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检索条件"任意字段=7th International Conference on Computing in Engineering and Technology, ICCET 2022"

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Attack Detection on Internet of things Devices using Machine Learning Techniques 7

Attack Detection on Internet of Things Devices using Machine...

引用

7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Sharma, Yatharth Kumar, Vansh Chaudhary, Himanshi KIET Group of Institutions Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350397253

Today, the exponential growth of technology and the boom in connection demand has resulted in significant growth of Internet of things (IoT) devices. In light of the enormous increase in smart devices, secure communication between them has become a major challenge due to an increase in cyberattacks. Adding hardware-based security is challenging due to the complex architecture of IoT devices and limited computation ability. Since IoT devices produce enormous amount of data, machine learning (ML) can be utilized to detect attacks on IoT devices. this study proposes some Machine Learning models to detect and prevent Botnet IoT attacks. the Network-based Detection of Bait-and-Switch IoT Attacks (N-BaIoT) dataset is used to train the models. the performance of models is analyzed using precision, recall and F1-score by carrying out multiclass classification. Experimental result shows that some of the proposed models are efficient in detecting botnet attacks with 99% accuracy. the model can also extend to use complete dataset and new ML techniques. © 2023 IEEE.

关键词： Internet of things

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Deep Learning based Money Detection System for Visually Impaired Person 7

Deep Learning based Money Detection System for Visually Impa...

引用

7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Jawale, Aniket Patil, Kanchan Patil, Aniruddha Sagar, Siddhesh Momale, Akash Sanjivani College of Engineering Department of Information Technology Kopargaon India

ISBN: (纸本)9798350397253

In recent times, visually challenged people are facing challenges in completing their daily tasks. One of the issues is to use bank money to make purchases and also to deal with financial fraud. In light of this, this study has developed a novel system to automatically identify the currency and provide output in audio format. the proposed system is composed of various modules. Using a camera, the data is collected and given as input to the first module. Following pre-processing, the deep learning model will determine whether the currency is genuine or not, and if it is, it will classify the currency. In order to do this, this study has examined various deep learning techniques. For this, many deep learning object identification and classification algorithms are examined before choosing the one that performs the task the best. In the following module, NLP is utilized to extract text from the currency and create the text. the created text will be converted into audio with the aid of the Text-to-Speech module, and the audio output will be provided to blind users via a Raspberry Pi connected audio device. © 2023 IEEE.

关键词： Natural language processing systems

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Detection of Fake Hotel Reviews Using ANFIS and Natural Language Processing Techniques 7

Detection of Fake Hotel Reviews Using ANFIS and Natural Lang...

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7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Rajesh, K.P. Prabu Nallasivam, Manikanda Sakthi Gokul Rajan, C. Sherlin Paul, P.S. Hari Kumar, S. Dharun, V.S. Sri Krishna College of Technology Electronics & Communication Engineering Coimbatore India Sri Krishna College of Technology Electrical & Electronics Engineering Coimbatore India Noorul Islam Centre for Higher Education Fire Technology & Safety Engineering Kumaracoil India Noorul Islam Centre for Higher Education Aircraft Maintenance Engineering Kumaracoil India Jaya Institute of Technology Electronics & Communication Engineering Chennai India

ISBN: (纸本)9798350372816

the wide spread of fake reviews presents a considerable threat to the correctness of online review systems which tends to undermining both business reputations and consumer trust. this research introduces an innovative method for identifying fraud hotel reviews through the use of an Adaptive Neuro-Fuzzy Inference System integrated with supervised learning algorithms. By integrating ANFIS with Random Forest, Support Vector Machines and Logistic Regression classifiers, we trained our model on a well-annotated dataset of hotel reviews. Our empirical analysis demonstrates that the ANFIS-based models significantly outperform traditional classifiers in terms of accuracy, recall, precision, F1 score and ROC-AUC metrics. It is observed that the ANFIS-SVM model achieved 92.5% accuracy, 91.8% precision. It also gives 93.2% recall, 92.5% F1 score and an ROC-AUC value of 0.95. these findings underscore the superior performance of our approach in categorizing authentic reviews from fake ones. Also the results show the impact of ANFIS-based models to improve the reliability of online review platforms, ultimately benefiting both businesses and consumers by enhancing the trustworthiness of user-generated content. © 2024 IEEE.

关键词： Adversarial machine learning

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Developing a YOLO based Object Detection Application using OpenCV 7

Developing a YOLO based Object Detection Application using O...

引用

7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Ponika, Mannem Jahnavi, Kopalli Sridhar, P.S.V.S. Veena, Kavuri Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation A.P. Vaddeswaram522502 India

ISBN: (纸本)9781665464086

Computer Vision is a technology in which computers are trained using machine learning and deep learning algorithms to recognize and process objects from video/images in the same way that humans do. Since 2012, Convolutional Neural Network and its variations have experienced exceptional improvement, making object detection a crucial application of image processing. the main issue with CNN is that this consumes more time to instruct/train as it has to analyze two thousand regions per picture so for this task in real time it needs approximately 47 sec per picture. this article has employed the YOLO real-time object identification approach to train our machine learning model. YOLO is one of the best CNN representatives that defies new way for object recognition, which is simple and highly efficient. Unlike existing algorithms, YOLO looks at the picture entirely by predicting the bounding boxes and class probabilities for these boxes using neural networks. this results in a faster detection of the image than other algorithms. © 2023 IEEE.

关键词： Object detection

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Optimised Home Electricity Management using Machine Learning 7

Optimised Home Electricity Management using Machine Learning

引用

7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Arunkumar, M. Devadharshini, S. Dept of Information Technology IFET College of Engineering Villupuram India

ISBN: (纸本)9781665464086

the interactive home automation system that is based on the Internet of things (IoT) is a cutting-edge technical breakthrough that allows remote control and monitoring of home appliances in addition to the actual purchase of items. the Internet of Objects (IoT) aims to bring everything in the world together under a unified infrastructure, which will give us the ability to manage the things that are around us and provide us with continual information on the state of those things. the fundamental objective of the study is to provide a recommendation for an effective Internet of things implementation that may be put to use to carry out remote monitoring and control of household appliances. the user interface for this system is implemented in a manner that makes advantage of portable devices. In addition, it provides control of home appliances using mobile devices by employing Wi-Fi as the communication protocol and the Raspberry Pi Uno as the controller. the home user will be responsible for following the system as it travels over the Internet via the web-based user interface. For the purpose of anticipating people's usage of power and so enabling them to become less reliant on it, machine learning is being utilised here. © 2023 IEEE.

关键词： Python

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t-path Based Reliable and Real-time Routing in Battery-Free Wireless Sensor Network 23

t-path Based Reliable and Real-time Routing in Battery-Free ...

引用

23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on Data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Chen, Kunyi Gao, Hong Cheng, Siyao Chen, Quan Li, Jianzhong Harbin Institute of Technology China Guangdong University of Technology China

ISBN: (纸本)9781665494571

Wireless Sensor Networks are deployed to sense data from the physical world and transmit these data to the server(s). To reduce the maintenance cost of replacing batteries, Energy-Harvesting nodes are developed which can get recharged from Solar Power, Radio Frequency, and other environmental energy resources. We term the network consist of energy-harvesting nodes as Battery-Free WSNs (BF-WSNs). Environmental energy is inherently time-varying and exhibits a high degree of heterogeneity. these features bring challenges to routing design. In this paper, we focus on the reliability and real-time issues and propose an opportunistic strategy as time synchronization is hard and expensive to reach in BF-WSN. We first model BF-WSNs as a directed weighted multigraph and propose a novel definition called t-path to describe the time-varying availability feature of BF-nodes. Further, we show that the problem is NP-Hard. We present a centralized approximate algorithm and prove that the ratio bound is 2 under two conditions. this paper also presents a distributed algorithm. Comprehensive simulations have been performed to evaluate the performance of the proposed algorithms and the results show that the efficiency of our algorithms. © 2021 IEEE.

关键词： Wireless sensor networks Simulation Reliability theory Approximation algorithms Routing Reliability engineering Real-time systems

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Usability study of mobile healthcare application for the elderly 9

Usability study of mobile healthcare application for the eld...

引用

Joint 9th international conference on Digital Arts, Media and technology with 7th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications engineering, ECTI DAMT and NCON 2024

作者： Kim, Su Bin Tae, Hyun Chul Lee, Chang Gi Moon, Jae Kyeong Yoo, Oui Sik Kim, Jong Hyun Korea Institute of Industrial Technology Digital Healthcare R&d Department Cheonan Korea Republic of Institute of Technology ECEN Inc. Seoul Korea Republic of

ISBN: (纸本)9798350318241

Various healthcare services and mobile applications tailored for the elderly have surfaced, yet studies related to UI design and usability do not significantly differ from those targeting the general public. Consequently, general healthcare services pose inconveniences and difficulties for elderly users, resulting in a multitude of operational errors. Hence, the primary objective of this study is to evaluate usability with the aim of enhancing mobile applications that are elderly-friendly. By comparing usability evaluation results before and after improvements to the healthcare application, design elements influencing the usability of elderly-friendly applications were identified. the usability evaluation was broadly categorized into an objective evaluation based on a performance matrix and a subjective evaluation employing the System Usability Scale (SUS). the findings of this study validate that providing assistance, increasing font size, introducing sound feedback, and incorporating a preparatory phase to enhance elderly users' cognition significantly contribute to overall usability improvements for the elderly. these research outcomes are expected to be applied in the future development of diverse applications and service research specifically tailored to the elderly. © 2024 IEEE.

关键词： Mobile computing

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Crop Advancement with Machine Learning 7

Crop Advancement with Machine Learning

引用

2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Pawar, Rajendra Bisht, Vishnu Pratap Pandey, Ritika Lodhe, Urvik Pawar, Aryan Chouksey, Aaryan Jatale, Sarthak School of Computer Engineering & Technology Mit World Peace University Maharashtra Pune India

ISBN: (纸本)9798350304268

Crop prediction is a crucial task in agriculture that can help farmers and agricultural researchers decide which crops to grow based on environmental variables such as soil type, temperature, rainfall, and humidity. In this project, we propose a data-driven approach for crop prediction using K-means clustering and classification algorithms. We use K-means clustering to group the data into different clusters based on similarities in the environmental variables, and then we use a classification algorithm such as decision trees or support vector machines to predict the best crop to grow in a particular location. this approach can help farmers and agricultural researchers make more informed decisions about crop selection and improve crop yields. We validate our model by comparing historical data on crop yields to our predictions and conducting experiments by growing different crops in different locations. Overall, this project demonstrates the potential of machine learning algorithms for crop prediction in agriculture. © 2023 IEEE.

关键词： Agriculture Classification algorithm Crop prediction Environmental variables K-means clustering

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Image Restoration Based on Improved Generative Adversarial Networks 7

Image Restoration Based on Improved Generative Adversarial N...

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7th international conference on Image, Vision and computing, ICIVC 2022

作者： Li, Yongsen Meng, Jiana Yu, Yuhai Wang, Cunrui Guan, Zhongyuan Dalian Minzu University School of Computer Science and Engineering Dalian China

ISBN: (数字)9781665467346

ISBN: (纸本)9781665467346

Image restoration aims to recover image information from broken or missing regions, which plays an important role in the field of computer vision. there has been a major breakthrough in image restoration by using deep neural network models, but it is not effective in restoring above high-resolution image details. In order to solve this problem, this paper proposes a generative adversarial network with embedded channels and spatial attention, simultaneously, makes the network obtain a larger perceptual field by increasing the size of the expanded convolution, which leads to induce the network to learn the image details and broken edges better, so as to improve the restoration effect on details. the network proposed in this paper is trained and learned on a public datasets, and it is shown in the experimental results that the effect of the method on image restoration is improved to a certain extent. © 2022 IEEE.

关键词： Image reconstruction

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Semantic based logistics data exchange model 7

Semantic based logistics data exchange model

引用

7th international conference on Intelligent computing and Signal Processing, ICSP 2022

作者： Chen, Yujun Fu, Lanqi Qiu, Jianlong Fu, Deqian School of Automation and Electrical Engineering Linyi University Linyi China School of Science & Engineering University of Limerick Limerick Ireland Linyi University School of Automation and Electrical Engineering Linyi276005 China

ISBN: (纸本)9781665478571

In recent years, with the rapid development of various logistics industries, all kinds of logistics information systems and platforms have developed synchronously and rapidly. Due to its industry characteristics, there are natural rigid requirements of multi-source, heterogeneous constraints and business collaboration and sharing in the data exchange between logistics information systems. the data exchange based on semantic guidance will become the core foundation of the intelligent logistics information system platform. therefore, this paper studies the knowledge sharing in the field of logistics, and studies the logistics data exchange model based on semantic guidance. Firstly, it analyzes and studies the standards in the field of logistics at home and abroad. then, through the expansion of logistics semantics, the technology of ontology, metadata and Knowledge Graphs is used for annotation. According to the system theory, a three-tier standard system model is proposed and constructed to guide the construction of Knowledge Graphs for data exchange of multi-source heterogeneous logistics information system. the model integrates the metadata and ontology standards related to the field at home and abroad, reveals the important idea of taking the metadata standard as the core in the construction of Knowledge Graphs, makes the Knowledge Graphs constructed in the field realize semantic standardization, lays the foundation for knowledge sharing and interoperability, and provides semantic standards for the data exchange of multi-source and heterogeneous logistics information systems. © 2022 IEEE.

关键词： Ontology

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