Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing最新文献_第3页

A Solution to Evade Social Media Distraction 避免社交媒体分心的解决方案

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549298

Aman Tahiliani, A. Verma, Shubham Agrawal, Kavita Pandey

引用次数: 0

Data Analysis of Agriculture using Data Mining Techniques 基于数据挖掘技术的农业数据分析

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549226

M. A. Mohamed, Muhsin Abdi Mohamud

{"title":"Data Analysis of Agriculture using Data Mining Techniques","authors":"M. A. Mohamed, Muhsin Abdi Mohamud","doi":"10.1145/3549206.3549226","DOIUrl":"https://doi.org/10.1145/3549206.3549226","url":null,"abstract":"Data mining is a method of uncovering hidden patterns in vast, complex databases. It's crucial while dealing with complex agricultural challenges. To detect trends in the influence of a range of factors on crop yield, data visualization is necessary. The planned prediction system is critical in addressing India's food security challenges, as well as advising government agencies on how to deal with over-or under-production situations. In nonlinear complicated settings, deep learning techniques are crucial for properly predicting the agricultural output. The state-of-the-art pattern recognition of time-sequence data has been achieved using the recurrent Neural Network (RNN) technique. Long Short–Term Memory is the most extensively utilized strategy in RNN models (LSTM). In order to forecast crop production in India, the suggested approach is used to extract information from previous agricultural data collections. Python Jupiter is used to carrying out the simulation. The RMSE, MAE, and correlation coefficient are the performance measures employed. Long Short–Term Memory (LSTM), Agriculture Data Mining, and Deep Learning are all used to analyze the data.","PeriodicalId":199675,"journal":{"name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","volume":"217 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124291482","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

Analysis of Deep Learning Models for Anomaly Detection in Time Series IoT Sensor Data 时间序列物联网传感器数据异常检测的深度学习模型分析

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549218

Ujjwal Sachdeva, P. Vamsi

{"title":"Analysis of Deep Learning Models for Anomaly Detection in Time Series IoT Sensor Data","authors":"Ujjwal Sachdeva, P. Vamsi","doi":"10.1145/3549206.3549218","DOIUrl":"https://doi.org/10.1145/3549206.3549218","url":null,"abstract":"The anomaly detection in Internet of Things (IoT) sensor data has become an important research area because of the possibility of noise and unavailability of labels in the sensors readings. The conventional machine learning algorithms cannot detect the anomalies when there is high correlation between the data points of the sensor data. Further, the volume and velocity of the data generated by the sensors in the IoT also a reason that the conventional statistical and machine learning algorithms fails to detect the anomalies. In recent years, the Deep Learning (DL) is gaining significant attention in the anomaly detection research due to the property of unsupervised learning of the high volume data and high detection accuracy of abnormalities. To this end, this paper proposed to study three DL models such as Autoencoders, Long Short Term Memory (LSTM) Autoencoder, and LSTM Recurrent Neural Networks (LSTM-RNN) for detecting anomalies in time series IoT sensor data. Simulations have been conducted using the Intel Berkeley Research Labs (IBRL) Sensor data to evaluate the performance. The results reveal which method performed better in terms of detection accuracy and training time.","PeriodicalId":199675,"journal":{"name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","volume":"1521 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128054663","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: 2

FExR.A-DCNN: Facial Emotion Recognition with Attention mechanism using Deep Convolution Neural Network FExR。A-DCNN:基于深度卷积神经网络的注意机制面部情绪识别

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549243

Pratishtha Verma, Vasu Aggrawal, Jyoti Maggu

引用次数: 1

Leveraging Towards Analytical Approach of Fixed batch-based Queueing assisted Blockchain System 对基于固定批处理的排队辅助区块链系统分析方法的借鉴

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549247

V. Goswami, B. B. Dash, S. Tripathy, Barun Bikram Dash, S. Patra, Rabindra Kumar Barik

{"title":"Leveraging Towards Analytical Approach of Fixed batch-based Queueing assisted Blockchain System","authors":"V. Goswami, B. B. Dash, S. Tripathy, Barun Bikram Dash, S. Patra, Rabindra Kumar Barik","doi":"10.1145/3549206.3549247","DOIUrl":"https://doi.org/10.1145/3549206.3549247","url":null,"abstract":"Bitcoin is a virtual cryptocurrency built on the blockchain, a transaction-ledger database. The blockchain is updated and maintained by a miner passing through a mining process in which a group of miners competes to solve a tough puzzle-like challenge. Users’ transactions are grouped into blocks, and when an algorithmic problem specialized for the block is solved, the block is recorded to the blockchain. According to a recent study, newly arrived transactions are not included in the block being mined and waits in the unconfirmed transaction pool and mined by a miner till the number of transaction matches a minimum batch size i.e. the block size limit. The transaction-confirmation time is investigated in this paper by simulating the mining process using a queueing system with batch service. We assume a Markovian queue that processes transactions in fixed batch K. Additionally, we evaluate the model’s performance metrics, such as the estimated number of transactions seeking to enter the block from the queue, the mean number of transactions waiting in the unconfirmed transaction pool, the waiting time for a transaction, and the confirmation time for every transaction. The validation of the analytical model was performed utilizing the software packages MAPLE 18 to analyze the conclusions acquired by the queueing model.","PeriodicalId":199675,"journal":{"name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116325703","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

Deepfake Creation and Detection using Ensemble Deep Learning Models 使用集成深度学习模型的深度伪造创建和检测

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549263

S. Rao, N. Shelke, Aditya Goel, Harshita Bansal

引用次数: 1

NoiseBay: A Real-World Study on Transparent Data Collection NoiseBay:透明数据收集的现实世界研究

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549325

Julia Buwaya, J. Rolim

{"title":"NoiseBay: A Real-World Study on Transparent Data Collection","authors":"Julia Buwaya, J. Rolim","doi":"10.1145/3549206.3549325","DOIUrl":"https://doi.org/10.1145/3549206.3549325","url":null,"abstract":"In applications where data is collected with the help of personal mobile devices, very often, from the user’s point of view, opaque and partly uncontrollable processes are running in the background of devices. In this paper, we show the advantages of an alternative participant-controlled transparent data collection approach. The paper combines a detailed experimental real world study with a best-practice report. We study the discrepancy between the transparency in the data collection process and the quality of the data collected in the context of mobile crowdsensing (MCS), a paradigm which leverages sensing data from the mobile devices of private individuals. We focus on applications where environmental data is collected and private user data in itself should not have any additional benefit. We treat the concrete example of MCS of tempo-spatial data for the creation of a thematic map of noise levels. We present a lightweight transparent online scheduling approach of opt-in requests for data collection for the users. Within the framework of a real world study, we show that our approach is competitive and results in an improved workload balance among users. We also present data on the responsiveness of users to requests.","PeriodicalId":199675,"journal":{"name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131245849","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

Impact of Image Pre-processing Operations on Wheat Canopy Segmentation 图像预处理操作对小麦冠层分割的影响

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549277

Ankita Gupta, L. Kaur, Gurmeet Kaur

{"title":"Impact of Image Pre-processing Operations on Wheat Canopy Segmentation","authors":"Ankita Gupta, L. Kaur, Gurmeet Kaur","doi":"10.1145/3549206.3549277","DOIUrl":"https://doi.org/10.1145/3549206.3549277","url":null,"abstract":"This research work is an exploratory study of imaging methodologies that could aid in the development of wheat canopy segmentation and water stress detection systems. Through the planned experimentation process, it was found that the chlorophyll fluorescence images needed to be pre-processed before being given as input to the segmentation algorithm for efficient extraction of regions of interest. For efficient canopy extraction, the enhancement associated with contrast and removal of random noise must be done. Multiple pipelines were constructed and it was empirically verified that the TV-L1 denoising with Primal-Dual algorithm is best suited for denoising. The contrast stretching method, also referred to as Min-Max, is the most appropriate operation for pre-processing the images. These pre-processing methods were extremely useful for extracting the area of an image that has maximum photosynthetic activity. The criteria for the selection of pre-processing methods are based on the quality of the segmentation algorithm that is computed using the metric Intersection Over Union. The work demonstrates a most constructive way to separate the wheat canopy from the chlorophyll fluorescence images, for the creation of an automatic drought detection system.","PeriodicalId":199675,"journal":{"name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131453877","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

Stock Prices Prediction Based on Social Influence & Historic Data 基于社会影响和历史数据的股票价格预测

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549257

Prashant Kumar, S. Adhikari, Parul Agarwal, Anita Sahoo

{"title":"Stock Prices Prediction Based on Social Influence & Historic Data","authors":"Prashant Kumar, S. Adhikari, Parul Agarwal, Anita Sahoo","doi":"10.1145/3549206.3549257","DOIUrl":"https://doi.org/10.1145/3549206.3549257","url":null,"abstract":"Stock market price prediction is a challenging issue as a range of elements including political statements, economic circumstances, business market value, historical stock price, and so on, influences it. Hence study exhibit that many prebuild models like ARIMA and deep learning model like LSTM are developed but their efficiency is not up to mark for stock price prediction. In this paper, we build hybrid model which is blended with CNN and LSTM to improve the performance. We used historical data (prior stock price) in the form of numerical information from of the NIFTY50 from 2015 to 2020, as well as news data in textual form from the @NDTVProfit twitter account. In addition, we used a variety of prebuild models and deep learning models to forecast the next 10 days' values. We initiated with numerals/historical dataset and applied ARIMA, SARIMAX, Facebook prophet and LSTM on historical datasets and obtained error score 1062,964,709 and 285 respectively. In addition, models as ARIMA, SARIMAX, Facebook prophet and LSTM have been applied on combined dataset (historical datasets and news datasets) and obtained error score 789,655,380 and 170. The new hybrid model, which is blended with CNN, and LSTM deep learning models is applied on combined dataset and 89 error score was obtained which is better as compared to all previous models.","PeriodicalId":199675,"journal":{"name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134366243","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

Identification of undamaged buildings after the event of disaster using Deep Learning 在灾难发生后使用深度学习识别未受损的建筑物

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549212

Neha Tyagi, M. Saraswat

{"title":"Identification of undamaged buildings after the event of disaster using Deep Learning","authors":"Neha Tyagi, M. Saraswat","doi":"10.1145/3549206.3549212","DOIUrl":"https://doi.org/10.1145/3549206.3549212","url":null,"abstract":"As direct response, or recovery and security operations, it is of paramount importance to establish precisely the location and assess the extent of damage to a building as quickly as possible after a tragic event. The automation of damage analysis may enhance the capability of administration to provide the help. For the same, convolutional neural-networks are being used by recent proposals to perform image classification of building damage depending on the amount and type of damage to be detected. Furthermore, the use of up/down-sampling images during CNN preparation helps in better damage recognition. However, a number of challenges has been observed in convolutional neural-networks-based methods such as multi-resolution images of damaged areas. Furthermore, recent convolutional neural-networks-based models are having very complex architecture which increases the requirement of computational power. Therefore, in this paper, a simple convolutional neural-network model has been presented which effectively identifies the damage and undamaged buildings after the natural disaster. The presented method has been compared with recent convolutional neural-network models. The experimental results shows that the simple convolutional neural-network outperforms the existing models with a 99.2% validation accuracy.","PeriodicalId":199675,"journal":{"name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126044152","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