a novel solution in the form of a solar-powered water trash collector, designed specifically for deployment in backwaters and smaller water bodies, to address the critical issues of water pollution and waste management. Focusing on the challenges faced by national rivers, including hazardous polymers, plastic waste, and decaying organic matter, the collector operates under IoT-enabled Bluetooth control using mobile application, incorporating a conveyor belt mechanism for efficient waste collection, particularly targeting prevalent plastic debris in freshwater environments. A notable feature of this solution is its integration of solar power, ensuring self-sustained operation and reduced environmental impact, aligning with the imperative for eco-friendly waste management technologies. Bluetooth control enhances adaptability for smaller water bodies, crucial for precise navigation. The core objective is to provide an effective solar-powered water trash collector tailored for backwaters, addressing the escalating waste production due to population growth. Additionally, a comprehensive waste management approach is proposed, facilitated by an Android application for seamless navigation of integrated trash bins. In conclusion, this study offers an innovative approach to tackle water pollution and waste management, introducing a solar-powered collector uniquely equipped to address backwater ecosystems’ specific challenges while promoting sustainability and environmental well-being. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bluetooth

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：