7th International Conference on Computing in Engineering & Technology (ICCET 2022)最新文献_第3页

Malware analysis in mobile devices 移动设备中的恶意软件分析

7th International Conference on Computing in Engineering & Technology (ICCET 2022) Pub Date : 1900-01-01 DOI: 10.1049/icp.2022.0604

P. Agarwal, A. Nair, S. Jaiswal, N. Batra, P. Grover

引用次数: 0

A scalable home automation IoT ecosystem using open source technologies 使用开源技术的可扩展家庭自动化物联网生态系统

7th International Conference on Computing in Engineering & Technology (ICCET 2022) Pub Date : 1900-01-01 DOI: 10.1049/icp.2022.0615

A. Phand, R. Bangar, R. Dani, J. Lohokare

{"title":"A scalable home automation IoT ecosystem using open source technologies","authors":"A. Phand, R. Bangar, R. Dani, J. Lohokare","doi":"10.1049/icp.2022.0615","DOIUrl":"https://doi.org/10.1049/icp.2022.0615","url":null,"abstract":"Home Automation provides remote access to appliances and devices in a home via the internet. While this is easily achievable on a small scale, the challenge is making the system deployable in a manner that is scalable across a whole city in a developing nation. This paper proposes an end-to-end home automation suite with modular open source components. Our backend supports multiple IoT protocols and is horizontally scalable. We implement a ‘Rules engine’ that analyzes real-time data and triggers various actions pre-determined by users. We also implement energy monitoring for judicial energy consumption in a home. Our system, when deployed, can handle data coming from homes for an entire city. Each user will have remote control of devices in his home through an android application. The user will also be able to define rules to trigger actions in his home via a web dashboard. The architecture used ensures that the system scales as the data traffic increases. Our experiments on the prototype system show low latency for real time analytics even when there are large simultaneous data streams. Our main feature contributions are the modular and dynamic nature of our system, scalability, and real time analytics for quick decision making.","PeriodicalId":224655,"journal":{"name":"7th International Conference on Computing in Engineering & Technology (ICCET 2022)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132640237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

FPGA based area efficient implementation of DDR SDRAM memory controller using verilog HDL 基于FPGA的区域高效DDR SDRAM内存控制器的verilog HDL实现

7th International Conference on Computing in Engineering & Technology (ICCET 2022) Pub Date : 1900-01-01 DOI: 10.1049/icp.2022.0646

S. Pradeep, B. Dappuri

引用次数: 1

Catch me if you can: achieving complete internet anonymity using open source technologies 如果你能抓住我:使用开源技术实现完全的互联网匿名

7th International Conference on Computing in Engineering & Technology (ICCET 2022) Pub Date : 1900-01-01 DOI: 10.1049/icp.2022.0606

R. Bangar, V. Narkar, A. Phand, J. Lohokare

引用次数: 0

Fuzzy logic based speed enhancement of electric vehicle 基于模糊逻辑的电动汽车速度提升

7th International Conference on Computing in Engineering & Technology (ICCET 2022) Pub Date : 1900-01-01 DOI: 10.1049/icp.2022.0639

A. Singh, P. Choudekar, .. Ruchira

引用次数: 0

An LSTM network-based real-time noise suppression tool for conducting audio and video conferences 基于LSTM网络的实时噪声抑制工具，用于召开音视频会议

7th International Conference on Computing in Engineering & Technology (ICCET 2022) Pub Date : 1900-01-01 DOI: 10.1049/icp.2022.0600

A. Gosain, A. Sood

{"title":"An LSTM network-based real-time noise suppression tool for conducting audio and video conferences","authors":"A. Gosain, A. Sood","doi":"10.1049/icp.2022.0600","DOIUrl":"https://doi.org/10.1049/icp.2022.0600","url":null,"abstract":"Background: A real-time noise suppression tool is an essential requirement of today's time. By using such a tool, audio and video conferences can be conducted noiselessly, even in the absence of a quiet office environment. The tool's methodology includes a signal pre-processing phase and a network computations phase followed by statistical analysis. In the pre-processing phase, various signal transformation methods are analyzed comparatively. Then, the most suitable one is applied to the input audio signal to extract its main frequencies. Afterward, these extracted features are used for training the deep neural network. Results: Many sample audio signals are fed to this tool, and it gives their cleaned versions with suppressed background noise. Statistical analysis for particular sample audio shows the amplitude difference between the two speech signals graphically. Conclusion: It is found that the Short-time Fourier Transform method is better amongst all other methods for extracting signal features due to its lossless nature. The backbone of the Long Short-term Memory Network facilitates this tool to retain any extended sequential information. An overall T statistic value of -94.920 for sample audio 1 and -208.843 for sample audio 2 solidifies the confidence in the accuracy of this tool.","PeriodicalId":224655,"journal":{"name":"7th International Conference on Computing in Engineering & Technology (ICCET 2022)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116590172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Smart tour advisor using machine learning and natural language processing 使用机器学习和自然语言处理的智能导游

7th International Conference on Computing in Engineering & Technology (ICCET 2022) Pub Date : 1900-01-01 DOI: 10.1049/icp.2022.0591

V. Walunj, S. Sharma, A. Wagh, U. Solanki, J. Mahajan

{"title":"Smart tour advisor using machine learning and natural language processing","authors":"V. Walunj, S. Sharma, A. Wagh, U. Solanki, J. Mahajan","doi":"10.1049/icp.2022.0591","DOIUrl":"https://doi.org/10.1049/icp.2022.0591","url":null,"abstract":"In the context of recommendation systems, deciding whether to prioritize accuracy above computing time or vice versa is difficult, which is why many researchers prefer hybrid recommendation systems. Researchers are currently working to generate appropriate and correct suggestions to support the use of the ontology but cannot fully use it due to a lack of methodologies. The pure problem of new user cold start, which arises when the system lacks information about the new user, is one of the main challenges of recommender systems that many academics are concerned about [2]. It is advocated in this paper to develop a smart tour advisor by using content-based filtering and item-based collaborative filtering for recommending Hotels, Restaurants, and Tourist Attractions to tourists. It focuses on solving the cold start problem by asking a few questions and analyzing user preferences [3,5,7], eventually making personalized recommendations. Before visiting any destination, any tourist must go through many complex parameters like booking a hotel, looking for the best tourist attractions, and selecting restaurants by seeing their ratings, reviews, and attributes, which is very tedious work. So, it is an end-to-end travel planner web application that will help to make better decisions for your travel routine for any destination.","PeriodicalId":224655,"journal":{"name":"7th International Conference on Computing in Engineering & Technology (ICCET 2022)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126422684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling and design of single-phase PV inverter with MPPT algorithm applied to standalone PV system 基于MPPT算法的单相光伏逆变器建模与设计

7th International Conference on Computing in Engineering & Technology (ICCET 2022) Pub Date : 1900-01-01 DOI: 10.1049/icp.2022.0635

B. Panda, A. Sharma, S. Nandi, L. Nanda, C. Jena

引用次数: 0

SentNet : a system to recognise human sentiments in real time SentNet:一个实时识别人类情感的系统

7th International Conference on Computing in Engineering & Technology (ICCET 2022) Pub Date : 1900-01-01 DOI: 10.1049/icp.2022.0611

A. Aggarwal, L. Sehgal, N. Aggarwal

{"title":"SentNet : a system to recognise human sentiments in real time","authors":"A. Aggarwal, L. Sehgal, N. Aggarwal","doi":"10.1049/icp.2022.0611","DOIUrl":"https://doi.org/10.1049/icp.2022.0611","url":null,"abstract":"Detecting sentiments through videos would prove to be an important part as nowadays, the conferences, gatherings are being held online due to global pandemic. Teaching has now turned into e-teaching modes. Even doctors are consulting the patients through online video conferencing. So, automated detection of the facial sentiment of the other person becomes the foremost concern. This paper proposes a real-time sentiment analysis end-to-end system using Raspberry Pi to capture videos that are passed into the proposed model after extracting image frames. The model used in the simulator has been trained using the MobileNet model. A distinguished dataset is used which contains features to analyze sentiments of people belonging to multiple ethnicities. The dataset consists of videos, so for training the deep learning model, frames are extracted from the videos. After that, faces are detected from these frames using two methods namely, Haar cascade(OpenCV) and MTCNN. Three different datasets are discussed: PU dataset, FER2013 cleaned dataset and Mixed (PU + FER13 cleaned) dataset. Model is trained using transfer learning on MobileNet Model and finally, hyperparameter tuning for attaining best accuracy. The final best test accuracy obtained is 74.42%, 97.50% and 88.73% in the first, second and third methods respectively.","PeriodicalId":224655,"journal":{"name":"7th International Conference on Computing in Engineering & Technology (ICCET 2022)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128113830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing sentiment analysis performance with blend of features and deep learning algorithms 结合特征和深度学习算法增强情感分析性能

7th International Conference on Computing in Engineering & Technology (ICCET 2022) Pub Date : 1900-01-01 DOI: 10.1049/icp.2022.0583

R. Ahuja, SC Sharma

引用次数: 0